Category: Artificial intelligence

Artificial intelligence (AI) is the use of technology and machines to work and react in place of humans, conducting functions that were previously thought to require human reasoning and problem solving skills. That is the ideal definition at least. However, to this day, most AI applications have been only successfully programmed to carry out specific tasks or solve pre-defined problems. AI is not something new, but there have been significant advances made in the field these past few years. It is believed that AI could be the answer in helping to combat important health challenges, such as how to meet the healthcare needs of an ageing population. We are seeing an emergence of world-renowned technology companies, including Google, Microsoft and IBM that are investing in AI research and development for the future of healthcare.

Applications of AI in Healthcare and Research

Healthcare Organization

AI could potentially be used for planning and management of resource allocation in health and social care services. Essentially, it would match patients with healthcare providers and design personalized care plans that meet their needs according to their allocated care budget. AI is currently being used in some hospitals around the world in order to improve patient experience and satisfaction. For example, the Alder Hey Children’s Hospital in Liverpool, UK worked with IBM Watson to create a ‘cognitive hospital’, in which an app was created to facilitate interactions with patients. The main aims of the app are to provide patients with a medium to declare their symptoms and chief complaints prior to a visit, provide information on demand, and provide health practitioners with the necessary information to help deliver the most appropriate and effective treatments.

Medical Research

A sector of the healthcare system which is currently slowly transitioning to AI is the medical research community. Healthcare data is complicated. The benefit of AI in this case is that it can be used to analyze large pools of complex information and identify patterns within datasets faster and more accurately than possible by a human. It can also be used to search scientific journal databases for relevant studies pertaining to the research topic of interest in order to combine the data and aid discovery. For example, the Institute of Cancer Research uses AI to predict cancer drug targets by using their canSAR database to combine the patient’s genetic and clinical data with information from scientific research. Researchers have also developed an AI ‘robot scientist’ named Eve in order to make the rigorous process of drug development faster and more affordable. AI systems could also be helpful in medical research by matching patients to appropriate clinical studies.

Clinical Care

Using AI to analyze clinical data, research publications, and professional guidelines; it has the capability to assist in the diagnosis of disease and to formulate personalized treatment plans for patients. The fields in which AI will produce the greatest waves of impact include: medical imaging, echocardiography, screening for neurological conditions, and surgery.

• Medical imaging – stored collections of medical imaging and scans are used to train AI systems to detect conditions such as pneumonia, breast and skin cancers, and eye diseases. The benefits of such practice include not just the reduction of time and costs in analyzing scans, but also the increase of accuracy in diagnoses.
• Echocardiography – detection of irregularity patterns in heartbeats, such as in coronary heart disease, can also be administered by AI systems.
• Screening for neurological conditions – speech patterns are analyzed and processed by AI systems to predict the onset of psychotic episodes and monitor signs of neurological conditions, such as Parkinson’s disease.
• Surgery – robotic tools controlled by AI are increasingly assisting microsurgical procedures to help reduce surgical mishaps and malpractices.

Public Health

Infectious disease outbreaks and sources of epidemics are major global health concerns and national security threats that know no borders. AI could potentially aid in their detection, isolation, and help achieve disease eradication goals.

The Limitations of AI

AI is only as good as the quality of data it is trained with. In other words, inconsistencies in the availability of data could hinder the learning process and restrict its potential. Also, keep in mind that with the analysis of large and complex datasets comes a significant amount of computing power that is required. An expected challenge in training AI systems is the fact that many healthcare systems around the world do not consistently digitize their medical records. Within these healthcare systems, there is also a lack of standardization in IT systems, digital record keeping, and data labelling.

Another concern is more on the human level. Humans have certain attributes, such as compassion, that cannot be learned by a machine. There are certain traits used in clinical practice by physicians- like reading between the lines and reading social cues, that are beyond the current comprehension level and replicative ability of AI. This presents the debate: are there some human traits that cannot be taught, and is there an algorithm for tacit knowledge that is normally instinctively understood by humans? Another claim is the question of autonomy: will AI be held on the same standard as an autonomous human, in which by definition cannot be held by a machine?

Ethical and Social Issues

Many of the ethical issues and concerns presented pertaining to AI mirror those raised about data usage, automation, and the overall dependence on technology on a broader scale in our daily lives.

Data Privacy and Security

While AI has the potential to pave the way for many good things in the near future, it can only do the work according to the intentions of the programmer; meaning, if the programmer had any malicious intent while programming, then AI could also be used to fulfill those malicious purposes. For example, there is fear that AI could be used to monitor behavior and detect patterns by tracking people’s biomedical sensors, such as smartwatches, activity trackers, etc. which could reveal information about a person’s health that can then be sold to health insurance companies.

Reliability and Safety

Reliability and safety are important issues to be discussed because AI is not only used to control the equipment, but is also used to make decisions on the necessary treatment plans for the patient. AI – just like any other machine – is only as good as the creator, but in this case, the trainer. AI too can make errors, and if the error is buried in a pile of endless data, it can easily go undetected and have serious implications. AI applications in healthcare also use data that is sensitive and private. In other words, if an AI system were to be hacked in order to obtain sensitive patient data, this could happen without being detected.

Transparency and Accountability

It is difficult to hold a machine accountable, let alone AI – a type of machine learning technology. There is a gray area when dealing with the transparency and accountability of machine learning technologies because they have the ability to constantly change their own parameters and rules as they learn. This creates the problem of validating the results and catching any errors or biases in the data obtained from AI systems.

Effects on Patients and Healthcare Professionals

AI systems that are used to help support people with chronic health conditions or disabilities have been found to positively impact patients by giving them a sense of dignity and independence. This is because AI health apps, for example, give the patient the ability to evaluate their own symptoms and learn ways to take of themselves from home without having to be admitted to a care institution for long periods of time. However, AI systems can also have negative impacts on patients. For example, there are concerns that if AI technologies are used to replace physicians and healthcare professionals, then there would be a loss of human contact – an essential element of physician-patient relationships. The patient’s freedom to make informed decisions for themselves about their health could be restricted if physicians are unable to explain to the patient how the AI system arrived to the diagnosis or the treatment plan.

Introducing AI systems into healthcare may cause healthcare practitioners to feel threatened by this new form of technology, especially if their expertise, autonomy or authority is challenged. The automation of tedious tasks, such as paperwork and computerizing data, could free up time for health professionals to spend engaging more directly with their patients. But this raises the issue that AI systems could be used as an excuse to employ less qualified staff since less expertise is needed. This is problematic because if the technology were to fail, the staff would not be able to recognize any errors. There is also the problem of complacency, in which healthcare practitioners would rely too much on the AI results and not challenge the results or check for errors.

Conclusion

AI technology is being trialed and used around the world in healthcare and research for many difference purposes ranging from the detection of diseases and management of chronic conditions to the delivery of healthcare services and drug discovery. AI has the potential to create new solutions for important health challenges, but unfortunately does not possess the ability to express key human characteristics and is limited to the quality of health data available. The use of AI has also raised some ethical and social concerns; such as, the ambiguity in transparency and accountability of machine learning technologies and the extent of data privacy, reliability, security and safety. We need to ensure that AI is trained and used in a way that is transparent and compatible with the interest of the public but also not overly regulated to the point of restricting further innovation in the sector. Ultimately, it is up to politicians, policy makers and most importantly, the general public, to decide the future course of artificial intelligence in our healthcare systems.

