Artificial Intelligence in Smart Farming

NB: We do not resell your papers. Upon ordering, we do an original paper exclusively for you.

NB: All your data is kept safe from the public.

Table of Contents

Background
Details & Description
Methodology of Implementing AI in Farming
Implications of AI in Smart Farming
Future Research
Conclusion
References

The growing number of people that are living on Earth right now creates scary projections that might be associated with many challenges. For instance, the magnitude of population growth could seriously deteriorate the food production industry. Because of this, the current directions from the UN predict the need to develop food production and enhance agriculture across the globe. Such initiatives are expected to feed the anticipated population with no limitations until 2050 (Wolfert et al., 2017). One of the few technologies that are going to support these improvements is smart farming. It is based on artificial intelligence (AI) and may be expected to facilitate the majority of agricultural processes to a certain extent, where it would be much easier to implement technological solutions and collect crops of better quality.

Owing to the development of the smart farming concept and precision agriculture, farmers all over the world gained a chance to implement digital tech to their daily operations and utilize AI to support some of the most important agricultural activities. The number of handheld agricultural tools is quickly decreasing, creating more room for the new industrial revolution that is going to move agriculture forward and contribute to a fundamental shift in how farmers view their industry (Wolfert et al., 2017). The current paper represents a thorough review of the existing evidence on why smart farming is beneficial and how the new technologies could be used to support farmers. The implications of utilizing smart farming and future research directions are also addressed to outline the forthcoming trends in AI-driven agriculture.

Background

To start with, the whole concept of smart farming is based on several technologies that are consequently developed to respond to the growing demand in the agriculture industry. AI-based smart farming includes multiple sensors that can be used to read and process information concerning humidity, soil condition, and water and temperature supervision (Walter et al., 2017). Farmers may also use smart technologies to gain more insight into networking and the usage of GPS tracking. On the other hand, there are multiple IoT-driven solutions that might include (but never be limited to) automated tools, robotics, and many other specific hardware and software tools. Speaking of software, smart farming seriously benefits from data analytics, as it allows them to predict and monitor climate change, crop yields, weather data, and other variables that are vital to the farming industry and agriculture in general (Bhange & Hingoliwala, 2015). The entire field can be easily assessed by drones and satellites that easily track the region and collect relevant data without major human interventions.

Given the fact that agricultural efforts now are quickly translated into the digital framework where the majority of tasks can be completed remotely, the advent of machine-to-machine (M2M) data collection becomes even more critical (Sa et al., 2017). The decision-making systems available to farmers are easily populated with the data from the fields and offer a great degree of detail. With the help of new technologies, farmers can pick the best strategy when adapting their measures to the field, increasing the efficacy of fertilizers and pesticides (Walter et al., 2017). Much more sensible utilization of these instruments promotes the usage of smart farming techniques and makes AI-based systems a vital element of agricultural strategies, as it enhances the condition of the field and helps farmers track herd health in real-time.

Details & Description

Precision agriculture and smart farming have become the two essential contributors to the popularization of digitalized agrarian science. The existing farming practices were significantly enhanced with such technologies as driverless tractors, non-human planting and seeding, automated irrigation, remote crop maintenance, drone-based crop and herd tracking (Pivoto et al., 2018). The lack of human error increased the quality of products in the agricultural sector and improved production efficiency to a certain extent. Another evident consequence of smart farming being implemented more often is the growing quality of life among farmers who do not have to complete endless heavy and monotonous tasks anymore. Digital technologies are currently changing the image of farming and creating more opportunities for farmers to look after crop yields and animal health more vigilantly (Eastwood et al., 2019). With the help of smart farming, experts in the field of agriculture are recurrently addressing labor issues, climate change, and population growth.

The advent of real-time monitoring and analysis technologies have created multiple benefits for farmers. Practically any element of agriculture can be translated into the digital environment with no actual losses, which makes the new industrial revolution a significant trend that cannot be overlooked on the way to agricultural initiatives that are entirely led by technology with minimal human intervention (Bronson, 2018). Based on the existing evidence, it may be concluded that there are three large pillars of smart farming that have to be nurtured to gain access to even more benefits: (a) the Internet of Things, (b) autonomous robots, and (c) drones (OGrady & OHare, 2017). Each of these categories significantly contributes to the transformation of farming activities, where agriculturalists get a chance to gain more digital knowledge and monitor their assets remotely.

