Detecting Three Different Diseases of Plants by Using CNN Model and Image Processing

Abstract

Agricultural output is critical to Pakistan's economy, and plant disease detection is critical for both the environment and human health. This work presents a CNN-based approach for detecting plant diseases, which is evaluated on sample photos to assess the temporal complexity and infected area. The model was given three disease cases: corn common rust, tomato bacterial spot, and potato early blight. Using the CNN algorithm, the CNN model attained an accuracy of 95.55% for tomato bacterial spot, 96.72% for Corn Common Rust, and 97.63% for Potato Early Blight. This method may aid in the diagnosis and treatment of plant diseases.