Improving Grape Quality in Viticulture with Autonomous Robots Using Deep Learning and Sensor Fusion

The pursuit of enhancing grape quality in viticulture has increased with the emergence of autonomous robots using advanced artificial intelligence (AI) methods. This research introduces an innovative methodology that combines deep learning (DL) and sensor fusion with Region-Based Convolutional Neural Networks (R-CNN) to enhance precision viticulture. Autonomous robots with multispectral and RGB cameras roam vineyards independently, taking high-resolution images and environmental data. The R-CNN algorithm analyses this data to precisely detect grape diseases, evaluate fruit ripeness, and monitor overall vineyard health. The technology employs sensor fusion to integrate visual data with environmental characteristics, enhancing predictions and insights on grape quality. Field investigations indicate substantial improvements in grape output and quality with quick interventions informed by automated evaluations. Findings highlight the capability of integrating robots with DL techniques to transform vineyard management, resulting in sustainable practices and improved production efficiency. The R-CNN-based robotic system demonstrated 95% accuracy in evaluating grape quality, improving efficiency and reducing the need for manual inspections.