ISSN 2394-5125
 

Research Article 


DETECTION OF AGRO BASED PRODUCTS QUALITY USING CONVOLUTIONAL NEURAL NETWORKS

Badagala.Vasantha Rani, A. Maheshwar Rao, Dr. E. Laxmi Lydia, Ch. Usha Kumari, Kanagaraj Narayanasamy, M. Ilayaraja.

Abstract
With the advancement in computer technology, image processing techniques are widely applied to food safety and quality. Images are the main source of data and information in agricultural science. The undertaking of organic product reviewing is in dispensable in the horticultural business in light of the fact that there is an incredible interest for great natural products in the market. Be that as it may, natural product reviewing by people is wasteful, work serious and inclined to mistake the mechanized reviewing framework speeds up the hour of preparing as well as limits blunders. In this paper we mainly focused to detect the quality of fruits through computer aided technologies like convolution neural networks. This was implemented to get the accurate and direct results where the user have no intention to waste the time and money. The system should be able to accurately detect and identify different kinds of fruits. Thus the developed system will help detect the fruit size and grade the fruits automatically. Grading is done based on the fruit size, shape and color by applying keras methods and algorithms to achieve better accuracy

Key words: cnn, keras ,ctta


 
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How to Cite this Article
Pubmed Style

Badagala.Vasantha Rani, A. Maheshwar Rao, Dr. E. Laxmi Lydia, Ch. Usha Kumari, Kanagaraj Narayanasamy, M. Ilayaraja. DETECTION OF AGRO BASED PRODUCTS QUALITY USING CONVOLUTIONAL NEURAL NETWORKS. JCR. 2020; 7(10): 1788-1795. doi:10.31838/jcr.07.10.319


Web Style

Badagala.Vasantha Rani, A. Maheshwar Rao, Dr. E. Laxmi Lydia, Ch. Usha Kumari, Kanagaraj Narayanasamy, M. Ilayaraja. DETECTION OF AGRO BASED PRODUCTS QUALITY USING CONVOLUTIONAL NEURAL NETWORKS. http://www.jcreview.com/?mno=112510 [Access: August 18, 2021]. doi:10.31838/jcr.07.10.319


AMA (American Medical Association) Style

Badagala.Vasantha Rani, A. Maheshwar Rao, Dr. E. Laxmi Lydia, Ch. Usha Kumari, Kanagaraj Narayanasamy, M. Ilayaraja. DETECTION OF AGRO BASED PRODUCTS QUALITY USING CONVOLUTIONAL NEURAL NETWORKS. JCR. 2020; 7(10): 1788-1795. doi:10.31838/jcr.07.10.319



Vancouver/ICMJE Style

Badagala.Vasantha Rani, A. Maheshwar Rao, Dr. E. Laxmi Lydia, Ch. Usha Kumari, Kanagaraj Narayanasamy, M. Ilayaraja. DETECTION OF AGRO BASED PRODUCTS QUALITY USING CONVOLUTIONAL NEURAL NETWORKS. JCR. (2020), [cited August 18, 2021]; 7(10): 1788-1795. doi:10.31838/jcr.07.10.319



Harvard Style

Badagala.Vasantha Rani, A. Maheshwar Rao, Dr. E. Laxmi Lydia, Ch. Usha Kumari, Kanagaraj Narayanasamy, M. Ilayaraja (2020) DETECTION OF AGRO BASED PRODUCTS QUALITY USING CONVOLUTIONAL NEURAL NETWORKS. JCR, 7 (10), 1788-1795. doi:10.31838/jcr.07.10.319



Turabian Style

Badagala.Vasantha Rani, A. Maheshwar Rao, Dr. E. Laxmi Lydia, Ch. Usha Kumari, Kanagaraj Narayanasamy, M. Ilayaraja. 2020. DETECTION OF AGRO BASED PRODUCTS QUALITY USING CONVOLUTIONAL NEURAL NETWORKS. Journal of Critical Reviews, 7 (10), 1788-1795. doi:10.31838/jcr.07.10.319



Chicago Style

Badagala.Vasantha Rani, A. Maheshwar Rao, Dr. E. Laxmi Lydia, Ch. Usha Kumari, Kanagaraj Narayanasamy, M. Ilayaraja. "DETECTION OF AGRO BASED PRODUCTS QUALITY USING CONVOLUTIONAL NEURAL NETWORKS." Journal of Critical Reviews 7 (2020), 1788-1795. doi:10.31838/jcr.07.10.319



MLA (The Modern Language Association) Style

Badagala.Vasantha Rani, A. Maheshwar Rao, Dr. E. Laxmi Lydia, Ch. Usha Kumari, Kanagaraj Narayanasamy, M. Ilayaraja. "DETECTION OF AGRO BASED PRODUCTS QUALITY USING CONVOLUTIONAL NEURAL NETWORKS." Journal of Critical Reviews 7.10 (2020), 1788-1795. Print. doi:10.31838/jcr.07.10.319



APA (American Psychological Association) Style

Badagala.Vasantha Rani, A. Maheshwar Rao, Dr. E. Laxmi Lydia, Ch. Usha Kumari, Kanagaraj Narayanasamy, M. Ilayaraja (2020) DETECTION OF AGRO BASED PRODUCTS QUALITY USING CONVOLUTIONAL NEURAL NETWORKS. Journal of Critical Reviews, 7 (10), 1788-1795. doi:10.31838/jcr.07.10.319