A Machine Learning Approach to Classification of Okra

Different machine learning techniques have been used for image classification purposes in agriculture. They can be applied to either roots, leaves or plants' detection and classification in order to assist farmer's tasks. This paper aims to propose alternatives or solutions to post-harvest...

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Bibliographic Details
Published in:	2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) pp. 843 - 847
Main Authors:	Karyemsetty, Nagarjuna, Rudra, Poojitha, Yaswanth, Gollapudi, Nikhitha, Gannamaneni, Kodali, Navya Sri, Prasad, Chitturi
Format:	Conference Proceeding
Language:	English
Published:	IEEE 20-01-2022
Subjects:	Agricultural Cooperatives Agriculture Background noise classify products Convolutional neural networks Image color analysis Manuals noise cancellation patterns post-harvest Shape Task analysis
Online Access:	Get full text
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Summary:	Different machine learning techniques have been used for image classification purposes in agriculture. They can be applied to either roots, leaves or plants' detection and classification in order to assist farmer's tasks. This paper aims to propose alternatives or solutions to post-harvest manual classification of okras by Indian farmers in Okinawa. Thus, we implement Deep Learning to classify okras into categoriespre-established by the Japan Agricultural Cooperatives. The classification features of okras in this study are their length and shape, and they classified into two: Class A and B. A set of pre-processing layers such as background noise cancellation, gray scaling and enhancement, image resizing and reconstruction are utilized to provide a higher detection rate. Moreover, a Convolutional Neural Network (CNN) is implemented to detect the patterns and predict the outputs.
DOI:	10.1109/ICSSIT53264.2022.9716357