Abstract
The quality assessment and sorting millions of fruits as well as vegetables by manual is usually slower. But also costly and cannot give an accurate result. In this research, to increase the quality of food above products were developed by using a vision-based quality inspection and sorting system. The quality assessment and sorting process analyzes taken image for its quality (good). It discards the defected one (bad). The image can be of vegetables or fruits. Four different systems for different food products (Orange, Lemon, Sweet Lime, and Tomato) have been developed. We have used a dataset of one thousand two hundred images which can be used to train as well as test the image systems. All images of 300 in the count. The obtained overall accuracy ranges between 85.0% to 95.00% for Orange, Lemon, Sweet Lime, and Tomato by using soft-computing techniques such as Backpropagation neural network and Probabilistic neural network.
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The authors are much obliged to the Department of Computer Science and Engineering, Manipal Institute of Technology - Manipal Academy of Higher Education, Manipal, INDIA-576104, for providing outstanding lab facilities to succeed in this work.
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Narendra, V.G., Govardhan Hegde, K. (2019). Intelligent System to Evaluate the Quality of Orange, Lemon, Sweet Lime and Tomato Using Back-Propagation Neural-Network (BPNN) and Probabilistic Neural Network (PNN). In: Luhach, A., Jat, D., Hawari, K., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2019. Communications in Computer and Information Science, vol 1075. Springer, Singapore. https://doi.org/10.1007/978-981-15-0108-1_34
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