Blue River Technology’s See and Spray uses computer vision and machine learning to target weeds.
Artificial intelligence is changing many things in our lives, including the way our food is produced. Technologies like machine learning, image recognition, and predictive modeling are being applied in the agriculture industry as ways to boost productivity and efficiency. These approaches could be important steps in the effort to produce more food for a growing global population by helping farmers reduce chemical inputs, detect diseases sooner, buffer against labor shortages, and respond to weather conditions as the climate changes.
Here are three examples of how AI is being used in agriculture to improve results.
Reducing Herbicide Use
John Deere recently invested $305 million to acquire Blue River Technology, a seven-year-old tech company that developed a robot called “See and Spray.” It uses computer vision, robotics, and machine learning to precisely manage weeds. Instead of spraying an entire field, the system can find and spray only where the weeds are. The system is not only efficient in the sense that it is faster than humans, but it also reduces up to 90% of the volume of chemicals normally sprayed and helps reduce herbicide resistance, according to the company.
Detecting Disease
A German company called PEAT has created Plantix, a mobile app that uses image recognition to detect plant diseases, pests, and soil deficiencies affecting plant health. Farmers and gardeners take a simple smartphone picture of their affected, and PEAT’s server identifies the pathogens or pests that are affecting the plants using deep learning built into image recognition software. Plantix then automatically recommends control options back to the user’s smartphone.
Predicting Weather
aWhere, a Colorado-based B Corporation, is using satellite imagery to gather over 7 billion data points around the world daily. It then uses machine learning to forecast weather, analyze crops, and help farmers increase yields and profits. Although aWhere monitors growing regions throughout the world, it delivers localized information and recommendations to the farmers who use its services. It also forecasts the output of crops around the world, which helps farmers foresee price volatility and respond accordingly.
