Classification of paddy crop and weeds using semantic segmentation

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, causing 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 recommend suitable herbicide to farmers, thus contributing to site-specific weed management and sustainable agriculture.

Keywords

• computer vision
• deep learning
• semantic segmentation
• precision agriculture
• weed management