Artificial intelligence — which is agriculture’s other “AI” and also referred to as deep learning or machine learning (ML) — interacts with us every day. From Siri, to Alexa, to cars that help you drive and park, to algorithms that serve you ads, news, job and home listings, to suggestions for what music and news to listen to, it’s there. The goals of artificial intelligence include learning, reasoning, and perception — the computer programs write themselves, get better the more data they are fed to learn from (co-called “Big Data”), and we don’t really know how they do it.
Agriculture is one of the oldest and most important professions; worldwide it is a $5 trillion industry. The global population is expected to reach more than 9 billion by 2050, and agricultural production will need to more than double (70 percent) to fulfill the demand. Land, water, and resources are already becoming insufficient due to multiple economic, environmental, and sociological forces. A smarter, more efficient, and even more productive approach for the supply-demand chain is urgently needed.
Artificial intelligence technologies are poised to yield healthier crops, control pests, monitor soil, and growing conditions, organize data for farmers, help with the workload, and improve a wide range of agriculture-related tasks in the entire food supply chain. Internet of Things (IoT) technologies and advanced analytics help farmers analyze real time data like weather, temperature, moisture, prices, or GPS signals.
A few examples of AI- or ML-enabled technologies are:
- A German-based tech start-up PEAT has developed an AI-based application called Plantix that can identify the nutrient deficiencies in soil including plant pests and diseases, and recommend fertilizer types to improve harvest quality. This app uses image recognition-based technology.
- Trace Genomics is a machine learning-based company that analyzes soil and crop’s health conditions and produce healthy crops with a higher level of productivity.
- SkySqurrel Technologies is a drone-based ariel imaging solutions company that monitors crop health. In this technique, the drone captures data from fields and then data is transferred via a USB drive from the drone to a computer and analyzed by experts.
- AI applications in agriculture provide guidance to farmers about water management, crop rotation, timely harvesting, type of crop to be grown, optimum planting, pest attacks, nutrition management.
- Farmers can get AI benefits right now, with tools as simple as an SMS-enabled phone and the Sowing App.
- Agricultural robots have been trained to check the quality of crops, detect weed with picking and packing of crops, perform repetitive tasks, control weeds and harvest crops at higher volumes than humans, and address skills and labor workforce gaps.
Farmers are contributing to the development of these high-tech scientific accomplishments: collecting the “big data” to feed and train these AI models, and helping to prove drone and robot technology for precise cultivation for higher crop yield and better quality while using fewer resources for the betterment of the entire industry. It is estimated that farmers generate over 4.1 million data points per day, and have already deployed over 75 million “Internet of Things” (IoT) interconnected devices.
AI will allow farms of all sizes to meet the challenges of the 21st century and beyond.