Methodology of Implementing AI in Farming

In order to implement AI in farming, experts in agriculture have to analyze their ground data and then find the best ways to analyze different weather conditions and additional sensors in real-time. In order to make the best use of AI in farming, these experts have to possess extensive knowledge in technologies and realize the value behind gaining access to soil conditions and other contributors to informed decisions (Andrewartha et al., 2015). Additionally, the implementation of AI technologies should be performed with the primary intention of optimizing planning procedures. In this case, experts will have to determine the right crop choice and pre-plan utilization of all available resources. With a variety of improvements related to harvest quality being the main idea behind the implementation of AI systems, experts have to be as precise in their actions as possible to protect automated systems from human error (Xin & Zazueta, 2016). Accordingly, AI sensors will then serve as hunters that help farmers find diseases in plants and make informed decisions on what herbicides or pesticides to use.

Another essential element of AI implementation is the willingness to overcome the labor challenge. Even under the condition where many farms are going through a period of severe workforce shortage, experts should still contribute to the development and deployment of AI-based farming to achieve more significant results (Xin & Zazueta, 2016). The trend to watch out for, in this case, is going to be the decreasing number of seasonal farmworkers. The number of workers will go down due to automated crop harvesting and other operations that were previously completed by human employees. When implementing AI to agriculture, stakeholders should carefully pick the most suitable employees with required competencies in order to limit the shortage of job positions and preserve the value of the human contribution. One more potentially important element of AI implementation are chatbots that can be of two-fold assistance to farmers. Experts will have the possibility to provide their apprentices with recommendations and answer their questions while also gaining insight into specific farm issues in real-time (Ravazzani et al., 2017). The implementation of smart farming initiatives can be performed at farms of any size, leaving the room for additional improvements.

Implications of AI in Smart Farming

Owing to the controversial nature of smart farming, the use of AI in agriculture creates both positive and negative implications. The most important thing about utilizing smart farming technologies is that it opens the prospect of soil sensing. It means that farmers fields can be easily tested for various nutritious constituents, condition of irrigation channels, or even the health of the crop (Rose & Chilvers, 2018). This information can be accessed in real-time, allowing farmers to make decisions based on their current status and available equipment. Another favorable implication of utilizing AI in farming is that the necessary resources can be conserved promptly. The smart farming system is going to apply a required amount of water and fertilizers to the areas necessary only, averting potential human errors. The usage of intelligent farming can be deemed as a yield-maximizing initiative that contains valuable information on practically anything from humidity and soil conditions to environmental temperature and precipitation predictions (Rose & Chilvers, 2018). The implementation of AI in farming helps agriculturalists reduce the usage of electricity and pay more attention to data collection and wireless monitoring instead.

Nonetheless, there are also negative implications related to the application of AI to farming procedures. The biggest issue related to smart farming and its derivatives is the necessity to have a high-quality, uninterrupted connection to the Internet (Ahmed et al., 2018). This puts the majority of rural communities at a severe disadvantage, primarily if the given collective farm is located somewhere in a developing country. Mass crop production in developing countries would require major investments due to the potential installation of tens or even hundreds of thousands of sensors. This would make AI-based systems inoperable and excessive in terms of their cost.

On the other hand, the implementation of AI requires the local community to have an exceedingly high knowledge of ICT and robotics (Schonfeld et al., 2018). The lack of precision and technical skill would make smart farming a useless, but a rather costly asset. To conclude, the lack of expertise might be the first item on the list of discouraging factors that slow down the implementation of smart farming across the globe.

Future Research

With all the advancements in the area of smart farming, it may be safe to say that there are even more improvements that are going to impress the farming world in the future. Drones, robots, and tracking technologies are just precursors of what farmers could benefit from in the future. This places a serious burden on the shoulders of smart farming researchers who will have to investigate the newest trends in the field and ensure that the fresh ideas are going to be implemented as soon as possible. One such direction is the blockchain technology that operates based on the Internet of Things (Ahmed et al., 2018; Pivoto et al., 2018). Multiple data sets regarding crops could be transferred simultaneously while being properly encrypted against potential hacker attacks. The lack of research on the blockchain is a crucial concept that has to be addressed by experts in smart farming.

Another weak area of smart agriculture that has yet to be strengthened by additional research is the usage of sensors. Additionally, new sensors could also be based on blockchain, allowing farmers to identify pH levels and sugar content (Bronson, 2018; Rose & Chilvers, 2018). As the population grows exponentially, farmers will have to install multiple new sensors to gain more control of the crops and ensure that all the data points are efficiently processed by automated AI-based systems. The process of using drones in agriculture has not been studied to the fullest as well. Some of the potential benefits of introducing drones to farming may also include improved spraying techniques and greater control over crops with remote decision-making capability.

