A comparative study of the deep learning-based image segmentation techniques for crop disease detection: Understanding of image segmentation techniques for crop disease detection

Abstract

Productivity in agriculture is a major driver of economic expansion. The fact that plant diseases are so frequent is one of the reasons why plant disease detection is so important in the agricultural industry. Plants suffer severe effects if proper care is not given in this area, which might affect the amount, quality, or productivity of the relevant products. For instance, both living and non-living organisms can cause various diseases in stone fruits and other crops. Early disease patterns and clusters can be identified using computer vision technologies. This work focuses on deep learning-based crop image segmentation research. Firstly, the fundamental concepts and features of deep learning-based crop leaf image segmentation are presented. The future development path is enlarged by outlining the state of the research and providing a summary of crop image segmentation techniques together with an analysis of their own drawbacks. Crop image segmentation based on deep learning has still faced challenges in research, despite recent remarkable advances in crop segmentation. For instance, there are not many crop images in the datasets, the resolution is modest, and the segmentation accuracy is not very great. The real-field criteria cannot be satisfied by the imprecise segmentation findings. With an eye towards the aforementioned issues, a thorough examination of the state-of-the-art deep learning-based crop image segmentation techniques is offered to assist researchers in resolving present issues.