Published Paper Details:

This study presents a deep learning framework using Convolutional Neural Networks (CNN) to assess plant health through leaf image analysis. Leveraging the growing availability of image data, the research focuses on two key objectives: early detection of plant diseases and the identification of specific conditions from leaf images. A dual-model approach is employed--one binary model for general plant health detection and a multiclass model for categorizing specific diseases. Techniques such as data augmentation, transfer learning with pre-trained models, and advanced feature extraction are used to improve accuracy. Experimental results on public datasets show high predictive performance, with confusion matrix analysis confirming minimal classification overlap. This framework has potential applications in real-time crop monitoring and precision interventions, with future efforts aimed at expanding datasets and incorporating more plant species for better robustness.