Abstract
India is one of the agricultural countries where 70% of the population depends on agriculture. The agriculture fields, paddy farming play a vital role. The worth and magnitude of farming yields are pretentious through ecological limits like rainfall, temperature and climate parameter, away from managing person beings. One more important organic parameter that influences yields harvest is the insects bytes that can be controlled to better crop yield. This work aims to develop an automated agricultural image-based plant-insect recognition system. The BAG and SURF features are extracted and passed over the classifiers SVM and k-NN. The performance of IR2PI using k-NN is better than IR2PI using SVM with an accuracy of 95%.
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Nagarathinam, T., Elangovan, V.R. (2021). SVM- and K-NN-Based Paddy Plant Insects Recognition. In: Peng, SL., Hsieh, SY., Gopalakrishnan, S., Duraisamy, B. (eds) Intelligent Computing and Innovation on Data Science. Lecture Notes in Networks and Systems, vol 248. Springer, Singapore. https://doi.org/10.1007/978-981-16-3153-5_3
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DOI: https://doi.org/10.1007/978-981-16-3153-5_3
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