<p>Global agricultural productivity reduced significantly due to frequent outbreaks of plant disease. To reduce crop losses, timely identification is essential. With advancements in artificial intelligence, especially in deep learning, the identification of plant diseases has seen significant improvements, with image segmentation enabling precise localization of disease symptoms. The study reviews segmentation techniques from classical image processing, machine learning, and deep learning-based methods. Further, deep learning methods are broadly divided into semantic, instance, and panoptic segmentation approaches. Key models are identified in these categories, and their relative strengths and weaknesses are discussed. An analysis of 106 studies from 2020 to 2025 is conducted, focusing on their objectives, strategies of implementation, and segmentation methodologies applied. The finding show U-Net and Mask R-CNN are widely adopted architectures because of their robustness across complex visual scenes. Hybrid models that employ multiple segmentation techniques have shown promising results in improving accuracy and diagnosis reliability. Several challenges still exist such as the lack of annotated datasets, high computational overhead, and difficulties in generalizing to unseen conditions. Future research directions are suggested with the aim of surmounting the challenges identified and encouraging further development. This work integrates current spectrum of segmentation techniques while discussing the practical constraints that hamper the development of an efficient architecture in precision agriculture. The study provides a foundational framework for developing scalable and reliable segmentation models for smart agriculture.</p>