Table Grape

Table grape research focuses on improving efficiency and yield in viticulture, primarily through advancements in automated image analysis and robotic systems. Current efforts leverage deep learning models, such as convolutional neural networks (CNNs) and graph neural networks (GNNs), for tasks like disease detection, fruit localization, and yield estimation, often incorporating multimodal data from various sensors. These technological advancements aim to address challenges in precision agriculture, including optimizing resource allocation and reducing labor costs, ultimately enhancing the sustainability and profitability of table grape production.