Machine learning application in plant diagnosis for improved crop production

The advances in capabilities of machine learning models are being extended to the agricultural sector for improved crop yield. By analyzing vast datasets of crop leaf images, sensor data, and environmental factors, machine learning researchers have made laudable progress in real-world scenarios, identifying disease types, extracting distinguishing features like lesion patterns and discoloration, and classifying crops and pathogens into specific groups. The efforts are effectively improving output in large and small scale farms. In this comparison, some of such existing efforts in literature are highlighted.