The paper "Detection of plant leaf diseases using deep convolutional neural network models" by Puja Singla, Vijaya Kalavakonda, and Ramalingam Senthil focuses on developing a web-based application to detect plant leaf diseases using deep learning models. The study aims to enhance crop yield and quality by predicting plant diseases, which is crucial for sustainable agricultural development. The authors conducted a comparative study using the PlantVillage dataset for binary and multiclass classifications, evaluating various deep convolutional neural network (CNN) models such as MobileNet, DenseNet201, ResNet50, Inception V3, and VGG 16 and 19. The MobileNet model was found to be the most effective, achieving an accuracy of 97.35% for multiclass classification and 99.39% for binary classification. The proposed model also performed well in terms of precision, recall, and other metrics. A web application was developed using the MobileNet model to send email alerts to farmers about plant diseases. The research results are expected to improve crop productivity and economic stability through timely and accurate disease detection.The paper "Detection of plant leaf diseases using deep convolutional neural network models" by Puja Singla, Vijaya Kalavakonda, and Ramalingam Senthil focuses on developing a web-based application to detect plant leaf diseases using deep learning models. The study aims to enhance crop yield and quality by predicting plant diseases, which is crucial for sustainable agricultural development. The authors conducted a comparative study using the PlantVillage dataset for binary and multiclass classifications, evaluating various deep convolutional neural network (CNN) models such as MobileNet, DenseNet201, ResNet50, Inception V3, and VGG 16 and 19. The MobileNet model was found to be the most effective, achieving an accuracy of 97.35% for multiclass classification and 99.39% for binary classification. The proposed model also performed well in terms of precision, recall, and other metrics. A web application was developed using the MobileNet model to send email alerts to farmers about plant diseases. The research results are expected to improve crop productivity and economic stability through timely and accurate disease detection.