The global precision agriculture market size was valued at USD 5.49 billion in 2021 and is projected to reach USD 19.24 billion by 2030 at a CAGR of 14.95% from 2022 to 2030.
Precision agriculture is also known as satellite agriculture, precision ag or precision farming, on-demand agriculture, and site-specific crop management. The site-specific crop management is an agricultural management theory based on measuring, responding to, and observing plant and intra-field crop variability.
Using the most recent technologies and research methodologies, precision agriculture is utilized to transform agricultural extension in developing nations. Precision agriculture aims to ensure the sustainability, protection, and profitability of the environment.
Precision agriculture is dependent on specialized systems, software, and IT services. Accessing real-time data about the conditions of the crops, soil, and ambient air, as well as other pertinent data such as hyperlocal weather forecasts, labor costs, and equipment availability, is a component of this strategy. The data is used by software for predictive analytics to provide farmers with advice regarding crop rotation, optimal planting times, harvesting times, and soil management. In fields, sensors measure the moisture content and temperature of the soil and surrounding air. Farmers have access to real-time images of individual plants thanks to satellites and robotic drones. These images can be processed and integrated with sensors and other data to generate guidance for immediate and future decisions, such as exactly which fields to water and when or where to plant a specific crop.
|Market Size||USD 19.24 billion by 2030|
|Fastest Growing Market||Asia Pacific|
|Largest Market||North America|
|Report Coverage||Revenue Forecast, Competitive Landscape, Growth Factors, Environment & Regulatory Landscape and Trends|
Precision farming is a method of farming that utilizes the internet of things (IoT), software, artificial intelligence, big data, and other technologies to optimize the use of farm inputs in order to reduce costs and increase productivity. According to a study conducted by MDPI, Basel, Switzerland, the use of pesticides and fuel decreased significantly after precision farming was adopted. Although there would be substantial fixed costs during the adoption phase, variable costs would decrease significantly. The labor cost decreases by approximately 20% and the farm's long-term productivity increases. Precision agriculture is assisting the world in addressing the challenges posed by climate change and a growing population by enhancing the productivity and profitability of crops without negatively impacting the environment.
Demand for smartphone integration in precision agriculture has increased due to the growing popularity of smartphones. Advanced applications compatible with all smartphones have been developed by businesses. With the integration of smartphones, farmers are able to monitor their fields from anywhere, and the data is backed up in the cloud. The farmer's needs are met by the fact that smartphones support Bluetooth, USB, and Wi-Fi connectivity. Consequently, the surge in smartphone adoption has created enormous opportunities for the global precision agriculture market. The improvement of agricultural facilities is made possible by technological progress. The development of monitoring protocols and systems for monitoring and managing farms and farm workers is one of the most significant changes in the use of mobile devices in agriculture. For example, in December 2020, a survey conducted by the leading news agency, "The Print," revealed that farmers in India have adopted advanced technology and that the use of smartphones in agricultural activities has enabled them to achieve nearly double their income and output of other farmers.
The high cost of precision farming equipment is one of the most significant factors inhibiting market expansion. The technologies and equipment used in precision agriculture, such as smart sensors, drones, VRT, GPS, GNSS, guidance tools, and receivers, are highly effective but costly. Additionally, skilled personnel is required for the installation and operation of precision farming equipment. As a result, growers in developing nations such as India, China, and Brazil, which have limited resources for agricultural practices, choose traditional farming over new technology-based farming due to the need for substantial capital investments. Moreover, Precision agriculture generates a large amount of data, including variable-rate seeding, yield monitoring, mapping, soil testing, and crop rotation history. This data is analyzed for decision-making, and the success of precision agriculture is dependent on its analysis. Therefore, it must be stored and managed. The greatest obstacle is managing this massive amount of data, which requires a high level of expertise, and many users lack the expertise to use this data to make decisions for their farms.
The use of AI-based applications and tools facilitates controlled and precise farming by providing farmers with the necessary information or guidance regarding the use of fertilizers, water management, crop rotation, pest control, type of crop to be grown based on soil, and nutrition management, and optimal planting. In farms, pests are controlled using AI-based tools. They use satellite imagery and artificial intelligence (AI) algorithms to compare it with historical data to determine whether insects have landed on the farm and what kind of insects they are. AI is also utilized in weather forecasting to assist farmers in deciding the type of crop to cultivate and monitoring the quality and nutrition level of the soil. Precision farming techniques based on AI assist farmers in monitoring the health of their crops, resulting in a high-quality harvest.
North America is the most dominant market for Precision Agriculture globally. Wide Area Augmentation System (WAAS), a GNSS-based solution, is the most popular technology in North America, with a market penetration of 66%. The market potential for satellite-based devices and equipment in North America is substantial. Increasing demand for real-time kinetic technology, fertilizer and sprayer controllers, robotics, variable rate irrigation, networks, and remote sensing technologies is primarily responsible for the market's expansion. US-based precision agriculture software companies include AgLeader, Agri-vision, Blue River Technologies, Crop Venture Incorporated, Farm Works, and Holland Scientifics.
The precision agriculture market share can be segmented on the basis of Offering, Technology, Application and Geography. On the basis of offerings, the growth of the Precision Agriculture market is divided into hardware, software, and services. The hardware segment is further subdivided into automation and control systems as well as monitoring and sensing devices. Due to their extensive use in precision farming, automation and control systems, such as GPS receivers, guidance and steering devices, and variable-rate technology (VRT) devices are anticipated to account for a sizable portion of the precision farming hardware market over the forecast period. High adoption of automation and control devices in the agriculture industry, such as drones/UAVs, GPS/GNSS, irrigation controllers, guidance and steering systems, yield monitors, and sensors, is responsible for the market's expansion.
Variable-rate technology is anticipated to experience the greatest CAGR growth over the forecast period compared to all other technologies. Farmers' early adoption of this technology is largely responsible for the market's expansion. Auto-guidance technology based on GPS enables growers to reduce the overlap of equipment and tractor passes, thereby saving fuel, labor, time, and soil compaction.
In terms of application, yield monitoring held the largest market share in the precision farming market. The growth of the market can be attributed to the advantages of climate service initiatives, which enable farmers to effectively respond to climate-related disasters and enhance food security and agricultural decision-making.