Precision Agriculture- Harnessing technology to boost productivity and profitability

The world’s population is projected to reach 9 billion by 2050, states the World Bank in its article on Climate Smart Agriculture, and we will have to produce about 70% more food to meet its needs. Producing food sustainably for a rapidly growing population is one of the greatest challenges facing humanity today. Diminishing profits and productivity and the environmental impact of traditional methods of agriculture have accentuated the challenge.

Precision Agriculture leverages advanced digital technologies and will play a significant role in the third modern farming revolution. It effectively minimizes inputs, labor, and time sustainably, maximizes productivity and profitability, ensures sustainability, and reduces environmental impact. The precision agriculture market is estimated to touch $15.6 billion by 2030.

Precision Agriculture is a farm management concept that revolves around the process of observing, measuring, and responding to various inter-and intra-field variability inputs for modern agriculture.

Popular definitions of Precision Agriculture (PA) or Site-Specific Crop Management (SSCM) describe the term as a technology-enabled approach to farming management that observes, measures, and analyzes the needs of individual fields and crops.

The goal of precision agriculture is to increase efficiency and productivity, reduce input costs, and improve environmental sustainability.

A brief history of Precision Agriculture

Precision agriculture can be traced back to the 1950s and 60s when the first GPS (global positioning system) satellites were launched.

However, it wasn't until the 1980s and 90s that precision ag technologies began to be widely adopted by farmers. This was due to the development of more affordable and user-friendly GPS receivers and mapping software, as well as the availability of satellite imagery and other remote sensing data.

Since then, the field of precision agriculture has continued to evolve and expand, with the introduction of new technologies such as sensors, drones, and autonomous vehicles, as well as advances in data management and analysis tools. Today, precision agriculture is being used by farmers around the world to improve the efficiency, productivity, and sustainability of their operations.

The world is at the edge of the third modern farming revolution and precision farming is an important part of it. The first farming revolution that occurred from 1900 to the 1930s, mechanized agriculture leaving each farmer able to produce enough for 26 people. Long after that, it was the 1990s when the second revolution known as the Green revolution took place. Due to scientific progression genetically modified newer sets of crops that are pest resistant and need less water were introduced, leaving each farmer able to feed 155 people.

The global population is expected to reach 9.6 billion by 2050 and food production must be double the current levels to feed every person. Advanced analytical capabilities and constantly improving IoT capabilities, like precision agriculture drones, will be key elements in the third revolution.

But how is Precision Agriculture different from what farmers and agri-businesses have already been doing?

Precision Agriculture and traditional cultivation differ vastly in practice. In traditional agriculture, farmers apply the same amount of pesticides, fertilizers, and irrigation across fields, at prescribed times and frequencies, as per the general recommendations for the region.

However, there are always differences in biological, physical, and chemical parameters even within a single field. Uniform treatment of fields without considering inherent differences results in the overuse of inputs in fertile land and underuse in poor patches. This inefficient use of land, water, fuel, fertilizers, and pesticides increases cost and environmental impact.

Precision agriculture targets the intrinsic differences in farmland and optimizes input by Variable Rate Application (VRA). To implement VRA, detailed spatial data must be collected across fields and locations through geographic information systems (GIS) and crop lifecycles using GPS and remote sensing. The precision farming tools used include precision irrigation systems, yield mapping & monitoring tools, and information management systems. Using advanced descriptive, predictive, and prescriptive analytics engines to analyze the data, precision agriculture arrives at data-driven management decisions to implement cost-effective, environment-friendly sustainable modern farming solutions.

Looking to solve complex farm management challenges with Precision Agriculture?

Farmers get guidance on the most ideally suited seed varieties, soil management with the amount of fertilizer to be used, the precise quantum of water required depending on the moisture in the soil, the right use of pesticides post examination of the crop condition, etc. Monitoring and data collection are done throughout a crop’s lifecycle to enhance all stages of agricultural production.

Using emerging technologies like the internet of things (IoT)-enabled devices such as smart agricultural sensors and robotic drones, satellite imagery, and GPS-enabled instruments, real-time data are collected on soil, crop, hyper-local weather predictions, equipment available, and other variables. This is supplemented by inputs from IoT and AI/ML-driven predictive analytics software.

