Introduction
Precision Agriculture (PA) describes a rustic production context that relies on innovation and data with the ultimate aim of decision-making, dissecting, and overseeing the modules. An example of such a framework is the empirical spatial and transient variation in obtaining the superficial value, ecological protection, and sustainability. Nowadays, most PA explorations are geared towards implementing innovative instruments and sensors for ease in remote identification of soil and crop aspects in actual prevailing time. Robots are broadly classified into two key groups, namely the mobile wing and stationary wing promenades. The moveable wing saunters have the ability to drift, leave and land upright with spry moves. Subsequently, they have a truncated momentum, transitory soaring range and are detrimental to perfunctory volatility. In contrast, the static wing rambles are structurally weak and lack the capacity to withstand prolonged sailing at extreme speed. Further, these mechanisms do not float and require launchers or runways for taking off and mooring.
Background Information
Fidelity cultivation involves technological techniques that set the mitigation strategies for best preparation, irrigation, and pest control tactics. One significant innovation in accurate cultivating is the Variable Rate Application (VRA) which utilizes the field materials at the point of use to augment development on the entire area. The distinction of plant and soil features is essential for the effective administration of the VRA and their geo-positioning on the map. Recognition of the plant and soil characteristics is attained by employing three discrete methods: documenting the resultant maps, soil assessment, and imaging of the hyper repugnant and multiplex phantom.
In present times, the most preponderance practices of differentiating the inherent properties of plant and soil involve making use of images from hovering appliances. The greatly renowned such automaton is the unmanned aerial vehicle (UAV). These interceptors give instantaneous photographs while flying at great heights over agrarian areas. The transmitted pictures differ from those attained by satellites in those expanses. Besides, these remotely-controlled aircraft are also equipped with provisions for diseases and weeds control, soil properties evaluation, and discerning vegetation cover. The machines have made the making of precise augmentation representations in real-time possible. This research gives an in-depth discourse of these autonomous vehicles (robots) and their deployment in conscientious agronomy.
What is Precision Agriculture?
PA is an integrated crop management infrastructure that assimilates current information age technologies into the advanced agricultural sector. It tries to align the quantity and type of inputs with the tangible crop requirements for particular areas within a farm (Miller et al., 2018). Unlike the traditional village, where entire fields are managed as a single entity, management of the land in PA is tailored for small sections confined in the fields. This customization promotes sound agricultural practices, thus improving the efficiency in the running of the enterprise. Therefore, PA is a technological farming practice that exhibits the prevailing paradigm shift in the mode of cultivation.
How does it work in farming?
PA is a contemporary science of farming that encompasses noticing, quantifying, and reacting to the spatial and terrestrial mutability in crops. It is often referred to as satellite farming guided by an explicit location model of crop management agriculture, which enhances efficiency in the production approach. The system is utilized to precisely lower the cost of production, handle field variants, and boost yield using lesser inputs (Bobryk et al., 2017). PA makes it possible for farmers and growers to work with enriched soils in extensive lands and run them in small subdivisions. Furthermore, PA incorporates a strategic schema of directing agronomists in soil management, crop rotation, and sowing and harvesting peak times which increases efficiency and crop throughput while ensuring lowered environment effects.
PA utilizes information technology (IT) to guarantee the best soil productivity and health to satisfy all the crop necessities. PA application consolidates the use of global positioning systems (GPS), mapping tools, sensors, robotics, and agricultural production data-analytics, thus the need to provide farmers with technical support and assistance (Bobryk et al., 2017). Other electronic programs are varying-rate watering, position-particulate pesticide administration, output mapping, and pest scouting for crop growth improvement without labor increment. Precision farming aims at curtailing environmental degradation while enhancing crop productivity for maximum economic gains. It also utilizes ultra-modern technology in crop science and optimal practices of field management to meet the agricultural obligations of farmers.
Technologies related to Precision Agriculture in Farming
An Overview of PA Technology
Human-machine interface devices form the terminal instruments that are suitable stand-alone applications. The machine instructions guide similar tracks of automatic operations for relieving apparatus drivers and preventing overlapping of fuel and chemicals. The ownership of data functionality enables farmer-farmer, supplier-farmer, and farmer-government interchange of information (Clapp & Ruder, 2020). Sampling site devices offer offline evaluation of soil composition and quality and the amount of phosphor, magnesium, potash, and pH-value in the ground deluge. The provision of biomass monitoring allows mapping of the required nitrogen quantity levels and plants’ growth status.
