Abstract
This article presents a neural network and machine vision-based approach to classify the vegetables as normal or affected. The farmers will have great difficulty if there is a change from one disease control to another. The examination through an open eye to classify the diseases by name is more expensive. The texture and color features are used to identify and classify different vegetables into normal or affected using a neural network and machine vision. The mixture of both the features is proved to be more effective. The results of experiments show that the proposed methodology extensively supports the accuracy in automatic detection of affected and normal vegetables. The applications in packing and grading of vegetables are the outcome of this research article.
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We wish to express gratitude to our beloved Principal, Dr Basavaraj Anami, K.L.E. Institute of Technology, Hubballi for his advice.
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Madgi, M., Danti, A. (2019). Automatic Classification of Normal and Affected Vegetables Based on Back Propagation Neural Network and Machine Vision. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_47
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DOI: https://doi.org/10.1007/978-981-13-9187-3_47
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