Chili leaf diseases cause significant damage to chili plants, leading to reduced crop yield and economic losses for farmers. Early detection and diagnosis of these diseases are crucial for effective disease management. In this research paper, we propose a chili leaf disease prediction model using Convolutional Neural Network (CNN). The proposed model utilizes an image dataset collected from different regions ,consisting of chili leaf images infected with common chili leaf diseases, like bacterial leaf spot, leaf Curl , Mosaic virus, etc. We pre-processed the dataset to enhance the image quality and to remove noise. The preprocessed dataset was split into training and validation sets. The CNN model was trained using the training set and validated using the validation set. The proposed model achieved an high accuracy on the validation set. The proposed model can be used to predict the occurrence of chili leaf diseases in real-time, which can help farmers in taking preventive measures to protect their crops
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.