Artificial Intelligence in Drone Technology for Farming

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Background

The world is facing challenges in expanding food production for its developing population. It is projected that the global populace will be about nine billion by 2050, hence the need to increase food creation by 50% proportionately (Pepe et al., 2018). Any augmented growth in population results in diminished access to water and land. Numerous pictures captured by promenades are grouped and stitched, forming several collections of ortho-maps. Afterward, these orthomorphic charts are fed into the geographic information systems (GIS) frameworks, where they are utilized during the evaluation, coordination, and execution of strategies.

In spite of the devices serving as aerial eyes, their preference emerges from their ability to handle surveys and disseminate quality information after data gathering. According to their sizes, UAVs’ various classes are the very little, small, medium, and huge UAVs. The universal database containing robotic guidelines information was established as an enforcement bureau to provide the guiding principles for drone data. The main challenge affecting the utilization of small UAVs in numerous states is that they are presumed to be airplanes. Undeniably, these miniatured machines are by far and unquestionably different from the planes in many ways. Reworking on UAV enterprise can make it a significant undertaking in every field, especially in the agricultural sector.

Unmanned Aerial Systems (UAS) in Agriculture: Regulations and Good Practices

The majority of the built-up areas such as airports and airplane take-off, departure pathways, and landing locations should be free from any obstruction of soaring objects. Drones are a menace to the aviation industry, and their reputation would be adversely impacted in the event they ram mid-air passenger helicopters (Patel, 2019). Consequently, UAVs are fitted with geo-barrier software which alerts the autopilot upon trespassing the no-fly areas and blocks them from hovering within the controlled zones.

Automated briefs with provisional flying limitations in areas with wildfires assist in safeguarding approved fire-suppressing helicopters and facilitating fire squads’ safe operation without distractions. Geospatial environment online (GEO) software incorporates perpetual gliding restraints around nuclear power foundries, prisons, and other susceptible sites (Patel, 2019). GEO also supports the impermanent curtailment of big stadia crowds and nationwide security occasions. Additionally, it offers flexibility for drone operators by according them access to open some restricted expanses where they are permitted to operate. However, some cunning users may deactivate such features or deploy UAVs that are not furnished with similar security controls.

Enterprises and unfettered operators cannot be entirely entrusted with the adherence to aviation safety guidelines to avoid an insurgence of the survival of the fittest principle. Nonetheless, a balance is necessary between steadfast business and enhanced public protection. A concession between promotion and over-modulation of private ventures is inevitable (Patel, 2019). This is because there are areas where drones can perform unregulated surveys with minimal threat, while access to other airspaces demands adequate aeronautical technical knowledge.

Drone Technology as a Tool for Improving Agricultural Productivity

In modern days, drone technology is often utilized in agricultural insurance claims and stock-taking of animals due to its mapping and imaging abilities. High-pixel infrared cameras enhance accuracy while counting animals as every animal is treated as a distinct heat spot (Bharti et al., 2020). The concentration on an animal also permits the evaluation of its well-being based on temperature assessment, thus enabling prompt identification and nursing of sick animals.

Drones are also employed in the pesticide spraying of the crops in the fields. Existing drones have liquid-pesticide tank volumes of at least ten liters, with an ejection rate of more than a liter per minute. The spraying platform is synchronized and paired with the imaging, analyzing, and automatic processor functions to effectively address affected plants or areas (Bharti et al., 2020). The approach results in improved dosage in the infected section and reduced overall chemical utilization within the portion.

Charting and photographing functionalities of drone frameworks with a variety of sensors can be utilized throughout the production course to boost production planning, thus improving yields. The drone mechanism is deployed in soil condition assessment prior to starting the vegetation cycle and thus provides the potential for better productivity (Bharti et al., 2020). Soil valuation by 3D terrain mapping provides the actual soil color covering, which aids in precise measuring soil moisture, water flow, and land quality.

Automated drones fitted with spraying features are used in the monitoring of agricultural processes and crops to schedule tasks and expeditiously address the observed issues throughout plant life. The integration of advanced drone-acquired airborne data with supplementary information from soil charts and weather predictions is essential in refining terminal data (Bharti et al., 2020). The assimilation also enables the growers to draw full benefits of the land and optimize their normal yield ceiling.

