Variable Rate Technology and Its Application in Precision Agriculture

In a field comprising of different or a particular species of crop, there may be noticed variability in the fertilizer need, irrigation requirement, seed rate, chemical requirement (pesticides, herbicides, weedicides), etc. However, it is a difficult task to detect the individual requirements of a crop by mere observation; therefore, precision agriculture puts forth a technology known as variable rate technology (VRT) that helps in easy and accurate detection of individual requirements of each crop thereby reducing human errors. VRT refers to a technology that enables variable rate application of materials in precision agriculture. It is the ability to adapt parameters on machine to apply, for instance, seed or fertilizer, according to the exact variation in plant growth or soil nutrients and type. The chapter looks into working of different variable rate technologies and their application in precision agriculture.

The main aim of this publication is to discuss the concept of variable rate technology (VRT), and its components associated with variable rate application of water, fertil­izer, and other agricultural inputs. Fertilizer application is influenced by soil parameters as well as geographical variation in the field. The selection, rate, and distribution of nutrients at the ideal distance from the crop and soil depth determine nutrient management. Depending on the soil's characteristics and the geographical variation in the field or plants, variable rate technology (VRT) enables the application of inputs at a certain rate, time, and location. There are two methods for putting VRT into practice: map-based and sensor-based. The sensor-based strategy, which employs appropriate sensors to evaluate soil and crop properties while on the go, calculates the quantity of nutrients needed per unit area/plant, and uses microcontrollers that apply appropriate algorithms to regulate fertilizer flow with the necessary amount of nutrients. In map-based approach; Grid sampling and soil analysis are used to create a prescription map. According to the soil and crop conditions, the microcontroller regulates the desired application rate. The sensor-based VRT system includes a fertilizer tank, sensors, GPS, microcontroller, actuators, and other components, whereas the map-based system does not require an on-the-go sensor. Both approaches of VRT for fertilizer application in orchards and field crops are reviewed in this paper.

Precision agriculture (PA) represents a transformative approach to farming, employing advanced technologies to enhance productivity, efficiency, and sustainability. This review article provides an in depth analysis of the latest innovations in PA techniques, their diverse applications, and future directions. Precision agriculture is revolutionizing the agricultural landscape by integrating sophisticated tools such as GPS, remote sensing, Internet of Things (IoT), and big data analytics. These technologies enable farmers to monitor and manage variability in crop production meticulously, optimize the use of inputs, and enhance overall farm management practices. The key innovations in PA include the development and application of Geographic Information Systems (GIS) and Global Positioning Systems (GPS), which facilitate accurate mapping and variable rate technology (VRT) for site specific input management. Remote sensing technologies, encompassing both satellite imagery and UAVs (unmanned aerial vehicles), provide critical insights into crop health, soil conditions, and weather patterns, allowing for proactive and informed decision making. The integration of IoT in agriculture involves deploying sensors and connected devices to monitor soil moisture, temperature, and other environmental parameters in real time. This integration supports precision irrigation, climate monitoring, and efficient resource utilization. Big data analytics further enhances PA by processing vast amounts of data to generate actionable insights, enabling predictive analytics and decision support systems (DSS) that aid in optimizing farming operations. The article explores the applications of these advanced techniques in crop management, resource use optimization, and environmental stewardship. Examples include variable rate application of fertilizers, precision irrigation systems, and automated machinery such as drones and robotic harvesters. These innovations lead to significant improvements in crop yields, resource efficiency, and sustainability. Moreover, the review addresses the challenges associated with the adoption and implementation of PA technologies. These challenges include data management complexities, high initial costs, limited accessibility, and the need for technical expertise. The article discusses potential solutions such as cloud computing, machine learning algorithms, government subsidies, collaborative models, and comprehensive training programs to mitigate these barriers, the review highlights the integration of advanced technologies like artificial intelligence (AI), blockchain, and enhanced connectivity through 5G networks as pivotal developments that will further revolutionize precision agriculture. AI and machine learning will enhance predictive modeling and automated decision making, while blockchain will ensure transparency and traceability in supply chains. Enhanced connectivity will facilitate real time monitoring and collaborative platforms, driving efficiency and innovation in farming practices.

