Plant Leaf Disease Detection Using CNN Model

Document Type : Primary Research paper


1 B. Tech Scholar, School of Computer Science and Engineering, Sandip University, Nashik, Maharashtra, India

2 Assistant Professor, School of Computer Science & Engineering, Sandip University, Nashik, Maharashtra, India


Plant diseases can have a significant impact on crop yield and quality, resulting in substantial financial losses for farmers. Early detection of plant diseases can help prevent their spread and increase crop yield. With the increasing availability of digital image datasets, machine learning algorithms can be used for automated detection of plant diseases. In this paper, we propose a plant leaf disease detection system using the Convolutional Neural Network (CNN) algorithm. The system uses a dataset of plant leaf images of five different crops - apple, cherry, corn, grape, orange, and potato - to train and test the CNN model. The results show that the proposed system can accurately detect the plant leaf diseases in the given images. In this paper, during preprocessing we have passed resizing, Rescaling, Shuffling, Dropout, Zoom/Brightness adjustment, Rotation, Background correction, horizontal flipping, etc. parameters So that we can convert our image data into augmented image data which will help our CNN model to learn for lowresolution images. We aim is to analyze the success rate of the proposed models and compare the outcome with other strategies.