Plant leaf disease detection using hybrid feature extraction techniques

Excessive yield losses are due to delayed identification of leaf diseases, resulting in lower farmer revenue. To address this, early and precise detection methods are crucial. Sunflower, a vital commercial crop in India for edible oil production, faces issues like leaf blight, downy mildew, powdery...

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Bibliographic Details
Published in:	Archiv für Phytopathologie und Pflanzenschutz Vol. 57; no. 3; pp. 200 - 218
Main Authors:	Chetan, H. R., Rajanna, G. S., Nandini, G., Santosh, K. C., Puneeth, B. H., Gururaj, K. S., Sreenivasa, B. R., Abdul Razak, M. S.
Format:	Journal Article
Language:	English
Published:	Abingdon Taylor & Francis 07-02-2024 Taylor & Francis Ltd
Subjects:	Agricultural production Airborne microorganisms Algorithms Blight Color imagery Disease detection Downy mildew Edible oils Feature extraction Image acquisition Image filters Image processing image segmentation K-nearest neighbors algorithm KNN Leaf blight Leafspot Leaves machine learning Medical imaging Plant diseases Powdery mildew Sunflowers
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Summary:	Excessive yield losses are due to delayed identification of leaf diseases, resulting in lower farmer revenue. To address this, early and precise detection methods are crucial. Sunflower, a vital commercial crop in India for edible oil production, faces issues like leaf blight, downy mildew, powdery mildew, and leaf spot. Our research aims to create an algorithm that effectively identifies these diseases using hybrid feature extraction techniques. We compiled sunflower leaf images from village databases and farm visits. Initially, color images were converted to grayscale, and the noise was reduced using Gaussian filtering during image pre-processing. Disease-affected areas were segmented using edge-based approaches. Hybrid feature extraction was then employed to encompass color and texture-related attributes. The K-Nearest Neighbors (KNN) algorithm facilitated disease classification. The proposed algorithm's performance was benchmarked against SVM, RF and DT classifiers. Remarkably, our algorithm achieved exceptional accuracy: 98% for leaf blight, 97.3% for downy mildew, 95% for powdery mildew, and 96.5% for leaf spot detection. These results underscore the algorithm's effectiveness in combating plant diseases and enhancing agricultural productivity.
ISSN:	0323-5408 1477-2906
DOI:	10.1080/03235408.2024.2347985