AI in agriculture Market Overview
The global AI in agriculture market was valued at $584.0 million in 2018, and is expected to advance at a CAGR of 38.3% during the forecast period. This can be attributed to factors including rising demand for agricultural production, increasing adoption of advanced technologies and smart sensors, rapid demand for drones in agricultural farms, and increasing need for livestock monitoring.
Based on technology, the market is classified into machine learning, computer vision, and predictive analytics. Among these, the machine learning category is projected to continue to lead the market throughout the forecast period. Machine learning plays a major role in AI in agriculture market with a combination of agronomic sciences and data technologies, which is being increasingly adopted by agricultural businesses and farmers across the globe. The rising deployment of information technology (IT) in agriculture applications such as field conditions management, crop management, and livestock management are considerably driving the market for machine learning category in AI in agriculture industry.
Based on application, the market is categorized into agricultural robots, precision farming, drone analytics, livestock monitoring, and others, wherein others include smart greenhouse management, soil management, and fish farming management. Among all categories, precision farming generated the largest revenue in the global market, and is expected to continue to lead the market in the coming years. This is due to precision farming gaining popularity among farmers as a consequence of increasing need for optimum yield production with limited available resources resulting in cost reduction of crop production. Furthermore, rapid use of IoT in agriculture sector is contributing to the growth of the precision farming market.
AI in agriculture Market Dynamics
Growing demand for monitoring of livestock is one of the prime factors driving the AI in agriculture market. Livestock plays an important role in fulfilling the demand for meat, milk, eggs, and wool. With changing lifestyle, growing population, and increase in per-capita income level, there is a rise in meat, eggs, and milk consumption globally. To fulfill the demand for protein rich foods, health of livestock needs to be monitored on a regular basis. With the application of advanced AI solutions such as facial recognition for livestock and image classification incorporated with body condition score and feeding patterns, dairy farms are now able to individually monitor all behavioral aspects of a herd. Further, for monitoring the health of livestock, many companies are increasingly using machine vision that helps to recognize hide patterns, facial features, water and food intake of livestock, as well as helps in recording their behavior and temperature.
Moreover, facial recognition also helps in diagnosis of abnormal cows before they show severe symptoms, thus saving farmers from the loss of low milk production. For instance, in China, an AI company is developing a livestock health monitoring system with facial recognition for managing herd of cows. Hence, with increasing use of AI for monitoring the health of livestock, the market for AI in agriculture is growing across the globe.
Rising adoption of IoT is another major factor driving AI in agriculture market. With increasing use of mobile devices and cloud computing, the IoT industry is growing across the globe. For instance, it was recorded that the global IoT industry was valued at $268.5 billion in 2018, and is projected to reach $470 billion by 2020, accelerating at a CAGR of about 32%. With growing IoT industry due to various benefits offered by IoT such as ability to handle large amount of structured and unstructured data format, the demand for IoT is increasing in agriculture sector.
Additionally, with rising population, demand for advanced technologies in agricultural practices for improving production of crops is surging around the globe. For instance, according to United Nations (UN) Food and Agriculture Organization, the world would need to produce nearly 70% more food in 2050 than it did in 2006, to be able to feed the growing population. Hence, to meet this emerging demand, agricultural companies and farmers are rapidly turning to the benefits of IoT for crop analytics and greater production capabilities. With growing demand for smart agriculture and precision farming, there is a greater use of IoT technology in farming, thereby driving the global AI in agriculture market.
AI in agriculture Market Competitive Landscape
In the recent past, the major players in the market have been mainly focusing on mergers and acquisitions, and partnerships to improve their product portfolios. Additionally, the market players are focusing on offering advanced and innovative platforms. For instance, in April 2019, Trimble Inc. announced the launch of Farmer Core software subscription that enables farmers in connecting their farm operations. The Farmer Core software offers AutoSync feature that helps to automatically sync guidance lines, landmarks, boundaries, and operator information using Precision-IQ field application.
Some of the key players operating in the AI in agriculture market include IBM Corporation, Microsoft Corporation, Bayer AG, Deere & Company, A.A.A. Taranis Visual Ltd., AgEagle Aerial Ssytems Inc., AGCO Corporation, Raven Industries, Ag Leader Technology, Trimble Inc., Google LLC, Gamaya SA, and Granular Inc.