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Satyam Srivastava   Mr.  Research or Laboratory Scientist 
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Satyam Srivastava published an article in October 2017.
Top co-authors
Shashikant Sadistap

8 shared publications

Academy of Scientific and Innovative Research (AcSiR), CSIR-CEERI, Pilani, Jhunjhunu, India

5
Publications
18
Reads
2
Downloads
6
Citations
Publication Record
Distribution of Articles published per year 
(2014 - 2017)
Total number of journals
published in
 
3
 
Publications
Article 7 Reads 2 Citations Non-destructive sensing methods for quality assessment of on-tree fruits: a review Satyam Srivastava, Shashikant Sadistap Published: 11 October 2017
Journal of Food Measurement and Characterization, doi: 10.1007/s11694-017-9663-6
DOI See at publisher website
Article 4 Reads 3 Citations Development of a low cost optimized handheld embedded odor sensing system (HE-Nose) to assess ripeness of oranges Satyam Srivastava, Shashikant Sadisatp Published: 18 August 2015
Journal of Food Measurement and Characterization, doi: 10.1007/s11694-015-9270-3
DOI See at publisher website
Article 6 Reads 0 Citations Quality assessment of commercial bread samples based on breadcrumb features and freshness analysis using an ultrasonic m... Satyam Srivastava, Saikrishna Vaddadi, Shashikant Sadistap Published: 19 June 2015
Journal of Food Measurement and Characterization, doi: 10.1007/s11694-015-9261-4
DOI See at publisher website
Article 0 Reads 1 Citation A Novel Vision Sensing System for Tomato Quality Detection Satyam Srivastava, Sachin Boyat, Shashikant Sadistap Published: 03 September 2014
International Journal of Food Science, doi: 10.1155/2014/184894
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Producing tomato is a daunting task as the crop of tomato is exposed to attacks from various microorganisms. The symptoms of the attacks are usually changed in color, bacterial spots, special kind of specks, and sunken areas with concentric rings having different colors on the tomato outer surface. This paper addresses a vision sensing based system for tomato quality inspection. A novel approach has been developed for tomato fruit detection and disease detection. Developed system consists of USB based camera module having 12.0 megapixel interfaced with ARM-9 processor. Zigbee module has been interfaced with developed system for wireless transmission from host system to PC based server for further processing. Algorithm development consists of three major steps, preprocessing steps like noise rejection, segmentation and scaling, classification and recognition, and automatic disease detection and classification. Tomato samples have been collected from local market and data acquisition has been performed for data base preparation and various processing steps. Developed system can detect as well as classify the various diseases in tomato samples. Various pattern recognition and soft computing techniques have been implemented for data analysis as well as different parameters prediction like shelf life of the tomato, quality index based on disease detection and classification, freshness detection, maturity index detection, and different suggestions for detected diseases. Results are validated with aroma sensing technique using commercial Alpha Mos 3000 system. Accuracy has been calculated from extracted results, which is around 92%.
Article 1 Read 1 Citation A Robust Machine Vision Algorithm Development for Quality Parameters Extraction of Circular Biscuits and Cookies Digital... Satyam Srivastava, Sachin Boyat, Shashikant Sadistap Published: 01 January 2014
Journal of Food Processing, doi: 10.1155/2014/376360
DOI See at publisher website ABS Show/hide abstract
Biscuits and cookies are one of the major parts of Indian bakery products. The bake level of biscuits and cookies is of significant value to various bakery products as it determines the taste, texture, number of chocolate chips, uniformity in distribution of chocolate chips, and various features related to appearance of products. Six threshold methods (isodata, Otsu, minimum error, moment preserving, Fuzzy, manual method, and k-mean clustering) have been implemented for chocolate chips extraction from captured cookie image. Various other image processing operations such as entropy calculation, area calculation, parameter calculation, baked dough color, solidity, and fraction of top surface area have been implemented for commercial KrackJack biscuits and cookies. Proposed algorithm is able to detect and investigate about various defects such as crack and various spots. A simple and low cost machine vision system with improved version of robust algorithm for quality detection and identification is envisaged. Developed system and robust algorithm have a great application in various biscuit and cookies baking companies. Proposed system is composed of a monochromatic light source, and USB based 10.0 megapixel camera interfaced with ARM-9 processor for image acquisition. MATLAB version 5.2 has been used for development of robust algorithms and testing for various captured frames. Developed methods and procedures were tested on commercial biscuits resulting in the specificity and sensitivity of more than 94% and 82%, respectively. Since developed software package has been tested on commercial biscuits, it can be programmed to inspect other manufactured bakery products.
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