Regional Analysis
North America Dominates the Global Market
North America is the most significant shareholder in the global AI in the agriculture market and is expected to grow during the forecast period. Increases in disposable income, ongoing funding for automation, big bets on the Internet of Things, and a growing emphasis from governments on homegrown AI equipment development are all hallmarks of the North American economy. The market also benefits from various agricultural technology vendors researching artificial intelligence solutions. Artificial intelligence (AI) is projected to usher in a technological revolution in the future of farming techniques in the region, with drones, robots, and intelligent monitoring systems being deployed in research and field experiments. Additionally, the regional market is anticipated to be driven by the increasing use of AI-powered technologies in the agricultural sector. In addition, the growing popularity of Internet of Things (IoT) devices in agriculture are anticipated to boost the worldwide AI in the agriculture market in the area.
Asia-Pacific is expected to grow during the forecast period. Rapid expansion is attributed to the expanding use of AI tools in farming. India and China, two of the world's fastest-growing economies, are using artificial intelligence (AI) technologies like remote monitoring and predictive analysis in the food business. Furthermore, the growing demand in these economies for smart cities is prompting agribusiness enterprises to implement AI-powered solutions and services. China is seeing a significant increase in the adoption of AI solutions in agriculture in the region, mainly to Alibaba Group's entry into the agricultural solution market with its AI technology to aid small farmers in the country.
Al is used for row crop cultivation. The robot is so effective at weeding rows of crops that only a twentieth as much herbicide is needed. The European Soil Data Centre is Europe's thematic focus for soil-related data. Its purpose is to act as a central repository for all relevant soil data and information on a European scale. The increasing popularity of Al in agriculture can be attributed to the widespread adoption of AI and computer vision-based monitoring and reporting technologies for indoor and outdoor fields. With over a thousand operating indoor farms in Germany and expansion into other European countries, the demand for Al in agriculture is increasing.
Due to the increasing popularity of AI-powered systems that employ deep learning methods, LAMEA is predicted to have moderate expansion. The global AI in the agriculture market is expected to expand at a staggering rate due to the increasing prevalence of applications that combine IoT and AI, such as predictive analysis, machine learning, and others. Despite the progress made over the previous two decades in agricultural innovation initiatives, there is still a pressing need to fortify agricultural research, technology, and innovation infrastructure to meet these problems.
Segmental Analysis
The global AI in agriculture market is segmented based on component, technology, and application.
The market is further segmented by components into hardware, software, and services.
The hardware segment encompasses a variety of robotic equipment, drones, cameras, and sensors employed in AI-powered agricultural applications. This encompasses soil sensors that monitor soil properties, including moisture and nutrients. Uncrewed aerial vehicles (UAVs) or drones for aerial data collection, Autonomous farm equipment and agricultural robotics, and Crop and livestock monitoring cameras with computer vision capabilities. The hardware component facilitates the acquisition of real-time data from the environment, which is subsequently processed and analyzed using AI algorithms. The increasing adoption of agricultural automation and precision farming techniques drives the demand for advanced hardware in the AI in agriculture market.
AI in the agriculture market is dominated by the software segment, the fastest-growing and largest component. It encompasses AI-powered analytics platforms that analyze sensor data and offer insights, machine learning algorithms for predicting crop yields, pest infestations, and other factors, computer vision software for plant and livestock monitoring, and decision support systems that suggest the most effective farming practices. Microsoft, IBM, and Deere & Company are among the most significant participants in this sector, providing AI-powered agricultural software solutions. One of the primary factors driving the adoption of AI software is its capacity to optimize farming operations and automate decision-making.
The services segment includes consultation, integration, and maintenance support, thereby offering farmers the opportunity to implement and manage AI-driven technologies effectively. This also includes AI training programs, data management, and analytical tools for the enhancement of decision-making processes, as well as designing AI solutions tailored to the agriculture industry’s requirements. The growing demand for crop monitoring and yield prediction from precision farming, since these services help optimize farming processes and resource utilization based on the advancement in AI acts as a growth driver for this segment.
IBM, Microsoft, Trimble, etc., are key players in this segment.
