Authors - Akshat Vashisht, Ishika Tekade, Juhi Shah, Aniket Sawarn, Deependra Singh Yadav, Palash Sontakke, Rajkumar Patil Abstract - Segmenting grape leaf stress condition accurately is a critical step in precision agriculture, as it enables early detection and treatment to mitigate crop losses. In this research, we propose a novel approach leveraging the Segment Anything Model 2 (SAM-2) for precise segmentation of stress condition regions on grape leaves. SAM-2 is a foundation model for promptable visual segmentation in images and videos. This model can generate high-quality masks with minimal user input, which makes it an ideal tool for such tasks. The SAM-2 model was tested on field images and we achieved an accuracy of nearly 70% without fine-tuning. Experimental results demonstrate that SAM-2 outperforms traditional segmentation models like U2Net and V-net. Data augmentation can improve the performance of SAM-2, especially in challenging tasks such as detecting early-stage leaf spots or stress condition symptoms in overlapping leaves. Techniques such as rotation, adjusting brightness, and scaling, can simulate different conditions, balance the dataset and improve generalization. This helps SAM-2 to adapt to different scenarios and improve its ability to detect complex patterns. This shows the potential of SAM-2 in agricultural applications and provides a framework that can be integrated into advanced plant monitoring systems. By automating the segmentation process with minimal user intervention, SAM-2 significantly reduces the labour-intensive task of manual stress condition detection, thus saving time and resources in agricultural operations. Integrating SAM-2 into the grape leaf stress condition detection pipeline enhances the precision and reliability of stress condition identification systems. Furthermore, its adaptability across different segmentation tasks provides a foundation for scalable and automated plant health monitoring systems. This study establishes SAM-2 as a transformative tool for advancing sustainable farming practices and precision agriculture.
