2024/12/29 by Thomas A. Ciarfuglia, Ionut M. Motoi, Ciarfuglia, Thomas Alessandro +5
Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #I.2.10 #I.2.9 #I.5.1 #Industrial Vision Systems and Defect Detection #J.3 #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2412.20521
openalex publication_date 2024/12/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In table grape cultivation, harvesting depends on accurately assessing fruit quality. While some characteristics, like color, are visible, others, such as Soluble Solid Content (SSC), or sugar content measured in degrees Brix (°Brix), require specific tools. SSC is a key quality factor that correlates with ripeness, but lacks a direct causal relationship with color. Hyperspectral cameras can estimate SSC with high accuracy under controlled laboratory conditions, but their practicality in field environments is limited. This study investigates the potential of simple RGB sensors under uncontrolled lighting to estimate SSC and color, enabling cost-effective, robot-assisted harvesting. Over the 2021 and 2022 summer seasons, we collected grape images with corresponding SSC and color labels to evaluate algorithmic solutions for SSC estimation, specifically testing for cross-seasonal and cross-device robustness. We propose two approaches: a computationally efficient histogram-based method for resource-constrained robots and a Deep Neural Network (DNN) model for more complex applications. Our results demonstrate high performance, with the DNN model achieving a Mean Absolute Error (MAE) as low as 1.05 °Brix on a challenging cross-device test set. The lightweight histogram-based method also proved effective, reaching an MAE of 1.46 °Brix. These results are highly competitive with those from hyperspectral systems, which report errors in the 1.27--2.20 °Brix range in similar field applications.