The market is further segmented by technology into machine learning, deep learning, predictive analytics, and computer vision.
Machine learning and deep learning algorithms are the foundation of numerous AI applications in agriculture. These technologies facilitate the examination of extensive datasets to identify patterns and insights that can be used to inform agricultural decisions. ML models can analyze historical crop yield data, weather patterns, soil conditions, and other factors to predict future yields. Examples of critical use cases include this. By analyzing imagery from drones or ground-based sensors, deep-learning algorithms can detect early indicators of pests or diseases in crops. ML can identify behavioral changes in livestock that may suggest health issues or other issues. Machine learning is a potent instrument for agricultural operations optimization due to its capacity to learn and develop from data continuously.
Predictive analytics employs data mining techniques and statistical models to anticipate future events or outcomes. In the agricultural sector, predictive analytics forecasts near-term weather patterns by analyzing meteorological data, satellite imagery, and other inputs. A more efficient irrigation schedule is achieved by predicting soil moisture levels and plant water requirements. Optimal planting strategies are determined by forecasting market demand, produce yields, and other factors. Rather than relying on historical averages or guesswork, predictive analytics enables producers to make proactive, data-driven decisions.
Computer vision makes use of AI algorithms to analyse images and videos taken from cameras or drones, and provide real-time insights on crop health, pest detection, and livestock monitoring. By using computer vision farmers can collect detailed data on crop growth, soil conditions, and pest infestations in real time. This technology enhances farm management efficiency by automating tasks such as improving irrigation, spraying herbicides or insecticides, and disease detection. Blue River Technology developed "See & Spray" technology, which uses computer vision and machine learning to spray herbicides only on weeds while sparing crops.
Enhanced crop monitoring and disease detection, demand for increased agricultural productivity, and efficiency drive the growth of this segment. Gamaya, Blue River Technology, Taranis, etc., are key market players in the computer vision segment.
The market is further segmented by application into precision farming, drone analytics, agriculture robots, livestock monitoring, and others.
Precision farming is the most extensive and rapidly expanding implementation of AI in the agricultural sector. It entails the utilization of cutting-edge technologies, including AI algorithms, sensors, and GPS, to enhance yields and optimize agrarian practices. Predictive analytics for crop yield forecasting and planning, automated guidance systems for farm equipment to reduce overlap and maximize efficiency, and variable rate application of inputs such as fertilizers and pesticides based on soil and crop requirements are among the critical use cases. Precision farming allows farmers to optimize productivity by applying the appropriate quantity of inputs at the proper time and location, thereby minimizing waste and environmental impact.
Drones outfitted with cameras and sensors are utilized more frequently in agriculture to gather aerial data regarding soil and crop conditions. AI-powered analytics platforms can process this data to identify early indications of pests, diseases, or nutrient deficiencies, evaluate crop health and growth stages to optimize management and generate high-resolution maps of fields for precision farming applications. Drone analytics offers producers a bird's-eye perspective on their operations and actionable insights to enhance decision-making.
Agriculture robots are designed to perform various agricultural activities with high precision and efficiency, reducing the need for manual labour and increasing overall productivity. Robots are involved in agriculture activities such as planting, harvesting, testing soils, and weeding. Robots utilize AI to navigate fields, identify crops, and execute precise actions such as seeding, pesticide application, and harvesting. Key drivers include operational efficiency, lower labour expenses, and optimized crop yields by ensuring accurate and timely agricultural operations. Key players like John Deere and Agrobot are driving advancements in robotic solutions tailored to farming needs.
The livestock monitoring segment leverages advanced technologies to improve the management and productivity of livestock operations. The livestock monitoring makes use of tools such as wearable sensors, computer vision, and machine learning algorithms that allow for real-time health monitoring and the early detection of diseases, thus improving animal welfare and reducing losses. AI also optimizes feeding, breeding, and living conditions by analysing data that leads to increased productivity and efficiency. This automated monitored process allows for the best allocation of resources, reducing labour costs and improving animal welfare. The livestock segment is expected to grow as the demand for sustainable and efficient agriculture practices grows. Key players are Allflex Livestock Intelligence, GEA Cowscout, Cainthus, etc.