Introduction

Diabetes is a chronic health condition, there are two forms of diabetes Type 1 which affects the body’s ability to produce insulin which is easily rectified by insulin shots, type 2 diabetes is when the body does not react to insulin which can be fatal thus, the challenge that i will be tackling concerning diabetic care is poor medical adherence which is noted to be significantly more common in type 2 diabetes. Poor medical adherence is important in type 2 diabetes for many reasons, for example the long term effects of not taking diabetic medication is risking blindness and also kidney damage. In addition to this further down the road it can lead to heart failure. In this report I will be showing/explain how the use of Ai could assist with the challenge of diabetic medical adherence an example could be to set a reminder or what dosage an individual should take.

Background

AI has been said to be a subdivision of computer science whose main goal is to create a program/method that assists via analysing data and simplifying the use of it in a wide range of areas of internet technologies. The way that ai applies to diabetic care is simple yet enticing for easy and efficient data handling and the various tools and devices used for its management. To ensure safer use of technology through AI, it is advised to possess designs which ensure safety and security, backups and procedures in place in order to keep everything safeguarded with all uncertainties noted and looked up for the various systems.

Due to recent technological advancements, various forms of technology allow for the monitoring and and tracking of patients symptoms and disease status. Examples include smartphones and wearables. In order to effectively treat diabetes health care professionals and physicians must give patients the option of choosing AI assisted care.

There are 3 key areas of diabetic care. These include patients with diabetes, health care professionals, and health care systems. Patients with diabetes now have newer dimensions of self care. They also have decision making that is fast and reliable. Diabetes patients also have variable follow ups for health care providers as well as an optimised utility of resources within healthcare systems.

There are four areas in which diabetic care could be improved through the use of better AI. Firstly, automated retinal screening the ai system currently used in this area is used to detect diabetic retinopathy, maculopathy and any other differences compared to normal findings. The second area is clinical decision support which is a system that provides health professionals a method to assist with clinical decision tasks. AI application to this is used to detect and monitor and monitor diabetes and any other diseases. The third is predictive population risk stratification, this is used to predict the future of an individual in this case the future of a diabetic patient which could help plan different possible scenarios making it easier to deal with problems that may occur. The most common clinical AI application is that it identifies those with diabetes who are at a higher risk concerning different complications that may or may not be faced as well as risk of hospitalization. Last but not least the fourth is patient self management which is a system in place that assists patience via increasing their skills and confidence in managing their own health issues. One of many AI applications that are used is improved glucose sensors which are improved through the use of AI as well as other methods such as activity and dietary tracking devices.

Methodology and Data

There are numerous strategies of dealing with the treatment of diabetic patients. One example would be Case-based reasoning. This is a form of AI used to fix issues where the solution has already been accuired from past experiences. CBR has been used for the diabetes support system. The purpose of this system is to find issues with the regulation of blood glucose and to offer solutions to these issues. This system also allows for the effective and ineffective treatment of a patient. CBR can provide insulin therapy for meal situations in diabetes.

The targeted methods of solving medical adherence that we will be focusing on is CBR also known as case based recording and machine learning. CBR as stated in the first paragraph is a form of ai that we utilise in order to correct issues that we have already faced in the past such as any type of glitches they had or updating information as we discover new and better ways of dealing with medical adherence in diabetes. This links to the challenge that we are facing due to the fact that it automatically detects the glucose levels in blood which could send out an alert to the individual who has the medical adherence issue reminding them. Another method that could be put in place is an ai based alert which alerts the diabetic individual that they should take their medicine. In addition to this the AI could help answer basic questions the individual has in mind and could possibly provide links to various websites for which they can do their own research to help them manage their condition.

Knowledge representation is another area of AI where the AI is dedicated to solving important tasks such as diagnosing medical conditions by utilising the computer system it possesses. The way knowledge representation links to medical adherence is that every computer system is assisting people with diabetes so there are less people every year that don’t take their medication. The datasets that can be used are various websites through the internet. This also could be useful to us as it conveys bits of info to the individual so that they learn more about their condition and how they can manage it themselves. The datasets could be stored on different programmes such as a spreadsheet database in figures. In addition to this there are many types of datasets the ai could utilise for example numerical datasets.

The A.I system discussed in this report provides accurate information and reasons as to why they should be taking their medication as well as any issues that may arise as a result of not taking them. An example could be the AI could scan websites and display different numerical values/data to the patient and slowly but surely help them understand their medical condition and teach them valuable skills in their medical issue to help them with medical adherence. In addition to this the AI could have basic interaction in the form of questions and answers to help with any queries the patient may have.

Analysis and Discussions

The AI we made worked as expected for example it sent out an alert at a certain time reminding the individual to take their medication and linking a random website which the individual could potentially use for themselves and learn all about their condition so they have a better understanding on how to manage their condition and reasons to take their medication. Also it linked a website to which they can use in order to adjust their diet so they can make the best of what they have. In addition to this the ai is able to answer a few questions such as “Could you send me some information on diabetes?” and “what can you tell me about diabete?”. Ways that we can improve on it is we can make AI more interactive and provide more than just a link. In addition to this we could’ve made it so the AI checked up on the individual throughout the day and placed all the data they gather from the individual into a spreadsheet/database so that it could be viewed whenever they like.

Conclusions

This coursework has taught me the importance of artificial intelligence and how it impacts diabetic care. The A.I system discussed in this report allows sufferers of diabetes to receive reliable and accurate information about their medication as well as the adverse effects of not taking them. However, this system also has its limitations. For example this system doesn’t

After going through the comments from my supervisor and my center coordinator I am satisfied with the progress I have made so far, and I wish to continue with my final aspects of research after my mock exams in late June. However, throughout research I have experienced some difficulties in balancing parts of my EPQ progress primarily because I had revision to complete and also being a 2021 MAP student, I had to dedicate a significant proportion of my time after my exams to writing my 1500 word academic assignment draft: which I had to submit before the 5th of July, but I self-assured that this will not impact the quality of my project as now I can directly focus all of my attention on my project.

I have already incorporated the recommendations from my supervisor by narrowing the scope of my question from ‘What Is the Medical Potential of Artificial Intelligence and how can it be used to deliver healthcare while providing its effects on the healthcare system?’ to a more condensed down and narrower focused title ‘What Is the Medical Potential of Artificial Intelligence in the Future of Heart Treatment’ – and subsequently I had to discard some of my research because it was too broad and not relevant to my proposed title. Also, now I need to be wary of the fact that as I continue to write I have to fulfill the purpose of altering my question by not branching off and focusing specifically on the applications of AI in future Heart Treatments.

I have found internet sources that provide key insights into establishing a basic understanding of the process of involving artificial intelligence in heart treatments and this has led to me having an idea of what experts in this field conclude. Furthermore, my next steps are to continue finding more articles (as altering my title has reduced the number of feasible resources I could use) and carry out further research by using the University of Manchester library and keep track of the amount of time on spend on each section I have created a Gantt chart to gain an estimate into how long each section will take me.