Conclusion

As a relatively undeveloped branch of agricultural science, AI-based instruments and smart farming, in general, can be considered the most viable path to continuous advancements. All the existing improvements in the area of smart farming show that the popularization of technologies had a positive influence on agricultural activities as well, helping farmers from all over the world save time and money when tracking the health of their crops and herd. Farmers are now free to use different sensors and the Internet of Things to collect all types of data and improve irrigation, planting procedures, or manage temperature without even visiting the field in real life. The increasing accessibility of intelligent software and hardware makes it reasonable to assume that the future of farming depends on the digitalization of its major processes and the advent of new technologies that are going to help farmers gain even deeper insights into their assets. Water and fertilizers are essential resources that have to be conserved by farmers, and the use of AI in smart farming could be the shortest pathway to proper agricultural maintenance of available inventory.

On the other hand, smart farming may be helpful in terms of protecting the environment from the negative impact of human activities. Predictive techniques included in AI-based instruments will help farmers from all over the world keep their fields and herds in order and collect vital data from thousands of sensors in real-time. Nonetheless, there is also a need for constant research in the area that would improve the existing techniques and come up with new ones. In turn, this would facilitate farming practices and help farmers ensure that difficult tasks are completed remotely, with the help of software and hardware that run on AI. As the current evidence shows, smart farming requires rigorous investments and a lot of persistence. There is no other way to develop smart farming rather than build more unique sensors and deploy additional preventive agrarian techniques. The current paper proves the need for the implementation of more elements of smart agriculture to conventional farms to increase their effectiveness and protect the environment.

References

Ahmed, N., De, D., & Hussain, I. (2018). Internet of Things (IoT) for smart precision agriculture and farming in rural areas. IEEE Internet of Things Journal, 5(6), 4890-4899.

Andrewartha, S. J., Elliott, N. G., McCulloch, J. W., & Frappell, P. B. (2015). Aquaculture sentinels: Smart-farming with biosensor equipped stock. Journal of Aquaculture Research & Development, 7(1), 1-4.

Bhange, M., & Hingoliwala, H. A. (2015). Smart farming: Pomegranate disease detection using image processing. Procedia Computer Science, 58, 280-288.

Bronson, K. (2018). Smart farming: Including rights holders for responsible agricultural innovation. Technology Innovation Management Review, 8(2), 7-14.

Eastwood, C., Klerkx, L., Ayre, M., & Rue, B. D. (2019). Managing socio-ethical challenges in the development of smart farming: From a fragmented to a comprehensive approach for responsible research and innovation. Journal of Agricultural and Environmental Ethics, 32(5-6), 741-768.

OGrady, M. J., & OHare, G. M. (2017). Modelling the smart farm. Information Processing in Agriculture, 4(3), 179-187.

Pivoto, D., Waquil, P. D., Talamini, E., Finocchio, C. P. S., Dalla Corte, V. F., & de Vargas Mores, G. (2018). Scientific development of smart farming technologies and their application in Brazil. Information Processing in Agriculture, 5(1), 21-32.

Ravazzani, G., Corbari, C., Ceppi, A., Feki, M., Mancini, M., Ferrari, F.,& & De Vecchi, D. (2017). From (cyber) space to ground: New technologies for smart farming. Hydrology Research, 48(3), 656-672.

Rose, D. C., & Chilvers, J. (2018). Agriculture 4.0: Broadening responsible innovation in an era of smart farming. Frontiers in Sustainable Food Systems, 2, 87-94.

Sa, I., Chen, Z., Popovic, M., Khanna, R., Liebisch, F., Nieto, J., & Siegwart, R. (2017). weednet: Dense semantic weed classification using multispectral images and mav for smart farming. IEEE Robotics and Automation Letters, 3(1), 588-595.

Schonfeld, M. V., Heil, R., & Bittner, L. (2018). Big Data on a farm smart farming. Big Data in Context, 109-120.

Walter, A., Finger, R., Huber, R., & Buchmann, N. (2017). Opinion: Smart farming is key to developing sustainable agriculture. Proceedings of the National Academy of Sciences, 114(24), 6148-6150.

Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming a review. Agricultural Systems, 153, 69-80.

Xin, J., & Zazueta, F. (2016). Technology trends in ICTtowards data-driven, farmer-centered and knowledge-based hybrid cloud architectures for smart farming. Agricultural Engineering International: CIGR Journal, 18(4), 275-279.

Click Here To Order Now!