Abstract
Computer vision is a consistent and advanced technique for image processing with the propitious outcome an enormous potential. A computer vision has been strongly adopted in the heterogeneous domain. It is also applied to various domain of agriculture that improves the quality of automation, growth of economy, and the productivity of the nation. Fruits and vegetables quality highly affects the evaluation of quality and export market. Recently, automatic visual inspection becomes very important for grading of fruits applications. In this paper, multiple features with sparse representative classifier and artificial neural network-based automatic grading of apple are done. Firstly pre-processing is done using histogram equalization to smooth the image. Then, fuzzy c-means clustering is used for segmenting the defected region. Secondly, the combination of these seven features, i.e., color moment, color histogram, color correlogram, color coherence vector Zernike moment, moment invariant, and Legendre moments is used to extract the information. Finally, the grading is done using SRC and ANN classifiers and achieve accuracy with above 96%. The agriculture industry achieves the direction of research and support technically for grading of apples using multiple features.
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Bhargava, A., Bansal, A. (2022). Grading of Variety of Bi and Mono-Colored ApplesT. In: Reddy, V.S., Prasad, V.K., Wang, J., Reddy, K.T.V. (eds) Soft Computing and Signal Processing. Advances in Intelligent Systems and Computing, vol 1340. Springer, Singapore. https://doi.org/10.1007/978-981-16-1249-7_35
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