I hope to have my EPQ completed by December, and this is judged by the fact that I will have fully researched the topic and after collating all my articles and sources form a first draft, get that assessed and act on those learning targets only to then produce a final version in which I will make sure I meet all the requirements and learning target given to me from my first draft.

ABSTRACT

The plant disease diagnosis is restricted by person’s visual capabilities as it is microscopic in nature. Due to optical nature of plant monitoring task, computer visualization methods are adopted in plant disease recognition. The aim is to detect the symptoms of the disease occurred in leaves in an accurate way. Once the captured image is pre-processed, the various properties of the plant leaf such as intensity, colour and size are extracted and sent to classifier with Artificial Neural Network for classification of disease which the plant gets.

INTRODUCTION

The Production of good quality food produce and improvement in crop yield are challenging for the researchers as well as agriculturist to meet the growing demands globally. Thus, it is crucial to maximize agriculture resources and its utilizations in a sustainable manner. Therefore, for the sustainable agriculture system, use of emerging technology becomes important for significant and efficient contributions. With the implementation of these techniques, possibility to reducing errors and costs for achieving ecologically and economically sustainable agriculture is the thought of the present era [1]. Earlier used techniques were inefficient and time consuming for analyzing the problems and implementation of remedial measures. Diseased plants exhibit a variety of symptoms like, stunting, yellowing, wilting, twisting, reddening, browning, blighting, and other abnormalities [2]. Thus, accurate diagnosis is essential to diagnosis and control the plant disease effectively. Until a disease is adequately diagnosed, a grower may waste time and energy as well as money to solve a problem with an unknown cause. Once a disease is diagnosed, appropriate management practices can be selected [3]. To overcome this problem a fast and accurate process is required, that can automatically detect the disease on the leaf. Technique such as visual detection requires significant time for visual inspection for a large cultivated area. Thus, image processing technique is proven to be an effective method as compared to visual analysis.

LITERATURE SURVEY

In the modern era, enhancement in the use of internet attracts the science and engineering techniques to get easy and quick solution, as, it is most efficient and effective way of communication. Therefore, researcher, Bhange et al. (2015) developed a web based tool for identifying pomegranate leaf disease. In the first step, feature extraction (based on color and morphological features) was done. Thereafter, to segment the diseased part from healthy region k-mean algorithm and SVM was used. Accuracy achievement in the proposed method was 82% [4]. Renugambal et al. (2015) proposed an artificial intelligence technique for automatic detection and classification of Sugarcane leaf disease using image processing technique. Infected leaves were captured by using digital camera. After then, the preprocessing and segmentation was done using image histogram equalization, filtering, color transformation to detect infected parts of the leaves. Finally, SVM classifier was used for classification purpose [5].

An image segmentation algorithm was proposed by Singh and Misra (2017) for automatic detection and classification of plant leaf disease (rose, banana, beans). Segmentation was done using genetic algorithm to distinguish diseased part and the healthy parts of the leaves. This algorithm was tested on ten species of plant i.e. Jackfruit, Banana, Mango, Sapota, Potato, Beans, Tomato, Lemon, etc. to check the accuracy of proposed algorithm. The author accomplished that the algorithm provides optimum results with less computational efforts in recognition and classification of leaf disease [6].

PROPOSED METHOD

IMAGE ACQUISITION

The impression of images is the primary and essential step to observe the state of the Groundnut leaf. The image imprisonment has been done through various tools and devices, such as, cameras, mobile phones and satellites. The proper estimation of RGB color pixels in an image is essential step towards successful completion of image capturing. The technical parameters of these simple, handheld devices such as light sensitivity of the photo sensors, spatial resolution and digital focusing have improved dramatically year after year. Today, nearly every person, farmer or plant pathologists carry these modern and sophisticated devices such as digital cameras together with a mobile phone or tablet computer.

IMAGE PRE-PROCESSING

The pre-processing follows the image acquisition. The acquisition of images and creating images database, pre-processing has been done. The pre-processing of created database is a preliminary step to eliminate the undesired distortion of the image and provides enhancement in features. While considering leaf of a plant, various colours have been observed. To distinguish the colour of the diseased lesion from the original colour of leaf, the RGB colour pixels should be converted into some other pre-processing for the better perception. The reason for unacceptability of RGB is the system dependency of such pre-processing. Therefore, the improvement in the precision of colour for detection of disease, the independency of pre-processing is essentially required.

IMAGE SEGMENTATION

Segmentation of an image is the process of partitioning the object (diseased spot) from its background (leaf). Different segmentation techniques are available like clustering methods, thresholding, edge detection, ANN based methods, partial

differential equation based segmentation, etc. In the present research kmean clustering technique for segmentation has been given the priority among all of the above stated techniques. The inherent advantages of k-mean clustering method are that, it works well with large data sets. The accuracy of system depends on the data sets. Therefore, this (k-mean clustering) proves to be fast, robust, easier to understand and simplest to implement. Furthermore, it may work more efficiently; if clusters are spherical (diseased spots are spherical in shape) and more in number. Increased value of the k (cluster) reduces the amount of error in the result. Current work has a value of five for k.

The formations of clusters have been done based on the selection of five random points selected from the data sets. These five random points treated as centroids of each cluster. These random points attract the same intensity points (based on Euclidian distance method). This movement of the centroid happened till the same intensity cluster formed and can’t move further. The ultimate end results come in the form of diseased and healthy parts of the leaves. After segmentation, one of the diseased clusters (obtained from one or more than one cluster) has been extracted and considered for calculation of the disease area of the leaf.

FEATURE EXTRACTION

At this stage of the project we calculate the Gray Level Co-occurrence Matrix of an image in order to extract the set of features required for further calculations. In a statistical texture analysis, texture features were computed on the basis of statistical distribution of pixel intensity at a given position relative to others in a matrix of pixel representing image. Depending on the number of pixels or dots in each combination, we have the first-order statistics, second-order statistics or higher-order statistics. Feature extraction based on grey-level co-occurrence matrix (GLCM) is the second-order statistics that can be used to analyze the image as a texture. GLCM (also called gray tone spatial dependency matrix) is a tabulation of the frequencies or how often a combination of pixel brightness values in an image occurs. Transforming the input data into the set of features is called feature extraction. In this project, the features are Mean, SD (Standard Deviation), Entropy, RMS, Variance, Smoothness, Kurtosis, Skewness, IDM, Contrast, Correlation, Energy, Homogeneity. If the features extracted are carefully chosen it is expected that the features set will extract the relevant information from the input data in order to perform the desired task using this reduced representation instead of the full size input.

Artificial Neural Network (ANN)

The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well. The below fig-2 shows the diagram of ANN(Artificial Neural Networking).

CONCLUSION

There are many methods in automated or computer vision plant disease detection and classification process, but still, this research field is lacking. In addition, there are still no commercial solutions on the market, except those dealing with plant species recognition based on the leaves images. By observing the existing and proposing method, some differences are shown in the table-1.In this paper, a new approach of using deep learning method was explored in order to automatically classify and detect plant diseases from leaf images. The developed model was able to detect leaf presence and distinguish between healthy leaves and 4 different diseases, which can be visually diagnosed. The complete procedure was described, respectively, from collecting the images used for training and validation to image preprocessing and augmentation and finally the procedure of training the deep ANN and fine-tuning. Different tests were performed in order to check the performance of newly created model.