Machine vision systems guarantee the produce’s security and safety by relating the collected information with the manufacturer’s data. Harvest monitoring features provide information about localized reaping of crops and the state of the machine to boost yields. Sensor and sensor blend development techniques offer a fusion of automated information from various sensor statistics to facilitate real-time choices concerning the multilayer databases (Clapp & Ruder, 2020). The variable-rate technology permits the application of fertilizers, seeds, and sprays in accordance with the perfect plant and soil information mapping. Area electronic submission supportive applications ensure compliance with the prevailing legal frameworks on aerial surveillance. Finally, farm management and decision backup software provide automated telemetry, documentation, machine control, and recommendation assistance.
Unmanned aerial vehicles
Unmanned aerial vehicles (UAVs) are mostly referred to as drones in the field of the internet of things (IoT). The invention of UAV-based censoring systems has highly promoted the PA due to the remote monitoring capabilities. UAVs’ deployment in watching over crops provides a quick, cost-effective, fast, and simpler mode of field data acquisition compared to the earlier techniques (Tsouros et al., 2019). In PA, UAV centered on IoT is considered as the most ideal for remote sensing technique. The ability of the UAVs to sail at low heights results in capturing ultra-high three-dimensional pictures of the crops. This attribute significantly contributes to the performance improvement in the monitoring applications. Further, UAV-based sensing mechanisms can be employed whenever the user demand as they contain high chronological resolutions. The provision heightens flexibility in the process of image taking and storage.
UAVs are less expensive and have ease of use than other manned aircraft (Tsouros et al., 2019). They also have the capability of covering expansive fields within short durations without causing any destruction; thus, they are more resourceful than flooring systems. Consequently, they are well-suited applications such as measuring crops’ growth, monitoring irrigation machines, and prescription of variable-rate fertilization. They are also used in identifying weed, bit spraying, and tracking the cattle herd. With the deployment of UAVs, PA has the potential of being a more sustainable venture.
N-sensor Technology
The technology facilitates the detection of nitrogen status and composition, and biomass of the crops. The functionality is attained by assessing the reflectance of the canopy in sections with red and near-infrared emission continuum (Ondoua & Walsh, 2017). Site-specific propagation of nitrogen within an area is allowed by merging the spectral data with unique algorithms. The N-sensors are essential in preventing under and over-fertilization within the targeted field; hence the lowered lodging, more uniform ripening, and better yield. However, N-sensors are best suited for environments where nitrogen is the key growth-inhibiting factor. Thus, regardless of the N-management schemes, other aspects limit the plant development resulting in an insignificant produce increase (Ondoua & Walsh, 2017). Consequently, farmers should develop and adopt the best management tactics to enjoy the benefit of crop sensor tools. Therefore, modern practices should endeavor the development of algorithms that consider additional aspects such as surrounding temperature, soil moisture content, crop progress physiognomies such as plant stature.
Precision Agriculture Tools
PA provides the automation perspective for simplification of information gathering and analysis. The reservation permits the making of managerial decisions and timely implementation on lesser areas within macro-scale fields (Paustian & Theuvsen, 2016). Effective collection and deployment of data call for users’ familiarity with the available precision farming technological tools. These appliances include the software, hardware, and the approved handling practices. Some of these aspects are discussed in the following section.
Remote Sensing
Remote detecting and monitoring involve assembling data while at a distance. Information detectors can comprise either portable instruments fixed on hovers or devices installed in satellites (Paustian & Theuvsen, 2016). The remotely acquired data offer a mechanism for examining the health of crops. Plant constraints associated with nutrients, crop infections, moisture, compaction, and other growth-related concerns in plants are straightforwardly perceived via aerial images. The automated cameras also take logs of close-infrared pictures that exhibit the condition of the plant tissue. The new generation of snapshot sensors with great spectral magnification improves the quality of the satellite collected information. Distant intuition can unveil seasonal erraticism, which distresses yield in crops. The predictability can be well-timed enough for deliberation of governing inferences that boost the existing crop’s profitability. Far away detected photographs can aid in determining the point and degree of crop destruction. Further analysis of those photos alongside scouting assists in defining the root of particular elements of crop diseases. Ultimately, the imageries are utilized in the development and implementation of a spot management strategy that enhances the application of agricultural compounds.
Geographic Information Systems
Geographic information systems (GIS) comprise computer software and hardware that generate maps using unique attributes and bearings data. GIS stores various statistics, such as soil sample maps, crop scouting reports, yields, levels of soil nutrients, and remotely obtained data (Paustian & Theuvsen, 2016). Interpretation enhancement of the geographically referred figures’ visual outlook is achieved by displaying the data in the GIS. Additionally, GIS is used in the evaluation of the pre-existing and alternative overseeing through amalgamation and manipulation of data loops to engender a dissection of management situations.
