A Study on the Selection of Features, Classifiers, and Resampling in Plant Disease Detection from Leaf Images
subject: Machine learning; Plant diseases; Texture features
description: Computer vision has become an integral part of modern agriculture. One of the key applications of computer vision is the automatic detection and classification of plant disease from digital images of plant leaves. In this study, we evaluate the discriminatory capability of selected texture features in identifying plant diseases from leaf images. Texture features are extracted from resized raw images. Experiments are carried out with public data sets of five different plants. Through extensive experimentation, two classifiersRandom forest and XGBoost are chosen for the evaluation. The class imbalance problem is addressed with a simple resampling. Resampling considerably improves the prediction accuracy. With the raw input images, the best feature as well as classifier depends on the plant type and the quality of the input images. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
creator: Prashaant, C.; Bijeesh, T.V.; Bejoy, B.J.; Raju, G.
source: Lecture Notes in Networks and Systems;Volume;1834 LNNS;pp.297-310
publisher: Springer Science and Business Media Deutschland GmbH
date: 01-01-2026
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 23673370; ISBN: 978-981957240-3;
language: English
type: Conference paper
coverage: Prashaant C., CHRIST University, Bengaluru, India; Bijeesh T.V., CHRIST University, Bengaluru, India; Bejoy B.J., CHRIST University, Bengaluru, India; Raju G., Sahrdaya College of Engineering and Technology, Kerala, Thrissur, India