References

1. K. A. Garrett, S. P. Dendy, E. E. Frank, M. N. Rouse, and S. E. Travers, “Climate change effects on plant disease: genomes to ecosystems,” Annual Review of Phytopathology, vol. 44, pp. 489– 509, 2006.
2. S. M. Coakley, H. Scherm, and S. Chakraborty, “Climate change and plant disease management,” Annual Review of Phytopathology, vol. 37, no. 1, pp. 399–426, 1999.
3. S. Chakraborty, A. V. Tiedemann, and P. S. Teng, “Climate change: potential impact on plant diseases,” Environmental Pollution, vol. 108, no. 3, pp. 317–326, 2000.
4. A. J. Tatem, D. J. Rogers, and S. I. Hay, “Global transport networks and infectious disease spread,” Advances in Parasitology, vol. 62, pp. 293–343, 2006.
5. J. R. Rohr, T. R. Raffel, J. M. Romansic, H. McCallum, and P. J. Hudson, “Evaluating the links between climate, disease spread, and amphibian declines,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 45, pp. 17436–17441, 2008.
6. T. Van der Zwet, “Present worldwide distribution of fire blight,” in Proceedings of the 9th International Workshop on Fire Blight, vol. 590, Napier, New Zealand, October 2001.
7. . A. Miller, F. D. Beed, and C. L. Harmon, “Plant disease diagnostic capabilities and networks,” Annual Review of Phytopathology, vol. 47, pp. 15–38, 2009.
8. M. B. Riley, M. R. Williamson, and O. Maloy, “Plant disease diagnosis. The Plant Health Instructor,” 2002.
9. J. G. Arnal Barbedo, “Digital image processing techniques for detecting, quantifying and classifying plant diseases,” SpringerPlus, vol. 2, article 660, pp. 1–12, 2013.
10. H. Cartwright, Ed., Artificial Neural Networks, Humana Press, 2015.

Abstract

India is an agricultural country and most of the people, wherein about 70% depends on agriculture. So, disease detection in plants is very important. Tomato is one of the strongly grown and widely used crops. There are many types of tomato diseases and pests, in which the pathology of which is complex. Crop diseases are a major threat to crop production, but their identification remains difficult in many parts of India due to the lack of the necessary infrastructure. It is very prone for attacks by aphids, whiteflies, Thrips. It is difficult and error-prone to simply rely on manual identification in a large open area. Recent advances in computer vision made possible by deep learning has made the way for automatic disease detection. To monitor the health of the tomato crops in acres of land where we cannot monitor the output of each sensor individually, AI is used to increase the yield and quality of crops using a Convolution Neural Networks (CNN), k-means clustering, and acoustic emission.

INTRODUCTION

India has vast area, but the current status of agriculture management is not sufficient to provide everything to the population, which can be problematic. The solution to this issue is the practice of monitoring and protecting the crops in the open land farming. Automation system is the technical approach in which the farmers in the rural areas are benefited by automatic monitoring and controlling of pests and protecting the crops. It replaces the direct supervision of the human. Here, AI is used to monitor the health of the tomato crops in large acres of land where we cannot monitor the output of each sensor individually with the help of Convolution Neural Networks and K means clustering and thus increasing the yield and quality of the crops.

The development and growth of crop depends on the temperature and humidity. The controlling and monitoring of open land parameters play vital role in overall development of plant. The objective of our project is to design a simple, efficient “Arduino‟ based system for automation of open land. The project features monitoring, recording and controlling the values of temperature, humidity and soil moisture inside the open land. The Arduino used is a highly compact, durable and easily available. The values of temperature and soil moisture are continuously communicated through various sensors to the Arduino. Also proper design, selection, construction and the management of the open land using sensors would augur well to the growth of a crop.

LITERATURE SURVEY

Susperrangi, Carlos Rubio and Libor Lenza presented the development and comparison of two different approaches for vision based automated pest detection and identification using learning strategies [1]. Santhosh Adhikari and Er.Saban Kumar presented the classification and detection of the plant’s diseases automatically especially for the tomato plants [2]. Christina Mueller Blenkle and Sascha Kirchner designed a system in which the position and sound of hidden insects are also detected, but the settlement sound of grain can be mistaken for insect sound [3]. K.Narsimha Reddy, B.Polaiah and N.Madhu presented the overview of different classification techniques [4]. Preetha Rajan, Radhakrishnan B presented the study of various image processing techniques and applications for pest identification and plant disease detection [5].

PROPOSED WORK

Owing to the inaccurate prediction results which is obtained from use of thermal and image sensor we propose to introduce acoustic sensors. This system is basically proposed only for large areas in acres to increase the crop yield without getting affected by pests. The system is trained to read the sensor output and the prediction is done through machine learning which improves the accuracy. The use of AI reduces the work of labors.

a) Arduino Uno

Arduino is a single-board microcontroller, intended to make the application of interactive items or environment further useful. It involves the whole lot to support the microcontroller; without problems connect it to a laptop with a USB cable or power it with an ac to dc adapter or battery to get began out. The Uno differs from all previous boards in that it does no longer use the FTDI USB to- serial using drive. It has 14 digital input/output pins (of which 6 can be used as PWM outputs), 6 analog inputs, a USB connection, a vigor jack, a reset button and more. It includes everything needed to aid the microcontroller; conveniently join it to a laptop with a USB cable or vigor it with a AC-to-DC adapter or battery to get started.

b) Acoustic Sensor

Literally acoustic or sound sensor is used to detect the sound. It is a small board that combines a microphone and some processing circuitry. The sound detector not only provides audio output, but also a binary indication of the presence of sound, and an analog representation of its amplitude. Early detection of pests in images is very crucial for effective management of pest control .but by using this acoustic sensor we can efficiently detect the pest at early stage.

Fig (c): Acoustic Sensor

c) Soil moisture sensor

Soil Moisture sensor is a sensor which detects the moisture substance of the soil. At the point when the soil is dry, the current won’t pass through it thus it will go about as open circuit. Subsequently the yield is said to be most extreme. At the point when the soil is wet, the current will go from one terminal to the next and the circuit is said to be short and the yield will be zero. The sensor is metal covered to make the proficiency high. The scope of detecting is likewise high.

d) Temperature sensor

The LM35 is one kind of commonly used temperature sensor that can be used to measure temperature with an electrical o/p comparative to the temperature (in °C). It can measure temperature more correctly compared with a thermistor. This sensor generates a high output voltage than thermocouples and may not need that the output voltage is amplified. The LM35 has an output voltage that is proportional to the Celsius temperature. The scale factor is .01V/°C. The LM35 does not need any exterior calibration and maintains an exactness of +/-0.4°C at room temperature and +/-0.8°C over a range of 0°C to +100°C.One more significance of this sensor is that it draws just 60 micro amps from its supply and acquires a low self-heating capacity. The LM35 temperature sensor available in many different packages like T0-46 metal can transistor-like package, TO-92 plastic transistor-like package, 8-lead surface mount SO-8 small outline package.

