A Novel Framework for Automatic Plant Disease Detection Using Convolutional Neural Networks

This research aims to introduce a groundbreaking framework for the automated detection of plant diseases leveraging the power and potential of Convolutional Neural Networks (CNNs). The productivity of agriculture was substantially affected by the rapid spread of plant diseases, which necessitated more efficient and timely detection methods. This proposed framework employed the state-of-the-art CNN architecture, which could be adept at learning intricate patterns within different plant images. This model was trained on a comprehensive dataset that encompassed various diverse plant diseases and healthy specimens. For the enhancement of generalization, the usage, and employment of various transfer learning techniques were also necessitated. This framework demonstrated superior accuracy, with sensitivity and specificity in the identification of various plant diseases across different plant species. Moreover, after the model’s efficiency was validated through its real-time testing on many diverse crops. This research aims to contribute to the advancement of precision agriculture by providing a more robust and scalable solution for early disease detection, which could empower farmers to adopt preventive measures and mitigate losses in yield.