Abstract
In this research work, a novel content-based image retrieval (CBIR) system is developed to classify the tomato plant leaf diseases. The proposed CBIR system uses color, shape, and texture features of the tomato leaf to classify similar images. The HSV color histogram is used to extract color features and Fourier descriptors provide shape feature, in the form of the contour of the region of interest. In order to consider global texture features, a variant of local binary pattern (LBP) called completed LBP (CLBP) is utilized. Furthermore, feature fusion of all color, shape, and texture properties is done to increase accuracy. Based on this feature vector, classification of disease is done using a supervised learning technique called support vector machine (SVM). The analysis of different kernels like linear, RBF, polynomial, and hyper-parameter optimization showed that linear kernel is best suitable. In regards to classification, the mean accuracy of 97.3% is achieved in the linear SVM model by five-fold cross-validation.
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Yogeswararao, G., Malmathanraj, R., Palanisamy, P. (2022). An Improved Content-Based Image Retrieval System for Tomato Leaf Disease Classification. In: Kumar, A., Zurada, J.M., Gunjan, V.K., Balasubramanian, R. (eds) Computational Intelligence in Machine Learning. Lecture Notes in Electrical Engineering, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-16-8484-5_18
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DOI: https://doi.org/10.1007/978-981-16-8484-5_18
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