CONCLUSION

This project is used to automate the open land with early detection of pest using acoustic sensor. This method yields more crops than the existing method with the improvement in their quality. The soil moisture, temperature, light intensity are measured and automatically controlled with IOT and AI technology. It has been interfaced with arduino, thus the open land has been automated.

REFERENCE

1. Learning strategies for pest detection and identification on tomato plants for autonomous scouting robots using internal databases. Altor Gutierrez, Ander Ansuategi, Loreto Susperrangi, Hindawi, Journal of Sensors, Volume 2019.
2. Tomato plant disease detection system using image processing. Santhosh Adhikari, Er.Saban Kumar, KEC Conference, Volume 1, September 27, 2018.
3. Plant leaf diseases detection using image processing technique. K.Narsimha Reddy, B.Polaiah, N.Madhu IOSR Journal of Electronics and Communication, Volume 12, Issue 3, Ver2, May-June 2017.
4. Plant leaf diseases detection using image processing technique. Preetha Rajan, Radhakrishnan B, International Journal of Computer Science and Network, Volume 5, Issue 1, Feb 2016.
5. A new approach to acoustic insect detection in grain storage. Christina Mueller Blenkle,Sascha Kirchner,Isabell Szallies,Cornel Adler. Germany 2018.
6. Improved efficiency of Insect pest control system by SSPA. Phanupong saeung Samran santalunai Thanaset 2018 IEEE, Thailand. Classification And Prediction Of Brinjal Leaf Diseases Through Image Segmentation. International journals of computer trends and technology, April 2017.
7. An Advanced Method for Chilli plant disease using image processing. Dipak,swapnil R. Kurkute,Pallavi S.Sonar, Antono, ICEST 2017.
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9. Chilli Leaf Curl Virus disease:a serious threat for chilli cultivation, Journal of Plant Diseases and Protection 20th January 2018.

Robotic surgery is used now a days by hospitals to do surgeries more precisely, flexibly and with more control than the surgeries which are done by human hand. The robots used for this purpose are manually controlled and coordinated by surgeons. Artificial intelligence is a computer system which is able to perform tasks which requires human intelligence, like visual recognition, decision making, recognition of sounds and more over the things which humans do. So, this research report mainly focuses on how artificial intelligence can be used in robotic surgery and how they are going to help the society, what are their limitations, and how it is going to affect the people who are related to this sector. Recently, society is surprisingly positive towards robotic surgery. People have changed their mind of view in the past few years dramatically towards allowing artificial intelligence algorithms and robots to operate on themselves or on their beloved ones. Present generation tend to trust technology so far beyond expectation.

Literature Review

The most common robotic surgery which is used now a day is the Da Vinci surgical system from Intuitive Surgical which was accepted by the FDA (Food and Drug Administration) in 2000, which perform the exact motions of a surgeon. It is a system where the decision-making stays with the surgeon. A human surgeon is manually controlling the motions through a master manipulator and everything is prosecuted at a smaller scale inside the patient, where there is minimal invasive. An enthusiastic one is image-guided surgery. In that, mostly in orthopedics or in neurosurgery, the robot is able to perform event in a finest motion based on a pre-operative plan advised by the surgeon. In the coming era, there is a high chance of artificial intelligence in all sectors and especially in robotic surgery. With the arrival of artificial intelligence, we are able to do surgeries more accurately and with less mistakes than manual surgeries which seen more now a days. It is faster than normal surgery and less blood flow is visible. But the surgical robots which is used today are operated by surgeons. The robot doesn’t control the scalpel the surgeon does as mentioned above.

Although, there are many types of artificial intelligence learning which is changing medicine, silently but immensely, in ways that will affect surgeons and maybe other care takers throughout the coming years. Machine learning mainly uses algorithms that have the efficiency to learn and respond to data, also can improve their performance according to the feedback given by them. Deep learning builds algorithms which can handle an excessive degree of complexity by mimicking the neural networks found in the human brain. These artificial neural networks indicate how each node of the network have the ability to handle one small part of a complex task. This fastens the ability of algorithms to handle an increased amount of complex data.

There are many benefits when artificial intelligence is used in robotic surgery like minimal invasive, in which the incision made will be so precise and much smaller than the one which is made by human surgeons. The probability of unintended ruptures which leads to hemorrhage and septicity are significantly lowered. So, when small incisions are made the recovery time will be less and they heal faster than the traditional one. In this situation patients can continue their normal life within a short time period, and the ones who does work can continue their work. Less bleeding will be observed as it is also a result of small incision, it’s not an end there are many like less pain is felt during the surgery and following the surgery. The use of painkillers during the time of recovery can be minimized, which lowers the risk of addiction.

In the case of low blood loss, we can avoid the need of blood transfusion. In ordinary surgery blood loss will be much higher, which causes complications during surgery. There are risks of infection, when surgical procedure is done, which can cause the delaying of recovery. There is also higher chance of infection in some specific part of the body particularly when large incisions are made, so in robotic surgery less invasive, the risk of developing infection is very low. With the help of robotics, we are able to go beyond the physical human capabilities. There is no hand shaking or trembling, no restriction in terms of fatigue, the surgeons don’t have to do surgeries for ten hours all can be done by robots with the help of artificial intelligence. So, there are various aspects that heighten the robotic surgery.

The most impressive example for artificial intelligence in past two to three years was the retinal vessel surgery for human eye, removing the membrane, re-joining arteries, or just putting needles and giving some local medication in case of vein occlusion, only one out of hundred surgeons has enough elegant fine motion control to do that.

Limitations

At the same time there are limitations which slow down the progress of arrival of artificial intelligence in robotic surgery. Artificial intelligence has the ability to make its own decision, sometimes there are cases where ethical issues may interfere with decision making, that time the decision made will not be ideal, in this situation emotion plays a major role, which is absent in artificial intelligence. In coming decades there is a possibility that the care providers will be replaced by robots, which results in the domination of machines over man ending up in utter turmoil. Workers are forced to modify their skills to thrive in their occupation for the future. Demands which are related to social and emotional skills like communication and empathy can grow almost quickly as demand for many advanced technological skills. Automation can also stimulate the growth for higher cognitive skills, specifically critical thinking, creativity, and complex information processing. Necessity for physical and manual techniques will decline, but these will stand as the single largest category of workforce skills in 2030 in many countries. The pace of skill shifts has been accelerating, and it may lead to excess demand for some skills and excess supply for others. As said in the upcoming days robots will overrule in very sectors and the byproduct is unemployment which leads to poverty of the families who are depended on them. Talking about all these they also can revolt against us; artificial intelligence has the ability to improve their way of behavior and thinking ability. Autonomous weapons are artificial intelligence systems which are assigned to kill. If these technologies are in the hands of the wrong person, there is high chance of misusage of this automation. As a result, these ideas should be handled so diligently.

