Abstract

Plant disease outbreaks have been more common in recent years, which has put food security and agricultural output at serious risk. Timely intervention and the avoidance of significant losses are contingent upon the swift and precise identification of these illnesses. Convolutional neural networks (CNN) combined with image processing techniques have become a potent tool for automated plant disease diagnosis in response to this difficulty. This study uses CNN and image processing to provide a novel method for plant disease identification. The suggested approach is taking pictures of plants, enhancing disease signs in the photos by pre-processing, and then using a CNN model that has been trained to classify diseases. A substantial dataset of plant photos, including both healthy and sick examples, is used to train the CNN model. The stages of feature extraction, feature mapping, and classification in the training process let the model recognize and identify various illness patterns. The suggested approach is effective, as evidenced by the experimental results, which show great accuracy and robustness in plant disease identification. In addition to improving illness diagnosis accuracy, CNN and image processing techniques provide a real-time, affordable solution that may be easily applied in the field. The findings of this study aid in the creation of automated plant disease management systems, which help farmers and other agricultural professionals identify and treat plant illnesses quickly, increasing crop yields and sustainability.