Supervised Learning-Based Image Classification for the Detection of Late Blight in Potato Crops

Abstract

This article presents the application of supervised learning and image classification for the early detection of late blight disease in potato using convolutional neural network and support vector machine SVM. The study was realized in the Boyacá department, Colombia. An initial dataset is created with the acquisition of a large number of images directly from the crops. These images are pre-processed in order to extract the main characteristics of the late blight disease. A classification model is developed to identify the potato plants as healthy or infected. Several performance, efficiency, and quality metrics were applied in the learning and classification tasks to determine the best machine learning algorithms. Then, an additional data set was used for validation, image classification, and detection of late blight disease in potato crops in the department of Boyacá, Colombia. The results obtained in the AUC curve show that the CNN trained with the data set obtained an AUC equal to 0.97; and the analysis through SVM obtained an AUC equal to 0.87. Future work requires the development of a mobile application with advanced features as a technological tool for precision agriculture that supports farmers with increased agricultural productivity.