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A Systematic Literature Review on Soybean Quality Assessment and Utility of Neural Network in Seed Classification

Author: 
Sachin Sonawane and Dr. Nitin S. Choubey
Subject Area: 
Physical Sciences and Engineering
Abstract: 

Development of a machine vision program to classify among damaged, undamaged, discolored, unmatured, fungi/ diseased soybean kernels can play a vital role in the visual inspection process which is generally performed for grading commercially clean Soybean seed samples. The US standard/ Canadian standard or other recognized standards provide necessary parameters to assess Soybean seeds sample and define its quality while trading Soybean in commercial market. Even today, in many industries, the seed grading process is performed manually and the accuracy of a grade for the given sample is highly de-pendent on human visual inspection. Which most tentatively causes the introduction of errors. To minimize such errors, to shorten the time delay, and increase the accuracy while grading Soybean seeds, an automated system is essentially required. However, till today, very less research work has been carried out on the development of such automated systems for Soybean quality assessment. In that aspect, this paper highlights the key parameters considered as a criteria for assessing quality of Soybean and summarizes the utility of various machine vision techniques in the visual inspection and classification of other type of grains. The selection of parameters and optimization of their values may contribute in improvement of accuracy and shortening of time delay for a designed neural network model.

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