Abstract
Udupi Jasmine is one of the four GI-tagged flower varieties of Karnataka state. Karnataka is the second largest producer of jasmine flowers in India. One major issue in jasmine cultivation is maintaining the quality of flowers. It is estimated that the labor cost for plucking and segregating the flower contributes 28% of the overall establishment cost. This work focuses to reduce the labor time involved in process of partitioning the jasmine flowers into normal and defected based on their quality. Automated jasmine classification makes use of image processing and machine learning methods for flower quality estimation. The acquired jasmine image is preprocessed, segmented and three different types of features are extracted. These feature vectors are normalized and fused to form one single feature vector for about 500 images in the dataset. The jasmine flowers are classified with a novel Convex-Hull and Geometry-based Support Vector Machine (SVM) classifier. The classification performance is estimated with various measures like sensitivity, specificity, accuracy and F1-score. The investigation results are quantified and compared with the other existing classifiers.
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Bhat, S.S., Nagaraja, Revankar, S., Chethan Kumar, B., Dinesha (2023). An SVM-Based Approach for the Quality Estimation of Udupi Jasmine. In: Noor, A., Saroha, K., Pricop, E., Sen, A., Trivedi, G. (eds) Proceedings of Emerging Trends and Technologies on Intelligent Systems. Advances in Intelligent Systems and Computing, vol 1414. Springer, Singapore. https://doi.org/10.1007/978-981-19-4182-5_27
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DOI: https://doi.org/10.1007/978-981-19-4182-5_27
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