Patch-wise weed coarse segmentation mask from aerial imagery of sesame crop

Sesame (Sesamum indicum L.) is an important commercial food crop and there are numerous weed species that reduce their yield. Drone-assisted autonomous aerial herbicide spraying on sesame fields intends to prevent weeds from limiting agricultural productivity. Additionally, agrochemical application quantity and human health are safeguarded using aerial spraying. Autonomous systems would require characteristics to differentiate between crop, weed, and background for accurate and selective spray application. Datasets from aerial sesame fields have been gathered for this study to extract weed and crop profiles in the area. Agrocam, a camera sensor made to track crop health, was used to record the dataset. We have created an intelligent SesameWeedNet-2 convolutional neural network (CNN) and a patch image-based classification method. The eight convolutional layers in the tiny model allow it to operate more quickly and precisely on small patch images. Our approach divides photos into small patch images of 31 × 31 pixels. After that, the proposed small CNN is trained and tested on these small patch images. Two critical components of our methodology are dataset grouping and patch-based model ensemble. Our algorithm suggests classifying the dataset based on the vegetation visible in the small patch images to improve categorization outcomes. Our suggested strategy has attained an accuracy of up to 96.7 %. We have made comparisons among our suggested patch-based technique with pixel-level semantic segmentation. The patch-based method wins for time efficiency even though the pixel-level method provides a fine outline of the crop and weed classes but is computationally expensive. Our approach has been put to the test in both dry and wet soil conditions, as well as at various growth phases. We are aware of no prior attempts to categorize and treat crops and weeds in sesame fields at the post-emergence stage. A complete deep learning-based patch-based technique and an aerial sesame-weed dataset contribute to this study to categorize weeds in sesame fields under varying lighting circumstances.