Read more- Digitizing Kharif Crop Production with Precision Agriculture

According to McKinsey, the invention and adoption of Precision Agriculture is shaped by the following trends-

Big Data and Advanced Analytic Capabilities, and Robotics — aerial imagery, sensors, and sophisticated local weather forecasts. In simple words farming that collects and uses data from plots for managing and optimizing the production of crops is known as predictive farming.

Predictive farming is analogous to taking a pill to target an ailment. The solutions are highly tailored from the type of crop suitable for a plot to the use of pesticides in targeted regions only. Adopting precision agriculture and modern farming reduces production costs and wastage, as the tailored needs of each plot are catered to.

Precision agriculture is practiced by adopting crop management software and technical equipment. Rigorous data collection is done on soil testing, plot measurement, weather pattern analysis, and crop analysis through sensor-equipped devices placed on the fields. The data is calibrated to devise conclusions, and based on those results, a detailed and precise set of practices can be adopted.

Examples of Precision Farming include the adoption of the same set of practices that use smart farming technologies to cater to the needs of individual plots and crops. Big data analytics software such as Cropin Grow (SmartFarm) or robots such as drones can get detailed information on the plot, soil type, suitable crops, irrigation, and fertilizer needs. The information obtained is used to tailor a very unerring selection of crops, fertilizer quantity, and watering needs. Precision agriculture helps farmers live a debt-free life as production costs and losses are reduced and overall environmental impact is also minimized.

By using precision ag technologies and practices, farmers can target their inputs (such as seeds, fertilizers, and pesticides) to specific areas of the field that need them the most, rather than applying them uniformly across the entire field.

This targeted approach can help farmers save on inputs, as well as increase crop yield and quality. In addition, precision ag technologies can help farmers monitor and manage their crops more effectively, allowing them to respond to potential problems (such as pests or diseases) more quickly and effectively.

Farmers can leverage sensors and mapping tools to precisely apply fertilizers at the right rate and location. In addition, precision ag technologies can help farmers optimize their irrigation practices, which can save water and energy.

Since details of areas in a single farm can be traced, precision agriculture benefits farmers in several ways.

Discussed below are some of its key advantages:

• A refined set of cultivation practices and choice of crops based on the suitability of land
• Elimination of volatility and risk
• Waste management
• Reduced production costs
• Minimum environmental impact
• Optimized use of fertilizers
• Water management with optimized irrigation practices
• Improved soil health

In developing economies, 32% of food losses occur during food production as analyzed by McKinsey on FAO data.

Conventional farming practices are area-centric. There is a general set of crops cultivated throughout an area. All the farmers in that area follow the same procedures with respect to the sowing, nourishing, irrigation, and harvesting period. These practices result in unpredictability, overuse of resources, and uncontrolled waste production.

Before the use of technology in agriculture, a farmer’s probability of yielding good produce was as good as tossing a coin and wishing for heads. Since farmers had no information on their farms, there was no way of learning the cause of crop loss. This practice pushed the farmers towards losses and debt. Advancements in big data analytics, IoT, and accessible satellite imagery created optimism for the agriculture sector, giving rise to digital farming, thereby combating the issue of unpredictability.

Anyone looking for a precision agriculture solution should be knowledgeable about the tools and technologies available. Computer-based applications are used to create precise farm plans, field maps, crop scouting, yield maps, and to define the exact amount of inputs to be applied to fields. One benefit of this method is the ability to design a friendly agricultural strategy, which helps to lower costs and boost yields. But, because the obtained data cannot be integrated into other supporting systems, these applications offer limited-value data that cannot be used for large-scale precision farming solutions. Let us dive into the tools and technologies needed for precision agriculture.

1) Global Positioning System (GPS) for Precision ag

The Global Positioning System (GPS) is a satellite-based navigation system that provides location and time information in all weather conditions, anywhere on or near the Earth. In precision agriculture, GPS is used to provide precise location information for mapping and navigation purposes.

