Agricultural Sustainability: Detecting Crop Diseases using Deep Learning and Image Processing

Agricultural productivity plays a vital role in global economic development and growth. Identifying plant diseases is essential for maintaining crop productivity and economic stability. Advanced solutions are required since traditional methods of diagnosis are laborious and prone to mistakes. Using convolutional neural networks (CNNs) and transfer learning models like EfficientNetB0, VGG19 InceptionV3 and Xception this study investigates deep learning approaches for crop disease identification. Models were trained, verified and tested using the Plant Village dataset which contains 38 different crop diseases. To improve accuracy various optimization strategies and activation functions were used. To test this performance the findings showed an outstanding performance with detection accuracy over 97% for all models. The models’ dependability was confirmed by evaluation criteria such as accuracy recall and F1-score. The range of precision was reported to be between 98% and 100%. The Xception model outperformed the others with an accuracy of 99.83% and demonstrated its efficacy in early disease identification. Deep learning offers an innovative method for managing agricultural diseases by drastically reducing diagnostic time and increasing accuracy. By using these strategies, food security can be improved, losses can be reduced, and sustainable farming methods can be encouraged. The potential of deep learning to transform contemporary agriculture is highlighted by this study.