Information and Communication Technology (ICT) in Drones

The development of comprehensive monitoring, mapping, and output projection systems based on distant-sensing and information and communication technology (ICT) has been a key focus in the agricultural sector. The prime target has been the small-scale farmers who play a vital role in sustaining food security globally. Satellite-centered crop monitoring (SCM) ventures assimilate auto-sensing, crop simulation, and ICT applications to produce and deliver accurate and almost real-time data about crop growth and productivity (Awad, 2019). They also detect and provide information on the destruction emanating from biotic and abiotic agitations. Isolated identification-based data and coverage for crops in developing economies technology provides authentic and timely community-level statistics about crop-planted fields. The tool also offers information on the season commencement and its geographical variabilities, and the estimated and definite harvest while portraying any catastrophe’s effects on a particular crop growing stretch. Consequently, the mechanization provides synchronous data for the execution of crop indemnification schemes in numerous states.

Actionable Intelligence from Drones to the Agricultural Industry

During the inception period, drones were used to take aerial views of the field. Farmers frequently flew over their farms as they were required to identify issues such as pests like nematodes, leaf color contrast, and irrigation leakages (Downing, 2018). The commercialization of drones necessitated the incorporation of affixed sensors onto them. This has enabled real-time image capturing, processing, and analysis, thus providing timely actionable insights to farmers. The need was also supported by the SAP Leonardo portfolio’s invention, which adds cognizance to encapsulation, combines technologies, and executes them harmoniously in the cloud (Downing, 2018). The practice acknowledges that though growers and farmers share several traits of prior solutions, their precise requirements are distinct from the offered typical applications.

Drone-centered sensor technology is specifically befitted and tailored for agronomic applications. In conjunction with the internet of things (IoT), they are weighted to amass accurate machinery, weather, and soil data (Al-Turjman, 2020). A recent study reveals that the agri-business sphere was leading in the civil utilization of drones as of 2017. There are numerous uses of drones, including diagnosing crop diseases, identifying pests, monitoring infiltration and fertilization levels, and automating crop pollination and protection.

The establishment of resolution-based actionable acumen requires the utilization of information from both the drones and all other accessible sources of data. It is evident that the material obtained from the drones is insufficient to be used to provide a complete representation of the environment (Downing, 2018) t. Subsequently, the data has to be combined with the weather, machinery, GIS, fertilizer commodity price, in-field IoT, agricultural productivity value, and other agroeconomic references to attain an excellent decision tool

In line with meeting this technological assimilation, connected agriculture is bridged using the Leonardo IoT principles by facilitating association via normalization and uncluttered interfacing. Additionally, the IoT services in the form of SAP Cloud can be employed to link widgets to the SAP cloud program. This enables the utilization of the data in those mechanisms in the integrated applications (Al-Turjman, 2020). Hence, the platform supports two-way communication to connect distant appliances to manage their lifecycle from enlistment to decommissioning and collect the gadget’s information and remit instructions to remote instruments. Such commands may include conversion of off-farm and on-farm irrigation sections based on the drone data analysis.

Drones-based Sensor Platforms

UAVs or unmanned aerial systems (UAS) are unoccupied airliners that hover autonomously and can be guided remotely. They function concurrently with a global positioning system (GPS) and other sensing devices installed on them. The number of drone-driven solutions in entire related industries is huge, and their perceivable value surpasses 127 billion USD (Pepe et al., 2018). With the prevailing and projected exponential growth in the human population, drones have found their way into the agricultural sector to supplement food and agricultural production.

In farming, a drone is simply an inexpensive airborne camera mechanism, furnished with a robotic pilot using sensors and GPS for gathering applicable data. Drones can be likened to normal aim-and-capture cameras for perceptible pictures. Despite the drone and the regular camera being able to give some statistics about plant coverage and growth, the former is superior since it is equipped with a multispectral sensor (Pepe et al., 2018). The feature amplifies the technique’s utility by allowing farmers to sight factors such as stress levels, soil moisture content, plant health, and produce, which are invisible in the discernible range. Drones also assist in overcoming numerous restrictions that hamper agricultural output.

The application of UAVs (drones) in agronomy offers a gateway to real-time access to farm information. They find their use in the cropping cycle during field and soil analysis, whereupon obtaining detailed 3D charts for soil, planting preparations, and assessment of nutrient status commence. During planting, the UAS dart nourished seeds in the soil with a mean penetration of 75%; thus, lowering planting costs (Pepe et al., 2018). Drones also undertake ground scanning and spray the right volume of liquid evenly in real-time while moderating the interspace from the ground. They also execute crop monitoring by observing the stretch-sequence display, which demonstrates the actual crop development and divulges yield inefficiencies for enhanced produce management.