A novel control system for variable rate fertilizer application based on CAN bus is the integrated application of GPS, RS, GIS and automatic control important fields in research of agriculture science. Precision Agriculture Huang, W., Meng, Z., Chen, L. and Zhao, C., 2008, in IFIP International Federation for Information Processing, Volume 259; Computer and Computing Technologies in Agriculture, Vol. 2; Daoliang Li; (Boston: Springer), pp. 1317–1320. effectiveness (Wang 1995). As an important part of precision agriculture technology, much attention has been paid to the variable rate technology. Variable rate application has the potential to improve fertilizer utilization efficiency, increase economic returns and reduce environmental impacts. Today, developed countries have used variable rate technology widely, but it is still in the starting stage in China. The rapid development of precision agriculture has increased the need for a standardized electronics communications protocol also (Marvin L. Stone et al., 2004). In this paper, we present a novel system based on CAN bus and provide evaluation experiments for the variable rate fertilizer application. The system structure consisting of the electro-hydraulic proportional valve control system, ground speed sampling ECU and light-bar guidance ECU is presented in this paper. The design of variable rate controller based on DSP and the closed loop control circuit based on proportional valve is discussed in details. Experimental results indicate that the control system can be used for variable rate fertilizer application based on CAN bus. Moreover, the system is open, easy to expand and communicate with other CAN bus based components. It also provides basis for other variable rate applications using CAN bus in precision agriculture. 2. MATIERIALS AND METHOD

In Iran, the experimental plots under fertilizer trials are managed in such a way that the whole plot area uniformly receives agricultural inputs. This could lead to biased research results and hence to suppressing of the efforts made by the researchers. This research was conducted in a selected site belonging to the Gonbad Agricultural Research Station, located in the semiarid region, northeastern Iran. The aim was to characterize the short-range spatial variability of the inherent and management-depended soil properties and to determine if this variation is large and can be managed at practical scales. The soils were sampled using a grid 55 m apart. In total, 100 composite soil samples were collected from topsoil (0-30 cm) and were analyzed for calcium carbonate equivalent, organic carbon, clay, available phosphorus, available potassium, iron, copper, zinc and manganese. Descriptive statistics were applied to check data trends. Geostatistical analysis was applied to variography, model fitting and contour mapping. Sampling at 55 m made it possible to split the area of the selected experimental plot into relatively uniform areas that allow application of agricultural inputs with variable rates. Keywords: Short-range soil variability, Within-field soil variability, Interpolation, Precision agriculture, Geostatistics

Keywords: EQUATION Note: Available for download at: http://soil.scijournals.org/cgi/reprint/65/3/957 Reference ECOL-ARTICLE-2001-002doi:10.2136/sssaj2001.653957x URL: http://soil.scijournals.org/ Record created on 2005-12-09, modified on 2016-08-08

An approach for precision farming under pivot irrigation system using remote sensing and GIS techniques

<b><sc>Abstract.</sc></b> The results from two surveys carried out in Florida, 2019, and Missouri, 2021 were used to investigate whether the crop types had an impact of the willingness to utilize technology on the farms. The surveys focused on the status of using Precision Agriculture (PA) in both states, including PA practices, equipment and software, Internet and e-mail use, information sources for PA, satisfaction level from service, providers, data handling, interpretation, storage, and ownership, the value of data for decision making, changes in management practices, desired information and services, and the next planned step in the practice of PA. The survey results showed more similarities in the main reason NOT to use PA between the two crop types but the present application of using technology in specialty crops is generally five times larger than in row crops. GPS receiver applications were reported similar for both types of crops. Lack of knowledge and high cost of data handling were cited as the main problems. The most significant difference was among using variable rate technology which was 43% for specialty crops while was reported 0% for row crops. Pest scouting and mapping were commonly used for specialty crops while they were rarely applied for row crops. Survey respondents found yield mapping, soil sampling map and irrigation scheduling were more valuable for specialty crops than row crops in management decisions. About 50% of the respondents would like to share the PA data in both types of crops. Almost 50 % of respondents got their PA information from retailers in both categories and as the second source, using extension agents were more common in specialty crops that row crops.

