Versatile and Easy-to-Operate Grading System for High-Grade Table Grapes: Leveraging Deep Learning, Computer Vision, and IoT

Grapes, ranking among the top fruits globally, undergo essential grading processes to ensure quality and market readiness. However, conventional grading methods rely heavily on subjective human expertise, leading to inconsistencies and inefficiencies. To address this, we present a novel grape grading system integrating computer vision, artificial intelligence (AI), and IoT technologies. Our system utilizes deep neural networks (DNNs) based prediction model to accurately grade the grapes, and a sensing station equipped with cameras and weight sensors to capture images and weight data of grape bunches. Our research focuses on Shine Muscat grapes, a prominent variety in Japan. The system implements a multimodal grading approach that combines image and weight information. Our system offers portability, affordability, and operational versatility that prioritizes the safety of the grape bunch. Experimentation with different DNN architectures and input data configurations reveals the superiority of ResNet-18 for grading classification using multiple images from different angles, achieving $85.71 \%$ accuracy. On the other hand, when the weight information is included with the images, ResNet-50 performs the best with $82.86 \%$ accuracy. Additionally, we analyze mispredictions across grade classes, highlighting the model’s challenges in discerning subtle differences between closely ranked grades.