This paper presents a study on detecting plant leaf diseases using deep convolutional neural network (CNN) models. The goal is to develop a web-based application that can detect plant diseases using leaf images and alert farmers. The study compares various CNN models, including MobileNet, DenseNet201, ResNet50, Inception V3, and VGG16 and VGG19, with a proposed model. The proposed model achieved an accuracy of 99.39% for binary classification, which is higher than other deep CNN models. The model also achieved high precision and recall values. The study also evaluates various metrics such as precision, recall, classification report, confusion matrix, and accuracy. The results show that MobileNet is influential among the selected models, with an accuracy of 97.35% and precision and recall of 0.973 each for multiclass classification. The web-based application was created using the MobileNet model to send email alerts to users regarding plant disease. The research results help improve a country's crop productivity and the overall economy through prompt and precise decision-making on crop diseases. The study also discusses the challenges of predicting plant disease due to a lack of laboratory competence and expertise. It highlights the importance of using automated detection technologies to identify plant diseases and take swift remedial measures. The study also discusses various machine learning techniques that have been proven to help detect plant diseases, including support vector machine, K-nearest neighbor, multiple linear regression, decision tree, random forest, naive Bayes, logistic regression, artificial neural networks, deep CNN, and fuzzy logic. The study also discusses the methods used for disease detection using leaf images, including acquiring leaf images, extracting features from the images, and using the extracted features to classify leaf images as either diseased or not. The study also discusses the results of previous research studies on plant disease detection, including the use of image-processing methods, piecewise fuzzy C-means clustering, and the use of deep belief networks. The study also discusses the results of a study by Mohanty et al., which used a publicly available dataset with 54,306 images to train a deep CNN for classifying 26 diseases of 14 crop species. The study also discusses the results of a study by Chen et al., which proposed a method to solve few-shot plant disease recognition using local feature-matching conditional neural adaptive processes. The study also discusses the results of a study by Cristin et al., which proposed an image-processing method for plant disease identification by removing noise and detecting artefacts during preprocessing. The study also discusses the results of a study by Mohanty et al., which used a publicly available dataset with 54,306 images to train a deep CNN for classifying 26 diseases of 14 crop species. The study also discusses the results of a study by Chen et al., which proposed a method to solve fewThis paper presents a study on detecting plant leaf diseases using deep convolutional neural network (CNN) models. The goal is to develop a web-based application that can detect plant diseases using leaf images and alert farmers. The study compares various CNN models, including MobileNet, DenseNet201, ResNet50, Inception V3, and VGG16 and VGG19, with a proposed model. The proposed model achieved an accuracy of 99.39% for binary classification, which is higher than other deep CNN models. The model also achieved high precision and recall values. The study also evaluates various metrics such as precision, recall, classification report, confusion matrix, and accuracy. The results show that MobileNet is influential among the selected models, with an accuracy of 97.35% and precision and recall of 0.973 each for multiclass classification. The web-based application was created using the MobileNet model to send email alerts to users regarding plant disease. The research results help improve a country's crop productivity and the overall economy through prompt and precise decision-making on crop diseases. The study also discusses the challenges of predicting plant disease due to a lack of laboratory competence and expertise. It highlights the importance of using automated detection technologies to identify plant diseases and take swift remedial measures. The study also discusses various machine learning techniques that have been proven to help detect plant diseases, including support vector machine, K-nearest neighbor, multiple linear regression, decision tree, random forest, naive Bayes, logistic regression, artificial neural networks, deep CNN, and fuzzy logic. The study also discusses the methods used for disease detection using leaf images, including acquiring leaf images, extracting features from the images, and using the extracted features to classify leaf images as either diseased or not. The study also discusses the results of previous research studies on plant disease detection, including the use of image-processing methods, piecewise fuzzy C-means clustering, and the use of deep belief networks. The study also discusses the results of a study by Mohanty et al., which used a publicly available dataset with 54,306 images to train a deep CNN for classifying 26 diseases of 14 crop species. The study also discusses the results of a study by Chen et al., which proposed a method to solve few-shot plant disease recognition using local feature-matching conditional neural adaptive processes. The study also discusses the results of a study by Cristin et al., which proposed an image-processing method for plant disease identification by removing noise and detecting artefacts during preprocessing. The study also discusses the results of a study by Mohanty et al., which used a publicly available dataset with 54,306 images to train a deep CNN for classifying 26 diseases of 14 crop species. The study also discusses the results of a study by Chen et al., which proposed a method to solve few