Title: Computer-aided crop disease classification system using colour and texture features

Authors: Megha Agarwal

Addresses: Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

Abstract: Reliable and fast plant disease recognition system is very important for both, human health and economy of the world. It is required to design an automatic system to predict the crop diseases and estimate its production accordingly. Several factors are causing damages to the crop hence in this paper, features are extracted through colour and texture properties of tomato plant leaves, and supervised classification is performed. Unlike other available features, in the proposed feature, images are decomposed into different frequency sub-bands using difference of Gaussian filters and features are extracted through local ternary co-occurrence patterns. Colour properties are added through hue and saturation components. This comprehensive feature is used to perform classification on PlantVillage data set. Performance is evaluated using machine learning classifiers. The proposed hand-crafted feature has performed superior to the deep learning models as well as state-of-the-art methods in terms of classification accuracy and AUC.

Keywords: disease classification; plant disease; PlantVillage; texture feature.

DOI: 10.1504/IJCAT.2023.134092

International Journal of Computer Applications in Technology, 2023 Vol.73 No.1, pp.42 - 49

Received: 31 Jan 2023
Accepted: 11 Mar 2023
Published online: 10 Oct 2023 *

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