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An Intelligent System for Fresh Bitter Gourd Detection Using CNN

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dc.contributor.author Tasnim, Zarin
dc.date.accessioned 2021-12-08T08:45:11Z
dc.date.available 2021-12-08T08:45:11Z
dc.date.issued 2021-09-20
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6539
dc.description.abstract Agriculture development is not only a normal development sector but also a vital sector all over the world. Convolution Neural Network is one of the most advanced algorithms in Machine Learning. In my study, I have built up a strong relationship between agriculture and Image processing system, bitter gourd freshness detection and automation system using multi-layer automation process. I have used 5*3 training layers for the dataset and relevant output process. In this study, I show 4 types of output like Fresh, Moderate, Wrong and rotten bitter gourd. After analyzing data and method implementation I get 91.56% model accuracy which is better than the other image processing algorithm. In the modern era agriculture development is the highly contribute field of food security. This study will allow farmers to choose the proper crop in the right market condition, which will play a key role in strengthening the economy of the country. Technology on the other hand is a huge blessing in people's lives. In today's world, the introduction of information technology in agriculture has led to great improvements in this field. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Sustainable agriculture en_US
dc.subject Technology assessment en_US
dc.title An Intelligent System for Fresh Bitter Gourd Detection Using CNN en_US
dc.type Other en_US


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