Abstract
Automatic detection of the healthy and unhealthy maize plant leaf is a prevalent machine vision learning task and has significant applications in the Food Industry. In this paper, effective machine learning technique for maize leaf healthy and unhealthy classifications based on leaf images that have been presented. This study estimates color feature extraction using RGB mean and standard deviation and the classification, using PNN and KNN methods. A new Five-stage image processing method is presented (including image pre-processing, image segmentation, feature extraction, classification, and performance analysis). The Experimental results show that a small set of RGB color features reach an accuracy of 92.5% and 90% using PNN and KNN classifier respectively, while doing classification the KNN classifier requires more computational time as compared to PNN Classifier.
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Khade, V.C., Patil, S.B., Jadhav, S.B. (2022). Maize Leaf Healthy and Unhealthy Classification Using Image Processing Technique and Machine Learning Classifiers. In: Kumar, A., Mozar, S. (eds) ICCCE 2021. Lecture Notes in Electrical Engineering, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-16-7985-8_75
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DOI: https://doi.org/10.1007/978-981-16-7985-8_75
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