Agricultural Leaf Disease Segmentation Approaches Using Deep Learning

Image segmentation is a fundamental computer vision technique that divides a digital image into distinct regions or segments, each representing different objects or parts of objects. This process enables computers to understand and analyze visual content by detecting and isolating specific areas. Image segmentation is widely used across various industries, such as medical imaging, autonomous driving, and agriculture, where it assists in disease detection, yield estimation, and crop monitoring. By accurately segmenting images, advanced algorithms can offer precise insights and improve decision-making processes in numerous applications. As a result, we carried out an in-depth assessment of deep learning-based image segmentation techniques, highlighting their developments and possible applications in agriculture. There are several types of deep learning-based image segmentation methods utilised in agriculture. This survey explores the various deep learning-based image segmentation methods employed to identify leaf diseases, highlighting their development, applications, and potential for future advancements in agricultural technology.