Convolutional Neural Network Method for Effective Plant Disease Prediction

Agriculture as a source of food is essential for humankind. Therefore, the diagnosis of plant diseases is a significant concern. Plant disease diagnosis through plant monitoring is necessary for maintainable agriculture. Observing plant diseases automatically is very challenging. Managing plant diseases requires a lot of effort and expertise. Traditionally, identifying plant foliar disease is subjective, inefficient, and expensive, requiring a large number of personnel and a large amount of information about plant disease. This novel uses a deep learning-based Convolutional Neural Network (CNN) approach for plant disease identification to tackle this problem. First, collect the dataset from online Kaggle and pre-process images to remove noise in the first phase. Then Logistic Decision Regression (LDR) method was utilized for feature selection in the pre-processed plant image. After that, we apply segmentation based on selected features. Finally, our proposed method proficiently classifies the plant's disease based on segment images. Therefore, this approach produces high disease detection accuracy and specificity with a minimum error rate compared to different methods.