Abstract
Reliable and accurate identification of disease is important for protecting the plant from pathogens and to obviate the yield loss. Accessibility to capture digital images and advancements in image processing techniques have made it possible to develop leaf disease identification system with lesser manual involvement. This paper proposes a fuzzy feature extraction technique that utilizes the classification proficiency of the probabilistic neural network (PNN) for the identification of plant leaf disease. The proposed method comprises of two sections: (a) extraction of color and texture features from the leaf images using fuzzy color histogram and fuzzy gray-level co-occurrence matrix, respectively, and (b) classification using the PNN. The leaf images of corn, grapevine, and tomato have been acquired from the PlantVillage database. The proposed model achieves a recognition accuracy of 95.68% and outperforms other classifiers such as SVM, DT, and RF.
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References
Dangl, J.L., Horvath, D.M., Staskawicz, B.J.: Pivoting the plant immune system from dissection to deployment. Science 341(6147), 746–751 (2013)
Deshpande, T.: State of agriculture in India. PRS Legis. Res. 53(8), 6–7 (2017)
Dhingra, G., Kumar, V., Joshi, H.D.: Study of digital image processing techniques for leaf disease detection and classification. Multimed. Tools Appl. 77(15), 19951–20000 (2018)
Vishnoi, V.K., Kumar, K., Kumar, B.: Plant disease detection using computational intelligence and image processing. J. Plant Dis. Protect. 128(1), 19–53 (2021)
Sharif, M., Khan, M.A., Iqbal, Z., Azam, M.F., Lali, M.I.U., Javed, M.Y.: Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Comput. Electron. Agric. 150, 220–234 (2018)
Islam, M., Dinh, A., Wahid, K., Bhowmik, P.: Detection of potato diseases using image segmentation and multiclass support vector machine. In: 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–4 . IEEE(2017)
Deshapande, A.S., Giraddi, S.G., Karibasappa, K., Desai, S.D.: Fungal disease detection in maize leaves using haar wavelet features. In: Information and Communication Technology for Intelligent Systems, pp. 275–286. Springer, Singapore (2019)
Kusumo, B.S., Heryana, A., Mahendra, O., Pardede, H.F.: Machine learning-based for automatic detection of corn-plant diseases using image processing. In: 2018 International Conference on Computer, Control, Informatics and Its Applications (IC3INA), pp. 93–97 . IEEE (2018)
Kaur, S., Pandey, S., Goel, S.: Semi-automatic leaf disease detection and classification system for soybean culture. IET Image Process. 12(6), 1038–1048 (2018)
Patil, P., Yaligar, N., Meena, S.: Comparision of performance of classifiers-svm, rf and ann in potato blight disease detection using leaf images. In: 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–5 . IEEE (2017)
Esmaeel, A.A.: A novel approach to classify and detect bean diseases based on image processing. In: 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), pp. 297–302 . IEEE (2018)
Ramesh, S., Hebbar, R., Niveditha, M., Pooja, R., Shashank, N., Vinod, P.: Plant disease detection using machine learning. In: 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C), pp. 41–45 . IEEE (2018)
Hughes, D., Salathé, M., et al.: An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060 (2015)
Verma, M., Raman, B., Murala, S.: Local extrema co-occurrence pattern for color and texture image retrieval. Neurocomputing 165, 255–269 (2015)
Nagi, R., Tripathy, S.S.: Application of fuzzy logic in plant disease management. In: Fuzzy Expert Systems and Applications in Agricultural Diagnosis, pp. 261–302. IGI Global, Pennsylvania (2020)
Han, J., Ma, K.-K.: Fuzzy color histogram and its use in color image retrieval. IEEE Trans. Image Process. 11(8), 944–952 (2002)
Jayanthi, K., Karthikeyan, M.: Efficient fuzzy color and texture feature extraction technique for content based image retrieval system. In: 2014 IEEE International Conference on Computational Intelligence and Computing Research, pp. 1–5 . IEEE (2014)
Munklang, Y., Auephanwiriyakul, S., Theera-Umpon, N.: A novel fuzzy co-occurrence matrix for texture feature extraction. In: International Conference on Computational Science and Its Applications, pp. 246–257 . Springer (2013)
Mohebali, B., Tahmassebi, A., Meyer-Baese, A., Gandomi, A.H.: Probabilistic neural networks: a brief overview of theory, implementation, and application. Handbook of Probabilistic Models, 347–367 (2020)
Zeinali, Y., Story, B.A.: Competitive probabilistic neural network. Integr. Comput.-Aided Eng. 24(2), 105–118 (2017)
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Nagi, R., Tripathy, S.S. Plant disease identification using fuzzy feature extraction and PNN. SIViP 17, 2809–2815 (2023). https://doi.org/10.1007/s11760-023-02499-x
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DOI: https://doi.org/10.1007/s11760-023-02499-x