Conclusion

Conventionally, we believe the surgeon to make decisions, and if they ever make any wrong decision, we have certain mechanisms that we can do at a public level to make sure that we as a society accept or deny that they made the right decision. And if they are blamedable, then they get punished, or we cancel their license. In spite of, when a machine takes a decision which is unacceptable, then who are we going to blame? If an algorithm says it is statistically good decision to separate a limb, and then it turns out it was wrong, and making someone an amputee for the rest of their life, and which was a programming error that someone made years before in manufacturing, so who is to be blamed here? A human surgeon can make mistakes, that’s why we are having statements and responsibility in which you have to give your consent to everything. But for people normally have more expectation on technology and on robotics to be perfect. And even if you have given the information that statistically a robot is better than 99.5% of all human surgeons, if it fails to do things correctly, then society will still blame the robot manufacturer, even though statistically it was a stronger decision to use the technology. This is a big issue in which there is no particular answer for it. The public itself is not ready for it.

References

1. https://ai-med.io/artificial-intelligence-robotic-surgery/
2. https://futureoflife.org/ai/benefits-risks-of-artificial-intelligence/
3. https://www.uchealth.com/services/robotic-surgery/patient-information/benefits/
4. https://www.online-sciences.com/robotics/robotic-surgery-cons-pros-uses-and-how-does-robotic-surgery-work/
5. https://en.wikipedia.org/wiki/Da_Vinci_Surgical_System
6. https://en.wikipedia.org/wiki/Artificial_intelligence
7. https://en.wikipedia.org/wiki/Robot-assisted_surgery
8. https://www.researchgate.net/post/What_are_the_advantages_and_disadvantages_of_artificial_intelligence

The increasing use of artificial intelligence (AI) in surgery has supporters and opponents. Advantages may include precision, speed, and research advances whilst disadvantages may include accountability, ethical and safety considerations. Orthopedic surgery is a field of Medicine rapidly advancing in its use of artificial intelligence to achieve exciting progress. The question is how far can and should it go?

AI has gained tremendous popularity in recent years, and some AI techniques such as search engines, voice recognition software, and autonomous driving vehicles are now part of our daily lives. AI research is also being conducted in many medical fields, and shows great promise in promoting practice efficacy, personalizing patient management, and improving research capacity.

AI techniques have made impressive advances in the medical imaging pathway, from acquisition and reconstruction to analysis and interpretation. By incorporating information from the patient’s medical records (including symptoms, laboratory results, and physical examination findings), AI identifies the most appropriate patient-specific imaging examination and determines the most appropriate protocol. AI can also potentially increase the speed of magnetic resonance imaging (MRI) data acquisition and decrease the computed tomography (CT) radiation dose.

The use of artificial intelligence has acted as a remote diagnostic measure for infection cases treatable outside of hospitals. Clark and Therese Canares, MD, assistant professor in pediatric emergency medicine at the Johns Hopkins University School of Medicine, believe artificial intelligence could help better diagnose and manage ear infections, even remotely from a patient’s home. They are co-inventors of OtoPhoto, the world’s first smart otoscope, a device that takes images of the inner ear and uses machine learning to determine whether or not an infection exists. OtoPhoto’s visuals are analyzed by a proprietary algorithm that makes the diagnosis. The images can be shared with a specialist in real time during a telehealth appointment, which is particularly useful during the ongoing coronavirus pandemic. This allows patients to get both the diagnosis and treatment at home, without potential exposure to Covid-19.

Orthopedic surgeons provide procedures such as traction, amputation, hand reconstruction, spinal fusion, and joint replacements. They also treat strains and sprains, broken bones, and dislocations. Their work involves adding foreign material to the body in the form of screws, wires, pins, tongs, and prosthetics to hold damaged bones in their proper alignment or to replace damaged bone or connective tissue. Such surgeons utilize advances in the development of artificial limbs and joints, and in the materials available to repair damage to bones and connective tissue aiming to duplicate the natural functions of bones, joints, and ligaments, and to accurately restore damaged parts to their original ranges of motion.

Orthopedic surgery began to use AI robotic technology in 1992, with the introduction of the ROBODOC system for the planning and performance of total hip replacement. Most orthopedic robots, such as the Mako system, are used for joint replacements such as knee and hip surgery. The robots’ technique in achieving limb alignment can reduce operation time and blood loss. The Renaissance robot and the ROSA robot have the advantages of improved pedicle screw accuracy and reduced radiation exposure for patients and clinical staff, compared with conventional spine surgery.

The Tianji robot is a multi-indication orthopedic surgical robot that can be used for all types of spinal and fracture surgeries. The Tianji robot combines a robotic arm with a real-time navigation system and has a high degree of surgical precision. Compared with freehand surgery, the Tianji robot can improve the accuracy of instrument placement and improve clinical results. In July 2019, Professor Wei Tian (President of Beijing Jishuitan Hospital) performed the world’s first multi-center 5th generation (5G) remote orthopedic surgery using 5G technology. This success suggested the combination of 5G technology and robotic technology can improve the safety and quality of remote surgery.

In terms of diagnosis prior to surgery, artificial intelligence can support radiologists, improving diagnostic accuracy, and preventing errors observer fatigue. Artificial intelligence algorithms have been applied to the diagnosis of fractures, osteoarthritis, bone age, and bone strength. Arguably, the most accurate diagnosis is likely to be achieved when artificial intelligence is used in combination with, not instead of, a radiologist. Despite the impressive advances in AI orthopedic surgery, realistically there will always be the need for a human element in the practice of surgery and medicine in general.

Conversely, there are disadvantages related to an increase in AI usage. Artificial intelligence in orthopedic surgery is expensive to use, time consuming in its preparation for surgery and lacks the support of long term follow up studies. There are also ethical considerations regarding the use of artificial intelligence in orthopedic surgery which include increased risks to patient confidentiality and consent unless safeguards are in place. In cases of misdiagnosis or maloperation, it is unclear whether the doctor or the robot are responsible. Currently surgical robots can only be used to perform relatively simple procedures and possess little autonomy and decision-making authority in treatment. There is the potential for future advancements in Artificial intelligence-assisted procedures to be self-learning with full surgical independence. However, there may be circumstances where human clinicians are unable to control or override procedures made by an artificial intelligence device, which raises serious ethical and safety questions.

The Nuffield Council on Bioethics published an article ‘AI in Healthcare and Research’ in May 2018, reminding us of the ethical issues raised by the use of AI in healthcare in general (that may restrict the AI advances in orthopedic surgery), such as: the potential for AI to make erroneous decisions; who is responsible when AI is used to support decision-making; difficulties in validating the outputs of AI systems; the risk of inherent bias in the data used to train AI systems; ensuring the security and privacy of potentially sensitive data; securing public trust in the development and use of AI technology; effects on people’s sense of dignity and social isolation in care situations; effects on the roles and skill-requirements of healthcare professionals; and the potential for AI to be used for malicious purposes.

It would seem the use of AI and its further advancement in orthopedic surgery must be in tandem with patient centered confidentiality, ethical and safety-first healthcare, with overarching accountability and transparency, keeping human physicians ultimately responsible and in charge. It is these considerations which will ultimately dictate how far and how fast orthopedic surgery can go on a practical and ethical basis. However, for future orthopedic surgeons with interest and imagination in the potential of engineering and technology in patient diagnosis and treatment, the future progress of artificial intelligence in surgery is very exciting.