Further, drones with thermal, multispectral, or hyperspectral sensors are used in irrigation to find the dry parts of the field or the ones that require improvements. Finally, UAVs are utilized in crop health assessment which is achieved by employing both near-infrared and visible light to scan crops. The drone-embedded gadgets detect the different amounts of Near-Infrared Radiation (NIR) and green light reflections through the screening (Pepe et al., 2018). The acquired multispectral images are essential in tracking the plants’ transformations and showing their health.

Use of Unmanned Helicopters for Agriculture

In Japan, the famous Yamaha Motor Company was the chief firm involved in the development of a remote-control aerial spraying system (RCASS. The complete package of the aircraft was able to do airborne agrichemicals spraying (Pharne et al., 2018). Another milestone was the crafting of a helicopter having two counter-revolving rotors using a liquid-cooled engine. The mechanism was complex as the control of x-axis rotation (roll), y-axis spin (pitch), and z-axis gyration (yaw) was uniaxial. Due to the challenge hampering the helicopter’s manual flying following its intrinsic servomotors characteristics, gyro sensors were incorporated.

The R-50 was also made in a similar period as RCASS, and its primary rotor utilized a two-stroke liquid-cooled engine. The R-50 was the first remotely-controlled aircraft for crop sprinkling, which had the capacity to hold a payload of 20 kg. An ultrasound sensor was initially used in R-50 for the electronic control of altitude to allow the operator to fully focus on spraying the beneath fields. However, the paddies’ effectiveness was negated, which absorbed the waves, thus necessitating the deployment and adoption of laser sensors to regulate the helicopter’s height (Xuan-Mung & Hong, 2019). The overly sensitive nature of laser sensors to bumpy terrain triggered its substitution with fiber-optic gyros for the altitude control system (ACS). The configuration featured a user-governed model-tracking mechanism to respond autonomously to navigation commands.

The earlier R-50 versions required the operator to utilize the control stick during the helicopter’s entire flying time. However, the integration of ACS automated the control of the three flight axes and simplified the processing of garnered information using the accelerometer and three fiber-optic gyros. The R-50 device could mount an agrichemical tank and spray instrument and undertake airborne besprinkling. The functionality led to labor and stretch of spritzing reduction from several hours to a few minutes. Further innovation led to the development of other hi-tech helicopters such as the RMAX and FAZER series (Xuan-Mung & Hong, 2019). These models were fully equipped with automatic flight modes for industrial use in the agricultural sector.

Since the infancy of commercial unmanned helicopters, several companies have continued to improve knowledge and technical developments to ensure that the aerial helicopters are feasibly efficient and invulnerable to use. The states have also ensured that the people who are licensed to operate and maintain these helicopters undergo rigorous training (Xuan-Mung & Hong, 2019). Other leading firms have also collaborated with the research institutes to diversify and expand agricultural helicopter utilization, such as in vegetable pest control and direct rice sowing in paddies.

Space Technology Use in Crop Insurance

The most crucial pointer in the crop insurance plan for deciding indemnification claims is the crop harvest approximation at the minimal organizational level. The crop cutting experiments (CCEs) approach has been widely used for crop produce projection since it is well established. However, accurate evaluation outcomes require many high-precision CCEs. Currently, the apportionment and assortment of designs for performing CCEs relies on statistical data, and execution is via arbitrary numbers. CCEs plot preference does not represent the real crop state as it does not encompass some of the crop conditions and sown areas (Liu & Ker, 2020). Notably, the innovative crop cover program may be essentially infeasible, thus the need for CCEs site optimization using satellite outlying-sensing information. The provision shows the crop situation and offers a map of the crop area.

In conjunction with ground-truthing, multidate spacecraft remote recognizing data is utilized for charting the specific crop extent. Multicyclic electromagnetic spectrum in the form of SAR space station data is employed in rice crop sensing. Multicast near-infrared and visible radiations are utilized for remote identification of wheat and other crops’ data. Additionally, multidate modulate magnifying satellites are deployed in the determination of distant detection-based vegetation indices. Examples of such indicators include the land surface wetness index (LSWI) and the normalized difference index (NDI), which symbolize crop dampness condition and crop strength status (Liu & Ker, 2020). The whole field is subdivided into four proportions from the data obtained via NDI and LSWI: poor, medium, good, and very good. A crop-specific situation map is generated from the overlaid crop charts produced from the high-pixel satellite data. CCE locations are then proportionately picked from each division depending on the picture element in each stratum.

