Abstract
In the current situation, agriculture is facing a wide number of problems to address the increasing global population. Also, the plant diseases affect the production and quality of crops. Specifically, plant disease severity identification is the most important problem in the agricultural field which can avoid the excess use of pesticides and minimize the yield loss. In the existing systems, no methodology exists to identify the disease severity and to prescribe the required quantity of medicines to be sprayed. In order to solve this problem, an automated medicine prescription system is proposed in this paper, which takes the images from the uncontrolled environment, enhances, and preprocesses the images received for the identification of disease. Precisely, in the proposed framework, k-means and SVM algorithms are used for clustering and disease identification tasks, respectively. Experimental setup and snapshots of results demonstrate the performance of the proposed system, by means of indicating the severity of the identified disease.
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Sachin, S., Sudarshana, K., Roopalakshmi, R., Suraksha, Nayana, C.N., Deeksha, D.S. (2019). A New Automated Medicine Prescription System for Plant Diseases. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_18
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DOI: https://doi.org/10.1007/978-3-030-00665-5_18
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