SUMMARY: Adapting variable rate technology (VRT) to Argentine conditions requires methods that use inexpensive information and that focus on the inputs and variability common to Argentine maize and soybean growing areas. The goal of this study is to determine if spatial regression analysis of yield monitor data can be used to estimate the site-specific crop Nitrogen (N) response needed to fine tune variable rate fertilizer strategies. N has been chosen as the focus of this study because it is the most commonly used fertilizer by corn farmers in Argentina. The methodology uses yield monitor data from on-farm trials to estimate site-specific crop response functions. The design involves a strip trial with a uniform N rate along the strip and a randomized complete block design, with regression estimation of N response curves by landscape position. Spatial autocorrelation and spatial heterogeneity are taken into account using a spatial error model and a groupwise heteroskedasticity model. A partial budget is used to calculate uniform rate and VRT returns. First year data indicate that N response differs significantly by landscape position, and that VRA for N may be modestly profitable on some locations depending on the VRT fee level, compared to a uniform rate of urea of 80kg ha -1 . A more complete analysis will pool data over many farms and several years to determine if reliable differences exist in N response by landscape position or other type of management zone. The study is planned for four years. The purpose of this preliminary analysis is to show how spatial regression analysis of yield data could be used to fine tune input use.

Precision agriculture, a technology-driven approach to farming, integrates GPS, IoT sensors, Variable Rate Technology (VRT), and data analytics to optimize crop yield and resource usage. This study explores the effectiveness of precision agriculture in enhancing productivity and promoting sustainable farming practices by analysing its impact on crop yield, water and fertilizer usage, and environmental metrics. Data was collected through IoT sensors, GPS mapping, and drone-based remote sensing to monitor field conditions, while VRT was used to apply inputs precisely where needed. Comparative analyses between precision and traditional agriculture show a 20% increase in crop yield and a 40% reduction in water and fertilizer usage for fields employing precision techniques. Environmental benefits were also notable, with significant decreases in greenhouse gas emissions and pesticide runoff. Case studies across diverse farming setups and controlled experiments provided further insights into the practical applications and challenges of precision agriculture. While results indicate substantial improvements in efficiency and sustainability, barriers such as high initial costs and technical expertise requirements remain obstacles for broader adoption, particularly among small-scale farmers. Addressing these challenges will require collaborative efforts from policymakers, agricultural organizations, and technology providers to develop accessible and cost-effective solutions. This study concludes that precision agriculture offers a promising path to sustainable, high-yield farming by reducing resource consumption and minimizing environmental impact. However, increased focus on overcoming adoption barriers is essential to make precision agriculture feasible for a wider range of farmers. Further research should continue to optimize these technologies, making them scalable and adaptable to various agricultural contexts worldwide.

Precision agriculture, a technology-driven approach to farming, integrates GPS, IoT sensors, Variable Rate Technology (VRT), and data analytics to optimize crop yield and resource usage. This study explores the effectiveness of precision agriculture in enhancing productivity and promoting sustainable farming practices by analysing its impact on crop yield, water and fertilizer usage, and environmental metrics. Data was collected through IoT sensors, GPS mapping, and drone-based remote sensing to monitor field conditions, while VRT was used to apply inputs precisely where needed. Comparative analyses between precision and traditional agriculture show a 20% increase in crop yield and a 40% reduction in water and fertilizer usage for fields employing precision techniques. Environmental benefits were also notable, with significant decreases in greenhouse gas emissions and pesticide runoff. Case studies across diverse farming setups and controlled experiments provided further insights into the practical applications and challenges of precision agriculture. While results indicate substantial improvements in efficiency and sustainability, barriers such as high initial costs and technical expertise requirements remain obstacles for broader adoption, particularly among small-scale farmers. Addressing these challenges will require collaborative efforts from policymakers, agricultural organizations, and technology providers to develop accessible and cost-effective solutions. This study concludes that precision agriculture offers a promising path to sustainable, high-yield farming by reducing resource consumption and minimizing environmental impact. However, increased focus on overcoming adoption barriers is essential to make precision agriculture feasible for a wider range of farmers. Further research should continue to optimize these technologies, making them scalable and adaptable to various agricultural contexts worldwide.