Advancement in crop coverage schemes has also led to the development of Android applications for gathering CCE information, geographic positions, and field images. The entire CCE data garnered via smartphones are synced with the designated geoportal of the remote sensing centers (Liu & Ker, 2020). Evaluated data from the remote sensing-based indices captured via smartphones has been statistically useful in enhancing CCE planning and crop insurance initiative. The farmers benefit from the smartphone CCE data-gleaning technology as it ensures the timely transmission of precision CCE information necessary for determining crop indemnity claims.

Drones for Community Monitoring of Forests

The elementary purpose of community forest monitoring (CFM) is to oversee crop status, check territorial invasions and recognize variations in particular locations of forests experiencing degradation and deforestation. The produced maps assist the authorities in safeguarding, administration, and forest conservation decision-making. The process trustees prepare and develop carbon and forest inventories and generate forest resources dataset for communal intellectual property decorum implementation on plant species’ traditional know-how (Wich & Koh, 2018). CFM boosts the preservation and supervision of indigenous natural resources by building the capacity of native people to undertake remote sensing through GIS and carbon and forest records. CFM also utilizes a standardized approach to generate geo-referenced data among the diverse original regions while meeting each area’s definite requirements. Further, the practice endeavors remote-detecting information storage calibration at various scales and reliability in the data processing.

Prudent protection, management, and conservation of forest land, natural resources, and indigenous ecosystems are accomplished by furnishing the communities with the requisite local-level knowledge of their territory. CFM gives the communities the opportunity to guide the gathering and evaluation of data according to society’s specific interests. The blend of remote-sensing and terrestrial-monitoring permits scientists and other interested parties to understand the forest cover degradation, loss, and restoration dynamics. Subsequently, the outcomes of the analyses direct the decision-making process on the preferred interventions for sustainable conservation and forest resources supervision. The acquaintance with the forest variabilities and situation supplements the national forest monitoring system (NFMS). The infrastructure supports a conceptual basis that outlines the roles and collaboration of all the involved actors (Wich & Koh, 2018). The technological framework of CFM offers an array of monitoring sites endowed with sufficient tangible equipment. The initial venture comprised a centralized station and six surveillance locations mounted on different areas with forest cover.

Finally, CFM encompasses the active involvement of local people with different levels of specialized training, knowledge, and duties, thus enkindling the community’s technical capacity growth. Endorsed technicians have undertaken applicable professional guidance required to conduct observations and measurements of forest information and documentation necessary for terrestrial monitoring (Wich & Koh, 2018). The train personnel also superintend satellite photography remote-sensing and drone captured airborne pictures. Finally, they utilize GIS to create and manage surveillance system data.

Conclusion

Drones employed in AI agriculture may be thought of as an intensive approach driven by advanced technology. However, there are features of this innovative farming, such as applying variable rate mechanization and location-centered soil data, that are already in use globally. UAS science has the latent of assisting the farmers in optimizing their inputs. They can also give the agronomists detailed, timely, and granulated information. Harnessing drones use in small to large-scale farming has a high probability of attaining higher agricultural output and a substantial increase in investment returns. Further, the practice is characterized by reduced emissions to the surroundings, thus ensuring environmental sustainability.

References

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Downing, J. (2018). Next-generation mechanization. California Agriculture, 72(2), 102-104. Web.

Liu, Y., & Ker, A. (2020). Simultaneous borrowing of information across space and time for pricing insurance contracts: An application to rating crop insurance policies. Journal of Risk and Insurance, 88(1), 231-257. Web.

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Pepe, M., Fregonese, L., & Scaioni, M. (2018). Planning airborne photogrammetry and remote-sensing missions with modern platforms and sensors. European Journal of Remote Sensing, 51(1), 412-436. Web.

Pharne, I., Kanase, S., Patwegar, S., Patil, P., Pore, A., & Kadam, Y. (2018). Agriculture drone sprayer. International Journal of Recent Trends in Engineering and Research, 4(3), 181-185. Web.

Wich, S., & Koh, L. (2018). Conservation drones: Mapping and monitoring biodiversity (1st Ed.).

Xuan-Mung, N., & Hong, S. (2019). Improved altitude control algorithm for quadcopter unmanned aerial vehicles. Applied Sciences, 9(10), 2122. Web.

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