A self-supervised overlapped multiple weed and crop leaf segmentation approach under complex light condition

One of the most damaging obstacles to crop production is weeds; weeds pose a serious risk to agricultural output. Due to the homogenous morphological properties of weeds, farmers are unable to identify and classify the weed leaves.This study can aid farmers in identifying, categorizing, and quantifying the true extent of crop yield reduction. Computer vision is a sophisticated technique widely used for weed and crop leaf identification and detection in the agricultural field. This work has used three different datasets, such as ‘Deep Weed’, ‘Crop Weed Filed Image Dataset (CWFID), and Multi-view Image Dataset for Weed Detection in Wheat Field (MMIDDWF), and collected 5090 images for training the model. This work uses segmentation techniques for vegetation and semantics for weed object detection. Furthermore, the masked image is distributed as small tiles; often the patches are square tiles, as in 25 × 25 (px), 50 × 50 (px), and 100 × 100 (px). This work has proposed a Deep Learning segmentation model named ‘Pyramid Scene Parsing Network-USegNet’ (PSPUSegNet) for data classification and compared the accuracy of the data from existing segmentation models such as UNet, SegNet, and USegNet. The suggested model, PSPUSegNet, obtained 96.98