CONTEXTReducing N surplus from agriculture without compromising yield and quality requires economically and ecologically viable solutions. OBJECTIVEBased on field data, we investigated a technical and market-based solution to balance the economic and environmental performance of nitrogen (N) fertilizer application in winter wheat in Switzerland. METHODSThe technical solution, i.e. variable rate (VR) technology, was compared to the standard uniform fertilizer application (ST) in terms of revenues and N balance over seven site-years between 2018 and 2020. The potential of a market-based solution to align revenues and N surplus was investigated based on the relationship between two indicators: the economic optimum (EO) of the revenues and the balanced N supply (BNS). The EO was estimated using a production function approach. The BNS was empirically defined as the point at which the N surplus estimated from total N input (N fertilizer + soil N supply) reaches a limit value of 30 kg N ha−1. RESULTS AND CONCLUSIONSOn average, the revenues of VR were about 4% higher than in ST. The N surplus was, on average, 32% (21 kg N ha−1) lower in VR compared with ST due to a 13% reduction in N inputs with no significant differences in yield. Despite the differences across years and fields, VR appeared to be reducing N surplus without losses in revenues in 5 out of 7 site-years. The revenue curve reached an EO at total N input of 205, 249 and 246 kg N ha−1, in the years 2018, 2019, and 2020, respectively. The BNS was calculated at 220, 195, and 178 kg N ha−1 N inputs for the years 2018, 2019, and 2020, respectively. The results show that a price increase of up to 5.4 times the current fertilizer price through taxes would be necessary in order to reduce the N surplus to an environmentally friendly level. Such an increase would hardly be politically feasible. SIGNIFICANCEThe reported data showed that VR technology appears as a viable solution for producing lower N surplus at comparable revenue levels, thereby making it an option for small- to medium-scale winter wheat production in Switzerland. The environmental benefit could encourage the financial support of technologies for precise N management, which are often too expensive for these systems. Future research should verify or extend the numeric values found in this study.

A review of life cycle impacts and costs of precision agriculture for cultivation of field crops

Real-time sensor based variable rate technology (VRT) equipment is complex with many different components working together to achieve a desired output. The equipment provides rate- controller set-point every second creating more challenges in terms of functioning of each component and its response time to achieve a variable rate application with existing rate-controllers, valves and nozzles. All components involved operate on inputs from the other thereby inducing a time delay. This time delay among components questions the credibility on performance of sensor based commercially available VRT equipment and its overall response time. Response time of commercially available real-time sensor based VRT system was evaluated with two applicator configurations: Applicator equipped with Capstan PWM technology with fixed orifice nozzles and an applicator equipped with Raven FC-Valve with variable orifice nozzles. Parameters like pressure, flow rate, controller input from the sensor system were measured and logged using a data acquisition system. The data were analyzed to determine if the applied rate correctly follows the desired set-point rates or if there is any delay in overall response time of VRT equipment for different settings of the rate-controller. Results showed that rate-controller settings were different for both applicator configurations for achieving minimum response time of around 0.5 s. This work will aid in determining the spatial resolution for variable rate application using commercially VRT equipment.

Precision agriculture is a rapidly advancing field that combines advanced technologies and data analytics to improve crop productivity and resource management. This article reviews the advancements, technologies, and applications in precision agriculture. It explores the role of sensing technologies, such as remote sensing, ground-based sensors, and GPS/GIS applications, in data collection and analysis for informed decision-making. The article also examines the impact of variable rate technologies, including variable rate seeding, nutrient application, and irrigation, on optimizing input usage and improving crop performance. Furthermore, it discusses the integration of precision crop management techniques, such as remote sensing, artificial intelligence, and the Internet of Things (IoT), in enhancing farming practices. While precision agriculture offers significant benefits, challenges related to cost, accessibility, data management, and education need to be addressed. It is crucial to overcome these challenges to fully harness the potential of precision agriculture for sustainable and efficient food production. By acknowledging and addressing these challenges, farmers and stakeholders can work towards the widespread adoption of precision agriculture, leading to improved crop productivity, resource management, and environmental sustainability.