Global Positioning System Receivers
Global Positioning System (GPS) satellites disseminate signals that sanction their receivers to compute their location. The statistics are provided instantaneously, implying that the uninterrupted locus information is portrayed while in locomotion. The synchronous data of the exact site allows mapping of the crop and soil proportions (Paustian & Theuvsen, 2016). The receivers of the GPS can be transmitted either to the field or attached on apparatuses and give the users the allowance to go back to particular locales for sampling or handle those sections. Unaltered signals of GPS are believed to have an accurateness of approximately 300 feet. The usefulness of these uncorrected signals is accomplished by comparing them with a satellite-based band that gives a differential rectification with an absolute accuracy ranging from 3 to 10 feet. In most areas, the beacons for gap correction are offered free of charge. The user should consider divergence tweak and the relative coverage use before acquiring a GPS receiver.
Yield Monitoring and Mapping
The movement of the particles in the clean-grain conveyor of the integrator is measured and recorded by the granule yield sensors. When connected with the GPS satellite, produce sensors are able to generate the requisite data for planning yields (Kirkaya, 2020). Yield recordings play an important role in the making of rigorous supervisory resolutions. However, gauging of landscape, soil, and other surrounding factors is essential during yield map interpretation. Proper utilization of data obtained from yield offers significant pointers in establishing the impacts of controlled cultural practices like irrigation and cultivation and farm inputs such as lime, pesticides, fertilizer, and seeds. Analysis of yield records acquired from cumulative histories for a number of years facilitates the determination of reliable yield measurements. This is because climatic and weather patterns adversely influence the yield levels.
Variable Rate Fertilizer and Grid Soil Sampling Application
Recommendations for fertilizer use by the crop advisors are based on the laboratory tests of soil samples collected from random locations. The sampling area utilized is often a field not exceeding 20 acres of coverage (Kirkaya, 2020). Grid soil sampling employs systematic networks to gather soil specimens, which also constitutes the details of the locality for data representation. The technique allows the construction of the application map for every sample, which is essential for quantifying the nutrient requirements for crops in that field. Afterward, the fertilizer application chart is derived from all the soil samples in the set. The application diagram is fed into a computer attached to a variable-rate fertilizer diffuser. The computer utilizes a GPS satellite and the discharge record to guide the produce-delivery regulator. This controller is tasked with varying the type and quantity of fertilizer brand as contained in the application plan.
Crop Scouting
The in-season surveillance of the crop situations involves observing for any patches, intensity, and type of wild plants. The process also entails noting the magnitude and variety of fungal invasion or insects, the status of the crop tissue, and spotting the eroded and waterlogged sections of the land. Linking the observations with the precise locations for future tracing during treatment is accomplished by deploying GPS receivers mounted on a knapsack or an all-terrain automobile (Kirkaya, 2020). The observations can also be employed when describing the disparities in yield records.
Information Management
The embracing of PA calls for collaboration in the development of the applicable information datasets and governance skills. A farmer must be acquainted with the vivid awareness of the venture’s goals for effective use of the PA information. Familiarization with the objective is also vital for the gathering of core information to aid in decision-making (Kirkaya, 2020). A successful information management infrastructure incorporates GIS and entrepreneurial perspective by offering experimentation and educational features.
Advantages & Disadvantages of Precision Agriculture in Farming
One of the benefits associated with PA is the ease of surveying the agricultural fields due to GPS incorporation. The farming technique also utilizes sensors that aid in the real-time control of variable rate application devices, measuring yields, mapping, and monitoring soil properties (Turin, 2020). The practice also allows sub-division of irregular farming areas into micro-scale portions based on their distinct requirements, which enables expeditious undertaking of corresponding interventions. PA offers prospects for enhanced management of resources and minimized wastage. Additionally, it supports air monitoring sensors which detect emissions, thus eliminating air pollution. Last but not least, PA optimizes the utilization of agrochemical materials, thereby reducing environmental degradation due to contamination of underground water and nitrate leaching.
Notwithstanding PA enterprise’s pros, a user requires expert advice prior to espousing the technology due to its inherent limitations. One such shortcoming is that the technique requires high initial capital, making its adoption viable as a long-term venture. The process of collecting sufficient requisite information for the successful implementation of the system is time-consuming, spanning several years (Turin, 2020). The practice entails an exhaustive course of data collection and analysis. Finally, PA requires individuals to be slightly acquainted with computer-based intelligence skills, which may be challenging to many average farmers.
