Accurately predict plant diseases with an average error of less than 5% and stop their spread in time. We train ML models on images from datasets like
ARS labeled as crop species (corn, wheat, soybeans, rice, etc.) with diseases (rust, blight, wilt, etc.). The training is over if the model can recognize patterns and relationships in the seen data and then make maximally improved predictions or classifications on unseen data. After training, ML models will be able to recognize diseases themselves. Then we test if the model's predictions are accurate, precise, and reliable by comparing them with so-called validation datasets. It’s time to integrate this ready-to-use model into a computer vision system. Cameras will automatically capture crops in the field and then the system will process images to find signs of diseases.