Tomato Plant Leaves Disease Detection using Machine Learning

Plants are the primary source of food for the whole population. The disease in plants leads to low crop production and reduces food quality. Early detection of the plant disease can save both time and labor costs. In this paper, our main objective is to create an end-to-end system for detecting tomato disease using the machine learning algorithms like logistic regression, support vector machine, and random forest algorithms. To extract the features from the image Histogram of Oriented gradients is used, and for evaluating the model, classification metrics are used like precision, recall, and f1 score. Out of all these algorithms, the support vector machine performed best by using HOG as a feature descriptor and at the last step, image classification is deployed with the help of Streamlight application.