Love has a variety of meaning it depends on the person on how he or she understands it. However, for Judith Butler saying “I love you” is a cliché and she is dissecting the actual sentence, for her the thought of commitment through marriage or any symbolism is a senseless matter. She concludes that because she believes that circumstances change so as well as the people. Hence, to be able to give a commitment to someone or something you should commit to yourself first, again and again, you need to accept and embrace the changes first in order to know their true meaning of it. People change through time that’s why we should continuously renew our promises and commitments even though there is no symbolism. The concern of bioethics is in the field of medicines, it tackles what is wrong and right in process of making medicines and also, what we should consider in doing operations in prolonging lives. Bioethics will also teach us what is the right usage of health care in the life of one another because the effect of it may be beneficial or risky in our lives. On the other hand, bioethics is essential in our world because it is one of the factors that can tell if we are still doing things morally good or not. Moreover. Death is the end point of one’s life, many people are afraid of death but life is like a rope that has two points, the one is the starting point which is birth and the second one is the endpoint, or basically death. The body has its spirit and soul, when we say soul it is the one who gives us emotions while we are alive, while the spirit is present when the body is dead whereas the brain will stop functioning and the heart will stop beating. Death is just a natural happening in our life. Lastly, Artificial Intelligence is prominent in our time, people are getting creative and innovative. A lot of inventions has been made and a variety of innovation is done that’s why this society is getting dynamic as well as the people.

Bioethics is the area of biological research and medical applications whereas it is bounded by ethical aspects. The application of biological research should be a line with moral standards and the application of medicine in one’s life must be ethical. Bioethics is more likely deals with the disciplines of the usage and application of science in our life and body. This ethical concept helps us to make our body system better, prolong the life that is dying, improve our life, and many more. In a life of an immortal, there are chances that we have complications in our body system that sometimes make our life shorter. Viruses, cancer, and a lot of body complications make our life harder if our bodies suffered from severe illness, and as a person, if you have a commitment to other people or in this world you are going to do everything to fight for your life, you are going to try the different test and medical procedure just to take away the disease that may take away your life. A person is dedicated to doing everything just to live especially if her/ his purpose is to be with his/her loved ones, even if he/she sacrifice all of his wealth, commitment is one of the reasons why human keep on choosing to live when love is the main reason nothing is impossible.

Death leaves us with pain that no one can heal, but death is also the beginning of endless love. This deathless love that human beings continue to feel for the ones they’ve lost. A lot of people are frightened when death is near but how does death function in our life, It served as the realization point where people realize the importance of the person. Always remember that the people you love will never die, they don’t die completely. They live in your mind and you are the reason why their light is still burning not physically. People have different shoes to walk, you choose the path you want. Don’t let death end the memories and experience you gain always keep the fire burning, it will be always inside you, the missing piece of the puzzle that will complete who you are.

The presence of commitment is true if a certain person is willing to render it to someone or to something regardless if there is marriage or anything that symbolizes commitment. In connection with that, human beings can develop love towards an artificial intelligence because artificial intelligence is most likely like humans, they are fixed to act, behave and analyze like humans, at the same time humans have a universal mental process that we called anthropomorphizing or attributing human characteristics like emotions and intent to a non-human entity. This kind of behavior is linked to psychological issues such as anxious attachment.

Overall, the use of artificial intelligence is a concept that is related to human, this concept is more likely to have the assets that human has, it tends to analyze and behave like humans they can respond to problems that humans are facing. For some reason, people worry about the ethics of machines and how they will identify the rightness and wrongness of such a thing, we worry about the machine’s lack of empathy. Machines and technologies are not the basis of making medical decisions, it helps people to boost confidence and how they will treat the patient itself. The help of Artificial Intelligence in bioethics is that they can predict the patient’s health status but they can’t predict when will be the person’s death. No one can stop death, all people have their end not now but someday. Artificial Intelligence is a way to help people have longer life not all people are lucky to reach a hundred age some people died at a young age due to incurable diseases some died due to accidents. The important thing here is how we value the life that we borrowed, and how we make the greatest story of our life. Keep in mind that live life to the fullest and do all the things you wanted to, “You Only Live Ones”.

Artificial Intelligence (AI) can be defined as the capability of a computer based system to think for itself this simulating human intellectual processing. Some human processes that can be simulated are learning, self-adjusting and reasoning; this is possible in machines because of advancements in intelligent expert systems, speech synthesis together with vision recognition capabilities. The evolution of the internet has led to improvements in technology and computer architecture that have revolutionized modern day living through innovations in neural networks in commerce, manufacturing, healthcare and in education. While there are many kinds of exciting tools that are impacting the global economic landscape, we should not overshadow the imminent and existential risk that may hypothetically arise from a super-intelligent, useful, yet potentially harmful applications that artificial intelligence can be used for.

The concerted efforts in the evolution of artificial intelligence can be traced back to 1923, “robot” was used for the first time in a play as the Rossum’s Univeral Robots (RUR). With advancing computer hardware architecture, the first base of neural networks was developed in 1923. In 1950, after the introduction of the Turing Test, a seminal paper named ‘Computing Machinery and Intelligence” was published. Other early inventions included robotics, autonomous vehicles (1979), vision, web crawlers, big data, natural language comprehension and translation, games, case based reasoning, virtual reality and advancement in blockchain technology.

Artificial intelligence intends to assist humans achieve tasks that would otherwise be too difficult and tedious. Therefore, AI is relevant to any intellectual task (Russel & Norvig, 2009, P. 320) and modern techniques have been adopted to advance neural network development. Some well known applications are in autonomous vehicles (such as self-driving cars, drones, bomb-detecting robots in combat situations.), mathematics, game development, search engines, online assistants, flight prediction, judicial verdict decisions, targeted advertisements, medical diagnosis, interstellar travel, event simulation, modeling natural events and journalism. Through use of artificial intelligence, scientists now can generalize the behavior of humans from their digital footprints so as to automatically build customer personas (Matz, S. C., et al). Social media websites are overtaking television and radio as a source of news. Therefore, news corporations are increasingly reliant on social media platforms for generating content and major publishers use technology for dissemination of stories more effectively, therefore generating high volumes of website views (Smith, Mark. 2016).

In conclusion, with the numerous inherent benefits artificial intelligence intends to achieve, it may also be important to identify and mitigate the dangerous or undesirable unintended reprecusions. Short-term advancements in research, innovation and design may influence world economies as well as social structures. However, in the long term, we should continuously optimize processes and minimize security ( Russel, Stuart., Daniel Dewey, & Max Tegmark, 2015). Some of the unintended applications would include terrorists adopting digital warfare, unemployment as some jobs will be rendered redundant, devaluation of human life, social injustice and isolation, and robotics and ungoverned lethal autonomous weapons.

