The global agriculture IoT market is estimated to grow from USD 11.4 billion in 2021 to USD 18.1 billion by 2026 at a CAGR of 9.8% during 2021–2026. The growth of the agriculture IoT market is driven by factors such as increasing adoption of the Internet of Things (IoT) and artificial intelligence (AI) by farmers and growers, growing focus on livestock monitoring and disease detection, high demand for fresh produce, population growth, loss of arable land, surging adoption of aquaculture monitoring and feed optimization devices in developing countries, and strong government support for precision farming practices.

The advent of advanced technologies such as guidance systems, variable rate technology, IoT, AI, and remote sensing has transformed the agriculture industry into a technologically intense and data-rich industry. Smart agriculture technologies assist in increasing profitability, improving sustainability, protecting the environment, and minimizing the consumption of resources such as water, fertilizers, and energy. IoT is implemented in various applications, including precision farming, livestock monitoring, precision aquaculture, smart greenhouse.

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Driver: Increased adoption of VRT, remote sensing, and guidance technologies by farmers worldwide

Precision farming/smart farming could ensure high returns for agribusiness if used to its complete potential; it can help farmers battle the negative effects of nature on crops by collecting geospatial data of soil, livestock, and plant, and other inter-and intra-field information. Precision farming also provides inputs about the requirements for irrigation, liquid fertilizers, nutrients, herbicides, and pesticides, thereby reducing the wastage of resources and the overall cost of farming. The use of advanced technologies such as remote sensing, variable rate technology (VRT), guidance technology, yield mapping software, GPS, and data management software in agricultural practices help enhance land fertility and profitability, facilitate sustainable agriculture, maximize productivity, reduce the cost of farming, and decrease labor overheads. GPS-based auto-guidance technology allows growers to reduce the overlapping of equipment and tractor passes, thereby saving fuel, labor, time, and soil compaction. Similarly, VRT allows input application rates to be varied across fields for site-specific management of the field variability. The growth rate of VRT is expected to be higher than that of other technologies because VRT helps in applying the right amount of input at the right place on the field, which minimizes the input waste and increases land and crop productivity.

Restraint: High upfront cost for deployment of modern agricultural equipment

A major factor restraining the growth of the agriculture IoT market is the requirement for high initial investments. Currently, agricultural IoT devices and tools are expensive, thereby making it unaffordable for smaller farmers in developed regions and most farmers in emerging economies. Apart from high initial monetary investment to deploy GPS, drones, and GIS, VRT, and satellite devices, precision farming also requires a skilled workforce to handle these technology-enabled devices. Precision livestock farming technologies include automated milking and feeding robots, monitoring and sensing devices, herd management software, which have a huge set-up cost associated with them. Farmers have to make huge investments in automation and control devices, distribution wagons, RFID or GPS-enabled livestock monitoring systems, and robotic equipment. These livestock monitoring products also have high installation and maintenance costs. Similarly, in the case of precision aquaculture farming technologies, tracking systems need to be employed at aquaculture farms, thereby increasing the overall cost of deployment.

Opportunity: Growing adoption of UAVs or drones in agricultural farms

Unmanned aerial vehicles (UAVs) or drones are quickly moving from battlefields to agricultural fields; they capture highly accurate aerial images up to hundreds of hectares/acres in a single flight, thereby saving a lot of money in the process. Agricultural drones are cheaper than surveillance drones, and they come with advanced sensors and imaging abilities that provide farmers with new ways of increasing crop yield and reducing wastage of resources.

Generally, farmers use satellite imagery and planes to monitor crops. However, these methods are time-consuming, and the data collected through these methods take a longer time to process and analyze.

Challenge: Unavailability of simple and standardized data management and data aggregation tools

Data aggregation is a major challenge in the agriculture IoT market. The data obtained from farms using smart agricultural tools is highly important as this data helps farmers in making productive decisions. In precision farming, large volumes of crucial data pertaining to mapping, variable rate seeding, soil testing, yield monitoring, and historical crop rotation is produced regularly. This data must be stored and managed properly as successful precision farming relies entirely on it for assessing farm conditions. Data management is the key to making smart farm-management decisions and improving farm operations.

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There is no industry standard for managing agricultural data; this makes the task difficult for growers. The challenge is to standardize the data management system throughout the industry to enable uniformity of operations. Many growers or farmers are not aware of the effective use of data for decision-making purposes. Therefore, it is important to provide farmers and growers with proper data management tools and techniques to acquire, manage, process, and use data effectively. Monitoring, collecting, and managing data on feeding rate, pH levels, quality of water, humidity, and others are essential for livestock farming, and the information also assists in making decisions to improve livestock production. Proper data management in livestock farming enables farmers to understand the behavioural pattern of livestock, track their locations, and detect disease conditions in animals.