Early-stage plant disease recognition: A deep learning approach on leaf symptom image

Abstract:

Plant diseases are one of the major reasons for crop loss worldwide, affecting farmers’ income and food production. Detecting diseases at an early stage is very important because it helps prevent the spread of infection and reduces damage to crops. However, traditional methods of disease identification are often slow and depend on expert knowledge. This study uses machine learning and deep learning techniques to detect plant diseases from leaf images at an early stage, focusing on small changes such as slight discoloration and texture variation. Different models like SVM, Random Forest, CNN, VGG16, ResNet50, and MobileNet were tested and compared. The results showed that deep learning models, especially transfer learning approaches, performed better in identifying early symptoms of diseases. The study highlights how artificial intelligence can help farmers with faster and more accurate disease detection, leading to better crop management and reduced crop losses. The findings underscore the importance of integrating machine learning technologies into smart farming ecosystems and provide valuable insights for future research aimed at real-time, field-deployable plant disease monitoring systems.