Detection of Leaf Diseases in Agricultural Plants Using Machine Learning

Identifying plant leaves early on is key to preventing catastrophic outbreaks. An important study area is automatic disease detection in plants. Fungi, bacteria, and viruses are the main culprits in most plant illnesses. Choosing a classification method is always challenging because the quality of the results can differ depending on the input data. A few examples of several classification techniques include K-Nearest Neighbour Classifier (KNN), Probabilistic Neural Network (PNN), Genetic Algorithm, Support Vector Machine (SVM) and Principal Component Analysis, Artificial Neural Network (ANN), and Fuzzy Logic. Classifications of plant leaf diseases have many uses in various industries, including agriculture and biological research. Pre-symptomatic diagnosis and crop health information can aid in the ability to manage pathogens through proper management approaches. Convolutional neural networks (CNNs) are the most widely used DL models for computer vision issues since they have proven very effective in tasks like picture categorization, object detection, image segmentation, etc. The experimental findings demonstrate the proposed model’s superior performance to pre-trained models such as VGG16 and InceptionV3. The range of categorization accuracy is 76% to 100%, based on.