Identification And Diagnoses of Plant Diseases in Fruit Crops Using Machine Learning Algorithms

India, being an agriculture-centric nation, historically relied on traditional farming methods that often resulted in crop losses and significant financial setbacks for farmers. In the present era, however, the incorporation of technology in agriculture has led to a rise in crop value, still, there is a great scope of research. One of the key reasons of the production of low-quality crops is the presence of infections or diseases. This research paper focuses on the application of machine learning algorithms, specifically Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and MobileNetV2, for plant disease detection in apple, strawberry, and grape leaves. The dataset used comprises 29,327 images, and various performance metrics were employed to evaluate the models' accuracies. According to the findings, the SVM model performs best with 82%, 98% and 72% accuracy for apple, strawberry, and grape crop respectively. Also, the precision, recall and F-score values are significantly high for CNN. So, among three considered model in the experiment for identification of disease in apple while for grapes and strawberry CNN works better than other two models.

I. Introduction

Plant diseases represents a significant threat to agricultural production and food security worldwide. Due to missing the timely identification of decease in plants, every year a huge loss of crops is reported in all over the world. Early and precise plant diseases identification is essential for effective disease management and mitigate potential yield losses. In recent years, machine learning techniques, such as Convolutional Neural Networks (CNN), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM), MobileNet, have gained increasing attention for plant disease detection due to their ability to process large amounts of data and make accurate predictions. A lot of research is going on in this domain but still there is a huge scope of finding effective and efficient results.