Future Research
Presently, PA infrastructure with technological integration has been established to sustain a broader enactment of the mechanism. However, the adoption of the innovation has been faced with numerous obstacles. These hindrances include institutional and logistical restraints, cultural insights, high costs for start-up, lack of practical domestic support, risk of low return on the venture, and technical skills disparities (Bhatia & Duda, 2019). To date, embracing and developing PA have been driven by the private sector providers with the government and other public establishments have made a little contribution for the extensive implementation of PA. Nonetheless, any resolution from the public affiliations to facilitate its assumption should consider the pre-existing economic substructure in-depth merit.
Further exploration should be done to identify land and regions’ typology with suitable conditions for PA implementation while endeavoring environmental stewardship and healthy competitiveness of farm. Pilot studies need to be undertaken to ascertain the ideal probable ecological benefits to raise awareness of farmers to expand the use of PA towards extensive large-scale farming (Veroustraete, 2016). The authorities in charge of managing development in rural areas also ought to utilize these preliminary trials when designing their programs and interests to align with PA.
Supplementary surveys and analysis are desirable to enhance the cost-efficiency and knowledge aspects of PA. On the verge of examining PA’s environmental consequences, the case studies have to inspect the broader eco-friendly footprint and be undertaken outside the field-particulate and farm measures (Veroustraete, 2016). An exactness calculator for farming integrated with environmental compliance guidelines should be availed for the various diversified agriscience systems. The estimator should provide for the quantification of conservational gains and the agronomist’s production advances.
An additional inquiry needs to be done to determine the viability of establishing an independent institution to handle the farm advisory services by offering precision advice and technical support to farmers. For active dispensation of roles, the body should not be affiliated with any other profitmaking organization. The enhancement of the automation transference, knowledge, cognizance of PA is inevitable. This mainstreaming will incorporate evidence-centered benchmarking, thus improve performance of PA, data capturing, processing, and impact assessment
Finally, a comprehensive analysis of the extent to which the freely accessible products for PA applications are fed with accurate data must be undertaken. This will provide information on the availability of pertinent reference data from remote sensing suites, which encourages the possible improvement of PA programs (Bhatia & Duda, 2019). The provision will also pave the way for more exploration on the prospective utilization of PA in crowdsourcing farm-data fetching and materials for IACS underpinning. Eventually, enriched inputs for harvest modeling and derived benefits will be attained.
Conclusion
Precision husbandry provides farmers with the capacity to effectively utilize crop resources such as pesticides, fertilizers, manure irrigation, and tillage water. Remarkably, excellent and efficient deployment of farm inputs results in more and quality yields in crops without causing environmental degradation. Nevertheless, it has been relatively challenging ascertaining the fringe benefits in the management of PA. Currently, countless technologies are in their infancy stages in deployment. In addition to that, specifying the machines’ prices and the prerequisite services they are expected to render has not been unanimous.
The study has delineated that precision agribusiness can be used to deal with both the existing ecological and financial concerns that hamper crop production and the farming sector at large. Many farmers can manage PA since it requires only a moderate level of expertise in handling the equipment. However, the extent of PA benefits realization is highly dependent on the acquaintance and the swiftness in retrieving and amassing the guiding knowledge on advanced technologies.
References
Bhatia, K., & Duda, D. (2019). Precision farming. International Journal of Trend in Scientific Research and Development, 3(3), 403-406.
Bobryk, C., Yost, M., & Kitchen, N. (2017). Field variability and vulnerability index to identify regional precision agriculture opportunity. Precision Agriculture, 19(4), 589-605.
Clapp, J., & Ruder, S. (2020). Precision technologies for agriculture: Digital farming, gene-edited crops, and the politics of sustainability. Global Environmental Politics, 20(3), 49-69. Web.
Kirkaya, A. (2020). Smart farming – Precision agriculture technologies and practices. Journal of Scientific Perspectives, 4(2), 123-136. Web.
Miller, N., Griffin, T., Ciampitti, I., & Sharda, A. (2018). Farm adoption of embodied knowledge and information intensive precision agriculture technology bundles. Precision Agriculture, 20(2), 348-361.
Ondoua, R., & Walsh, O. (2017). Precision agriculture advances and limitations: Lessons to the stakeholders. Crops & Soils, 50(4), 40-47.
Paustian, M., & Theuvsen, L. (2016). Adoption of precision agriculture technologies by German crop farmers. Precision Agriculture, 18(5), 701-716.
Tsouros, D., Bibi, S., & Sarigiannidis, P. (2019). A Review on UAV-based applications for precision agriculture. Information, 10(11), 349.
Turin, E. (2020). Advantages and disadvantages of no-till farming around the world (review). Taurida Herald of the Agrarian Sciences, 2 (22), 150-168.
Veroustraete, F. (2016). Is there a future for precision farming with a smaller environmental impact? Advances in Plants & Agriculture Research, 3(1).