Tomato Segmentation

Tomato segmentation, the automated identification and delineation of individual tomatoes within an image, is crucial for optimizing agricultural practices like harvesting, grading, and yield estimation. Current research heavily utilizes deep learning, employing convolutional neural networks, transformers, and diffusion models to achieve accurate segmentation, even in challenging scenarios with occlusion and varying lighting conditions. This work is driven by the need for efficient and precise automation in agriculture, leading to the development of novel datasets and improved algorithms that surpass previous state-of-the-art performance. The resulting advancements promise significant improvements in crop management and resource optimization.