Abstract
Bangladesh extensively depends on agriculture in terms of economy as well as food security for its huge population. For this reason, it is very important to efficiently grow a plant and enhance its yield. Quantity and quality of fruits can degrade due to various diseases that are very much crucial issues. A little research has been conducted for recognition of jackfruit disease to help distant farmers, utmost of who need proper cultivation support. Recognition of jackfruit diseases poses two challenging problems, i.e., detection of disease and classification of disease. In this research, we perform an in-depth investigation of an online automated agro-medical expert system that processes an image captured with handheld devices or mobile phones and recognizes the diseases for helping the distant farmers. Adequate experiment has been performed to prove the efficiency of our proposed system. k-means clustering algorithm is used to extract discriminatory features from segmented out images of diseased jackfruits. After that, we classify the diseases using support vector machines (SVMs). Our classification accuracy is nearly about 90%, which seems to be reliable as well as ensuring by comparing performances with the relevant works.
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Habib, M., Mia, M., Mia, M., Uddin, M., Ahmed, F. (2020). A Computer Vision Approach for Jackfruit Disease Recognition. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3607-6_28
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DOI: https://doi.org/10.1007/978-981-15-3607-6_28
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