Abstract
The agriculture has a significant part in the Indian economy. Wheat is India’s second highest cultivated crop. Damage in the wheat grain is the main cause for degradation of food quality. In addition, feeding products from spoiled wheat grains for long term induces diseases or leads to malnutrition. Hence, detecting the damaged wheat grains is important. The study aimed to predict the quality of wheat grains. Initially, the wheat grain dataset is taken and preprocessing is performed followed by the segmentation. After this, feature extraction and classification is performed. At last, the performance analysis is carried out. In two class classification, MLP classifies the grains as good or impurities grains. On the other hand, MLP classifies the wheat as healthy, damaged, grain cover, broken grain and foreign particles in five class classification. The performance of the proposed system is analyzed in terms of test loss and accuracy that shows an efficient outcome. A comparative analysis is also performed and the results reveal that the proposed MLP improves classification accuracy by 90.19% over existing methods.
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Sarveswara Rao, P., Lohith, K., Satwik, K., Neelima, N. (2022). Qualitative Classification of Wheat Grains Using Supervised Learning. In: Saraswat, M., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. Lecture Notes on Data Engineering and Communications Technologies, vol 111. Springer, Singapore. https://doi.org/10.1007/978-981-16-9113-3_7
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DOI: https://doi.org/10.1007/978-981-16-9113-3_7
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