One of the main ways GPS is used in precision agriculture is for mapping and field-level data collection. Farmers can use GPS receivers mounted on their vehicles or equipment to collect data on the location, shape, and size of their fields, as well as the location of specific features within the fields such as irrigation systems, drainage ditches, and fence lines. This data can be used to create detailed maps of the fields, which can be used for planning and scheduling field operations, implementation of variable rate technology in agricultural input management, and analyzing field performance.

Farmers can also use GPS-based guidance systems to help them navigate their vehicles and equipment around the fields with a high degree of accuracy, reducing the risk of errors and increasing efficiency.

2) Geographic Information System (GIS)

A geographical information system (GIS) consists of a computer software database system used to input, store, retrieve, analyze and display, in map-like form, referenced geographical information. 

In precision agriculture, GIS is used to create, store, and analyze spatial data related to farming operations, such as field boundaries, topography, soil types, crop types, and other features. Farmers can use GIS to identify the most suitable locations for planting certain crops or to identify areas that are most vulnerable to pests or diseases.

3) Grid Sampling for targeted farming

Grid sampling is a technique for segmenting fields into around 0.5–5 ha-sized units. Those grids' soil samples will be used to calculate the proper application rates for crop inputs. The Grid Samples are then collected, combined, and delivered to the lab for evaluation.

Grid sampling can be used to collect soil samples from different parts of a field, in order to understand the spatial variability of soil characteristics such as pH, nutrient content, and organic matter. This information can be used to make informed decisions about fertilization, irrigation, etc.

4) Variable-Rate Technology (VRT)

Variable-rate technology (VRT) consists of farm field equipment with the ability to precisely control the rate of application of crop inputs that can be varied in their application including tillage, insect control, fertilizer, plant population, and irrigation.

VRT is based on the idea that different parts of a field may have different soil types, topography, crop characteristics, and other factors that may affect their requirements for inputs. By using VRT, farmers can optimize their resource use and improve their efficiency and productivity.

The three key ways in which Variable-Rate Technology is used in Precision Agriculture are- 

a. Fertilizers

VRT is used to apply fertilizers at different rates across a field, depending on the soil nutrient levels and crop requirements of each area. This can help farmers save on fertilizers and improve crop yields and quality.

b. Pesticides

VRT is used to apply pesticides depending on the prevalence and distribution of pests and diseases across the field. This helps farmers control the amount of pesticides and reduce the risk of environmental pollution.

c. Seeding

VRT is used to sow seeds depending on the soil characteristics and crop requirements of each area. This helps farmers optimize crop yields and quality.

5) Yield Monitors for Precision Agriculture

A yield monitor is a device that is used in precision agriculture to measure and record crop yields as they are harvested. Yield monitors typically consist of sensors that measure the flow of grain or other crops as they pass through the combine harvester, and a computer or other data recording device that records the yield data.

Yield monitors can be used to create detailed maps of crop yields across a field, showing the spatial variability of yields and identifying areas that are performing poorly or well.

Yield monitors can be used to measure and record the quality of harvested crops, such as moisture content and test weight. This information can be used to optimize storage and handling practices and to improve the overall quality of the crop.

6) Yield Maps

Data from an adapted combine harvester that has a GPS integrated with a yield tracking system is processed to create yield maps. A yield map is a visual representation of crop yields across a field, showing the spatial variability of yields and identifying areas that are performing poorly or well. In addition to yield monitors, Yield maps can also be created using other data collection tools, such as GPS or remote sensing technologies.

Yield maps can help farmers optimize their resource management practices by identifying areas of the field that have different resource needs. They can also be used to support planning and decision-making by providing a visual and spatial context for data analysis.

7) Remote Sensors for precise data capture

Remote sensors are generally categorized as aerial or satellite sensors. Changes in soil type, crop development, field boundaries, roads, water, etc., can indicate differences in field color. Remote science in agricultural terms means viewing crops from overhead (from a satellite or low-flying aircraft) without coming into contact, recording what is viewed and displaying the image, and providing the map to pinpoint the field problems earlier and more effectively. 

8) Auto-Guidance Systems for Precision Agriculture

Auto-guidance systems are precision agriculture technologies that use GPS and other sensors to help farmers navigate their vehicles and equipment around the fields with a high degree of accuracy.

