Hu Wan
ABSTRACT
Nowadays, the development of agriculture is of great practical significance to all regions and countries. According to statistics, as a large agricultural country, China's annual food loss caused by insufficient pest prevention and control capacity exceeds 30% of the total loss, and its direct economic loss amounts to billions of CNY. The worldwide data is even more huge, so the detection of pests and diseases is particularly important. In the traditional agricultural field, diseases control mainly relies on the accumulated experience of farmers themselves, which is not always stable and requires a long time to form. In this paper we propose a suitable and accurate method for agricultural diseases detection, and finally achieve about 87% accuracy on a relatively large dataset.
- A. N. I. Masazhar, M. M. Kamal. 2017. "Digital image processing technique for palm oil leaf disease detection using multiclass SVM classifier", Proceedings of 2017 IEEE International Conference on Smart Instrumentation, Measurement and Applications, pp. 1--6.Google Scholar
Cross Ref
- L. R. Wei, J. Yue, Z. B. Li, G. J. Kou, H. P. Qu. 2017. "Multi-classification Detection Method of Plant Leaf Disease Based on Kernel Function SVM", Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, vol. 48, pp. 166--171.Google Scholar
- S. Ramesh, R. Hebbar, Niveditha M, Pooja R, Prasad Bhat N, Shashank N, P. V. Vinod. 2018. "Plant disease detection using machine learning", Proceedings of 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control, pp. 41--45.Google ScholarCross Ref
- J. K. Kamble. 2018. "Plant Disease Detector", Proceedings of 2018 International Conference On Advances in Communication and Computing Technology, pp. 97--101.Google ScholarCross Ref
- P. Sharma, Y. P. S. Berwal, W. Ghai. 2018. "KrishiMitr(Farmer's Friend): Using Machine Learning to Identity Diseases in Plants", Proceedings of 2018 IEEE International Conference on Internet of Things and Intelligence System, pp. 29--34.Google ScholarCross Ref
- A. Devaraj, K. Rathan, S. Jaahnavi, K. Indira. 2019. "Identification of Plant Disease using Image Processing Technique", Proceedings of Internation Conference on Communication and Signal Processing, pp. 0749--0753.Google ScholarCross Ref
- D. A. Bashish, M. Braik, S. Bani-Ahmad. 2010. "A Framework for Detection and Classification of Plant Leaf and Stem Diseases", Proceedings of 2010 International Conference on Signal and Image Processing, pp. 113--117.Google ScholarCross Ref
- A. Camargo, J. S. Smith. 2009. "Image pattern classification for the identification of disease causing agents in plants", Computers and Electronics in Agriculture, vol. 66, pp. 121--125.Google ScholarDigital Library
Index Terms
- Plant Disease Classification Using Deep Learning Methods
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