Classification of paddy crop and weeds using semantic segmentation

Kamath, Radhika and Balachandra, Mamatha and Vardhan, Amodini and Maheshwari, Ujjwal (2022) Classification of paddy crop and weeds using semantic segmentation. Cogent Engineering, 9 (1). ISSN 2331-1916

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Abstract

Weeds are unwanted plants in a farm field and have harmful effects on the crops. Sometimes rigorous weeds bring down the crop yield significantly, caus�ing huge losses to farmers. A prevalent method of controlling weeds is the use of chemical herbicides. These herbicides are known to cause harmful effects on our environment. One of the ways to control the ill effects of herbicides is to follow the Site-Specific Weed Management (SSWM). Site-specific weed management is to use the right herbicide for the right amount on agricultural land. This paper investigates a semantic segmentation approach to classify two types of weeds in paddy fields, namely sedges and broadleaved weeds. Three semantic segmentation models such as SegNet, Pyramid Scene Parsing Network (PSPNet), and UNet were used in the segmentation of paddy crop and two types of weeds. Promising results with an accuracy over 90% has been obtained. We believe that this can be used to recom�mend suitable herbicide to farmers, thus contributing to site-specific weed man�agement and sustainable agriculture.

Item Type: Article
Uncontrolled Keywords: computer vision; deep learning; semantic segmentation; precision agriculture; weed management
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Depositing User: MIT Library
Date Deposited: 08 Apr 2022 10:30
Last Modified: 08 Apr 2022 10:30
URI: http://eprints.manipal.edu/id/eprint/158544

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