These systems typically consist of a GPS receiver, a display unit, and sometimes other sensors (such as cameras or lasers) that provide additional information about the field and the vehicle's position within it.

Auto-guidance systems can be used in a variety of ways in precision agriculture

a. Navigation

Auto-guidance systems can help farmers navigate their vehicles and equipment around the fields with a high degree of accuracy, reducing the risk of errors.

b. Row Guidance

Auto-guidance systems can help farmers keep their vehicles and equipment on the correct rows, reducing overlap and skips.

c. Equipment Guidance

Auto-guidance systems can help farmers control and monitor the position and orientation of agricultural equipment such as planters, sprayers, and harvesters with a high degree of accuracy, improving implement performance and reducing errors.

d. Record keeping and Data Analysis

Auto-guidance systems can record data on the location and movements of vehicles and equipment, which can be useful for record-keeping and analysis.

9) Proximate Sensors for precise crop and soil data capture

Proximate sensors can be used to measure soil (N and pH) and crop properties as the tractor pass over the field.

Proximate sensors are sensors that are used in precision agriculture to measure the properties of crops or soil in close proximity to the sensor. The soil sample is scooped and pressed against an electrode, a stabilization period of about 10-15 seconds is allowed, and the reading is taken. These sensors can either be hand-held or mounted on vehicles or other Agri equipment. Proximate sensors are typically used to collect data on a real-time or near-real-time basis, allowing farmers to respond to changing conditions and make adjustments as needed. 

Proximate sensors can measure

a. Crop characteristics

Proximate sensors are used to measure crop characteristics such as height, biomass, and leaf area index. This information can be used to optimize crop management practices and improve crop yields.

b. Soil characteristics

Proximate sensors are used to measure soil characteristics such as pH, nutrient content, nitrogen, and moisture content. This information can be used to optimize fertilization, irrigation, and other resource management practices.

c. Pests and diseases

Proximate sensors are used to monitor the prevalence and distribution of pests and diseases in a field, in order to optimize control measures and reduce the risk of outbreaks.

10) Computer Hardware and Software

Computer support is required to analyze the data gathered by other components of precision farming technology and to make it accessible in formats such as maps, graphs, charts, or reports.

a. Data storage and management systems

Precision agriculture systems often generate large amounts of data, which needs to be stored and managed in a structured and efficient manner. This can be done using a variety of data storage and management systems, such as databases and cloud-based platforms such as Cropin Cloud.

b. Data analysis and visualization tools

Precision agriculture systems often require the use of specialized software tools to analyze and visualize data in order to extract insights and make informed decisions.

An economical, scalable, and accurate precision agriculture solution is cloud-based SaaS (Software as a Service). SaaS solutions used in agriculture technology focus on providing advanced farming technologies that help farmers, agribusinesses, and other stakeholders to make smart decisions based on the analysis of data.

Cropin is at the forefront of making agriculture smarter with the use of satellite imagery, weather analysis, and machine learning for monitoring, detection, analysis, and prediction. Cropin’s intelligent agriculture cloud can be integrated with already installed software and sensors through APIs. The data gathered on soil or moisture levels, temperature changes, or crops can be processed using the precision farming solution that ties together the capabilities of big data analytics and machine learning algorithms to provide actionable insights based on the accuracy of collected data.

Data management and analysis are crucial aspects of precision agriculture.

Data collection and storage

In precision agriculture, data is collected from a variety of sources, including GPS and mapping systems, sensors, remote sensing technologies (such as satellite imagery), and other farm management systems. This data is typically stored in a central location, such as a computer or cloud-based platform, where it can be accessed and analyzed.

Data analysis and visualization

Once agricultural data has been collected and stored, it needs to be analyzed in order to extract insights and make informed decisions. This can be done using a variety of data analysis and visualization tools, such as spreadsheets, statistical software, and specialized precision ag software. These tools can help farmers identify patterns and trends in their data, and visualize the results in a way that is easy to understand and interpret.

Integration with other farm management systems

Precision agriculture systems often need to be integrated with other farm management systems, such as financial management, inventory management, and labor management systems. This can help farmers get a more complete picture of their operations. In addition, integration with other systems can help streamline data management and analysis, reducing the time and effort required to process and interpret data.

