When tracking any disease, early and accurate identification is essential. The traditional method of identifying plant disease is done by visual examination. This process is plagued with inefficiencies and prone to human error. For a trained computer, diagnosing plant disease is essentially pattern recognition. After sorting through hundreds of thousands of photos of diseased plants, a machine learning algorithm can spot disease type, severity, and in the future, may even recommend management practices to limit loss from a disease.
Machine learning in agriculture allows for more accurate disease diagnosis—all the while, helping eliminate wasted energy and resources from misdiagnoses. Farmers can upload field images taken by satellites, UAVs, land-based rovers, pictures from smartphones, and use this software to diagnose and develop a management plan.
Crop disease is a major cause of famine and food insecurity around the world. A core objective of modern agriculture is to create seeds and crop protection products that provide relief to these global challenges. One of the many benefits of machine learning is how this technology can make more accurate and precise improvements to a process. In plant breeding, machine learning is helping create more efficient seeds. Such advancements offer the potential to create even more adaptable, and productive seeds to better utilize our precious natural resources.
Source: Modern Agriculture