Abstract
Farmers in rural India have minimal access to agriculture aspect that can inspect paddy crop images and provide advice. Expert advice responses to queries often reach farmers too late. The disease in paddy crop mostly affects leaf and panicle. The disease that affects the panicle is more severe than the other parts of the paddy crop, as it directly hampers the production. Owing to the infestation of stem borer at the time of ear-head emergence, panicle gets dried and turns white in color, which is known as white ear-head. Automatic detection of white ear-head is done based on high-resolution images captured through mobile camera. In our proposed methodology, we analyze the image of defected panicle by using advanced image processing technique with machine learning to identify whether a panicle is white ear-head affected or a healthy one. This paper executes three machine learning techniques, that is PCA, Gabor filter and ANN, with an accuracy of 85, 90 and 95%, respectively.
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Sethy, P.K., Gouda, S., Barpanda, N., Rath, A.K. (2020). Detection of White Ear-Head of Rice Crop Using Image Processing and Machine Learning Techniques. In: Elçi, A., Sa, P., Modi, C., Olague, G., Sahoo, M., Bakshi, S. (eds) Smart Computing Paradigms: New Progresses and Challenges. Advances in Intelligent Systems and Computing, vol 766. Springer, Singapore. https://doi.org/10.1007/978-981-13-9683-0_10
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DOI: https://doi.org/10.1007/978-981-13-9683-0_10
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