A Comparative Study of the Deep Learning Based Image Segmentation Techniques for Fruit Disease Detection

Abstract

Agriculture’s productivity is a key factor in economic growth. One of the reasons that disease detection in plants is crucial in the world of agriculture is that diseases in plants are a fairly common occurrence. If sufficient care is not taken in this region, plants suffer major consequences, which have an impact on the quality, quantity, or productivity of the corresponding 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 few crop images in the datasets, the resolution is modest, and the segmentation accuracy is not 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.