The recent rapid digitalization has reduced the exhaustive paperwork in banks, hospitals, and most private and public sector organizations seem to diminish as their businesses move online. Digitization has reduced the manual work - which was time-consuming, error-prone, and inefficient - thus saving millions for corporations. Digitization of the economy has broken the barriers and has successfully curtailed the fear of tech dependency, especially among the farming community. Digitalization is slowly also revolutionizing the vast and complex Agriculture sector.

The United Nations projects that by the year 2050 the population of the world will be 9.7 billion. With the relevance of over 60% of the world population relying on agriculture for food, the pressure to increase crop production to meet demands doesn’t seem to ease. Coupled with climate change, which is leading to the rise in global temperatures, levels of greenhouse gases, and frequency of extreme weather events like droughts and floods, along with increasing labor costs, high production costs, and unpredictability, poses a major challenge to the future of agriculture. Hence, the goal is to increase productivity in a sustainable way with climate-smart agriculture.

Agri-businesses are highly vulnerable to climate change. Farming is becoming increasingly challenging in many areas due to decreased yields and increased frequency of extreme weather events like drought or flood. Climate change is expected to diminish global agricultural production by 17 percent by 2050, according to a study by the National Academy of Science (NAS).

Precision farming, according to NAS, should play a big role in making farming more sustainable, without compromising production or farmer earnings. With precision agriculture, the World Economic Forum believes world food production could rise 10-15 percent by 2030, and greenhouse gas emissions and water consumption would decrease by 10 to 20 percent, respectively, if 15 to 25 percent of farms adopted the technology.

Site-specific field management and variable-rate application (VRA) technologies are two of the most commonly discussed aspects of precision agriculture. These measures are essential to reducing the environmental effect of agriculture.

Precision farming and modern agriculture practices focus on reducing production cost and wastage, as it is tailored to the needs of each plot. It centers on data collection and analysis of farm pIots which comprises sensors, drones, and robots for recording the data, and software as a service (SaaS) can be used to adapt to precision farming systems.

Role of Artificial Intelligence and Machine Learning in Precision Agriculture

Although IoT is still at a nascent stage, the governments of agriculture-dominant economies do invest in other cutting-edge technologies like artificial intelligence and machine learning (AI/ML) to make smarter agriculture decisions. In countries like India, the implementation of IoT in agriculture has its own set of unique benefits and challenges.

Firstly, the farmers fear upgrading to Agri tech as they lack knowledge about the applicability of the technology in agriculture. Besides this, the sensors, robots, and drones are expensive, have high maintenance costs, and require technically trained labor to operate. Further, the captured data needs to be analyzed in a lab or using specialized instruments on the farm. Also, a variety of sensors are required for collecting data on different parameters, which need to be analyzed separately, hence driving operational costs higher. Therefore, the digital farming solution must be cost-effective and highly scalable, considering the various sizes of farms.

Using precision agriculture, farmers can achieve the following:

• Identify the best crops and hybrid seeds suitable for a particular area
• Work only on exactly identified areas to be replanted
• Take specific actions to provide the essential and optimum level of inputs (fertilizers and chemicals)
• Save time and cost and minimize the environmental impact of polluting soil and water
• Create maps for irrigation schedules and use ideal amounts of water to curtail wastage
• Anticipate pest infestations and diseases and take preventative measures before they destroy crops
• Apply weedicides and pesticides without harming biodiversity and killing non-target plants
• Harvest produce when they mature to meet consumer preferences and early enough to extend storage time

The global precision farming market is estimated to reach $12.84 billion by 2026. Both big and small farmers and organizations working with growers benefit by adopting precision and digital farming technologies, which help to optimize agri-input resources without adding costs or workload. With climate change impacting farming in a big way and unpredictable natural disasters such as floods and drought becoming regular events, and the world population growing unabated, precision agriculture is here to stay.

The data obtained from precision agriculture can be used to provide better information for managing all activities on the farm to improve productivity.

Cropin, a pioneer in the Agritech space, built the first global Intelligent Agriculture Cloud. Cropin’s platform offers actionable insights for precision agriculture, which aids decision-making to increase efficiency, scale productivity, and strengthen sustainability.