The robotics revolution has started! Since robotics have been manufactured, the efficiency and productivity in working conditions are growing rapidly. They formed a huge leap in our lives as they became part of many workplaces. Moreover, they can implement a wide variety of tasks successfully in a record time compared to human performance. Also, they are playing a pivotal role in helping laborers in their work to be faster and more effective. “When automation or computerization makes some steps in a work process more reliable, cheaper or faster, this increases the value of the remaining human links in the production chain” (Autor, 2015). However, some employees believe that robotics will have an unfavorable impact on the workplace, all because they thought that their jobs are in danger. For instance, now accountants, laborers of construction, farmers, housekeepers, and chauffeurs are worrying about losing their jobs due to robotics “Up to 20 million manufacturing jobs around the world could be replaced by robots by 2030” (Oxford Economics, 2019). So, this will decrease laborer wages and increase the rate of unemployment in the future. From another perspective, robotics may be the main source for real disasters that are waiting to face the world in the future. This essay will discuss the impact of robotics on the workforce and how employees need to cooperate with them in fields such as accounting, workers of construction and farming.

There are so many high-risk fields that still depend on manual laborers as construction workers, who consistently facing hard and perilous working conditions. As a result, the numbers of wounds and death are constantly incrementing. “317 million nonfatal professional injuries and 321,000 occupational fatalities occur all around the world each year, so, that 151 workers sustain a work-related accident every 15 seconds” (Abdalla, Spenser Apramian, Linda and Mark, 2013). However, replacing them with robots will increase safety and permit errands to be finished precisely. For example, in China, they started to use the robot-welder, which is manufactured to pick up a big pile of boards and take them into an elevator to increase safety, instead of hiring workers, because if a laborer is injured while working particularly on this job, it could be a serious issue. Robots also will be cost-efficient in the long run as they can work for long periods with the same efficiency without taking rest. On the contrary, laborers need time to take a break, and they get bored due to the same task that they do every day, so it became a routine for them, and this affects the quality of their work.

Recently, several researches have shown that robots are superior in accounting functions rather than humans, and this affects accountants who are concerned about where they are going to fit if this occurs in the future. “Software programs attempting to replicate human experts, behavior, and expertise, store human knowledge and experience and transform it into rules thus trying to solve accounting problems and perform some accounting tasks” (Stancheva, 2006). Robots not only can be used in banking fields as they will arrange meetings, collect and count money but also, they will be quite useful in business fields to produce a higher quality of output for companies. As well as, they help to utilize the data and understand information faster and accurately so the human error will be reduced and data can be easily checked later on. Moreover, an individual’s positions will turn out to be less stressful, so employee’s tasks can be more centered around significant works that need greater liability. However, replacing accountants who are working as risk-takers with robots cannot be ideal because this type of position needs creativity and from its qualification to be able to take specific decisions when there is an issue, and robots will struggle to achieve this. From another perspective, accountants and businesses have to find the best way in which workers can interact with robots effectively. “Businesses should review their organization’s activities to assess where potential value from automation is highest and create a strategic plan that includes both capital investment and reskilling workers” (McKinsey Global Institute, 2017).

Agrarian robots which related to agriculture became one of the key patterns that will profoundly impact farming and may replace farmers too. “There is a three-in-four chance that artificial intelligence (AI) will replace farmers” (Oxford University, 2019), because they can perform many tasks, especially through planting, fertilizing corn, weed control, and scouting operations. They also can take care of farmland, reap plants, and expanding crop yields. From another perspective, they can help to assist ranchers in dealing with the issue of a waning labor force and permit them to work more proficiently. Needless to say, some countries started to prepare for the TerraSentia robot, that can be wandered autonomously through fields and analyze plants with advanced sensors to identify which is strongest and healthiest and announce back to human officials while inspecting crops. Besides, a French designer created the Wall-Ye robot, that assists with pruning and collecting grapes at vineyards, utilizing infrared sensors and scissor-hands.

On the other hand, robots have their darker side that has to be taken into consideration. For instance, preparing for the design, and intelligent software system requires many types of researches, which is quite expensive and needs an extended time to complete. Also, they are complicated in their maintenance. Moreover, you cannot assemble human knowledge in a machine because it is an endowment of nature. “Humans will always need for effective and embodied interactions with other humans, which can never be replaced by robots” (Lin, 2016). As a result, they cannot interact with unexpected situations. No matter how insightful a machine may become, it can never replace a human. Also, workers can improve and develop their working through age and experiences. However, this cannot be said about robots as they are machines that cannot have creativity or imagination. From another perspective, robots require manual labor to control them because if they become out of control, they may cause colossal disasters.

To sum it all up, it is better to manufacture robots to assist workers in achieving their tasks successfully rather than replacing them. Furthermore, as indicated by most specialists, the sort of assignments that can most effectively be robotized is those that have a serious level of redundancy in either physical tasks or data processing. Besides, with each employment taken over by the machines, there will be an equivalent number of chances for responsibilities to be finished by individuals. “Rather than robots replacing medical or accounting professionals, the latter need to work hand in hand with robots, to continue raising the value of work within their profession” (Lee, Loke, 2007). Moreover, people will wind up in harmonious connections with robots after they comprehend how to interact with them in an ideal manner, because robots perform real tasks that require human intelligence. For having a productive future, numerous specialists recommended that people and robots need to work with each other as robots need to do tasks that can be mechanized while people need to take care of the responsibilities that require an individual or imaginative touch. Furthermore, the government needs to create real solutions to survive those workers who may lose their jobs by providing educational courses and aptitudes retraining for existing and future specialists.

References

1. Abdalla, S., Spenser S., Linda F., and Mark R. (2013). Injury Prevention and Environmental Health. (3rd ed.). https://www.ncbi.nlm.nih.gov/books/NBK525209
2. Autor, D., (2015). Why Are There Still So Many Jobs? The History and Future of Workplace Automation. Journal of Economic Perspectives Volume 29, Number 3. https://ifr.org/downloads/papers/IFR_The_Impact_of_Robots_on_Employment_Positioning_Paper_updated_version_2018.pdf
3. Boston Consulting Group. (2015). The Robotics Revolution. https://ifr.org/downloads/papers/IFR_The_Impact_of_Robots_on_Employment_Positioning_Paper.pdf
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5. Lin, P. (2016). Relationships With Robots: Good Or Bad For Humans? Forbes. https://www.forbes.com/sites/patricklin/2016/02/01/relationships-with-robots-good-or-bad-for-humans/?sh=5fb0843c7adc
6. Matthews, K. (2019). 5 Ways Robotics Will Disrupt The Construction Industry In 2019. Robotics Business Review. https://www.roboticsbusinessreview.com/news/5-ways-robotics-will-disrupt-construction-industry-in-2019
7. Oxford Economics (2019). Robots ‘to replace up to 20 million factory jobs’ by 2030. BBC News https://www.bbc.com/news/business-48760799
8. Schilperoort, L. (2020). 10 Common Jobs To Be Replaced By Robots Within 5 Years – Job Tradition. Jobtradition.com. https://www.jobtradition.com/10-common-jobs-to-be-replaced-by-robots-within-5-years
9. Stancheva, E. (2018). HOW ARTIFICIAL INTELLIGENCE IS CHALLENGING ACCOUNTING PROFESSION. https://ifr.org/downloads/papers/IFR_The_Impact_of_Robot_on_Employment_Positioning_Paper_updated_version_2018.pdf