International Journal of Innovative Research in Electronics and Communications (IJIREC) Volume 6, Issue 2, 2019, PP 8-16 ISSN 2349-4050 (Online) & ISSN 2349-4042 (Print) DOI: http://dx.doi.org/10.20431/2349-4050.0602002 www.arcjournals.org International Journal of Innovative Research in Electronics and Communications (IJIREC) Page | 8 A Literature Review on Image Processing and Classification Techniques for Agriculture Produce and Modeling of Quality Assessment system for Soybean industry Sample Mr. Sachin Sonawane 1* , Dr. Mohan Awasthy 2 , Dr. Nitin Choubey 3 1 Faculty at NMIMS, Mukesh Patel School of Technology Management and Engineering, Shirpur. 2 Faculty at G. H. Raisoni Institute of Engineering and Technology, Nagpur. 3 Faculty at NMIMS, Mukesh Patel School of Technology Management and Engineering, Shirpur. 1. INTRODUCTION Soybean is the most admired and favored food in the world. It is readily available in all the countries. Because of richness in protein, it is recommended and consumed in the daily food. Variety of value- added edible products can be prepared from Soybean including Soya-Milk, Oil, Pulses etc. for human as well as animals. The countries having major share in Soybean production are United States, Brazil, Argentina, China and India [1]. In international food market, the quality of Soybean is a major concerning order to assure the production of best tertiary product by food industry. To ease the trading process the countries like US, Canada have recommended certain criteria and standards to buyers for grading Soybean quality. Thus, the measurement of Soybean quality is essential as well as an equally important requirement in today‟s scenario to protect the buyers from substandard produce [2]. Accordingly, if the Soybean quality assessment is done manually then the chance of error in measurement is significant and in case it is done with the help of a perfectly trained automatic machine then the chance of error would be minimum. On these aspects, this research work is directed towards the modeling of an automated system for the quality measurements of Soybean Sample [3]. In this research work, authors propose a novel two stage model to addresses the issues related to quality assessment of Soybean. A two-stage model comprised of; first stage to detect the physical Abstract: Soybean, the most popular golden bean of America, is widely known for its fat free food products. Richness in Protein makes it one of the best suggested meals which can be consumed in the form of pulses, oil, food for animals etc. The quality of such products is mainly dependent on the quality of Soybean procured as fresh farm produce. Governments and regional authorities have already defined the standards for quality assessment and grading of Soybean, which are meant to be followed in the commercial market while trading. Presently, visual inspection is the preferred way to conduct physical quality assessment of Soybean and it is performed by an expert person, at the time of procurement of Soybean based on the standards recommended by the buying authority. Physical parameters of Soybean kernel like color, growth corresponding to size, damage, fungi/ disease as well as mixing of other material/ objects in soybean sample influence the grade of that sample during quality assessment. Dependency of this process on human expert usually harms the accuracy in assessment. Therefore, an automated machine is desired to be a suitable solution to address this issue and can benefit in terms of increased accuracy, reliability, and reduced response time. Worldwide researchers are working on designing such automated systems for different type of fruits, grains etc. However, in case of Soybean, up till now we are successful in cleaning, sorting, color detection, and also, through various image processing techniques and algorithms researchers could detect the anomalies present in grain sample. But an automated system for the quality assessment and grading of Soybean according to an International Standard is yet to be implemented. In this paper, we propose a two-stage model for the quality assessment of Soybean; first stage focuses on image processing techniques like Image acquisition, preprocessing and feature extraction Likewise the second stage works on classification of Soybean kernels and sample grading with the help of a machine learning technique on the basis of an International Standard. Keywords: Quality Assessment, Visual Inspection, International Standard, Automated System. *Corresponding Author: Mr. Sachin Sonawane , Faculty at NMIMS, Mukesh Patel School of Technology Management and Engineering, Shirpur.