仅用RGB图像实现无损番茄果实表型测量，精度超98%。
TomatoScanner: phenotyping tomato fruit based on only RGB image
- 基于边缘增强的YOLO模型与深度估计融合，无需额外设备。
- 在自建数据集上体积误差仅37.06%，宽度误差低至5.63%。
- 适合农业科研与智慧温室场景，算法轻量高效可部署。
在番茄温室中，表型测量对监测作物生长、精准调控环境以提升品质和产量具有重要意义。传统人工测量虽准确但效率低且存在安全风险。现有计算机视觉方法多依赖2D校准、破坏性测量或3D深度相机，前者需额外标定，后者成本高，难以普及。本文提出非接触式番茄果实表型分析方法TomatoScanner，仅需RGB图像输入。首先通过改进的EdgeYOLO模型进行实例分割，结合个体分离与姿态校正预处理；其次利用Depth Pro进行深度估计；最后融合像素与深度特征输出真实表型结果。我们构建了自建番茄表型数据集，测试显示其在宽度、高度、垂直面积和体积上的中位相对误差分别为5.63%、7.03%、-0.64%和37.06%。创新性地引入EdgeAttention、EdgeLoss和EdgeBoost三个模块，使分割精度从0.943提升至0.986，平均边缘误差由5.641%降至2.963%。模型保持轻量化，参数量48.7M，推理速度达76.34 FPS。代码与数据集见：https://github.com/AlexTraveling/TomatoScanner。
原文摘要 · Abstract (English)
In tomato greenhouse, phenotypic measurement is meaningful for researchers and farmers to monitor crop growth, thereby precisely control environmental conditions in time, leading to better quality and higher yield. Traditional phenotyping mainly relies on manual measurement, which is accurate but inefficient, more importantly, endangering the health and safety of people. Several studies have explored computer vision-based methods to replace manual phenotyping. However, the 2D-based need extra calibration, or cause destruction to fruit, or can only measure limited and meaningless traits. The 3D-based need extra depth camera, which is expensive and unacceptable for most farmers. In this paper, we propose a non-contact tomato fruit phenotyping method, titled TomatoScanner, where RGB image is all you need for input. First, pixel feature is extracted by instance segmentation of our proposed EdgeYOLO with preprocessing of individual separation and pose correction. Second, depth feature is extracted by depth estimation of Depth Pro. Third, pixel and depth feature are fused to output phenotype results in reality. We establish self-built Tomato Phenotype Dataset to test TomatoScanner, which achieves excellent phenotyping on width, height, vertical area and volume, with median relative error of 5.63%, 7.03%, -0.64% and 37.06%, respectively. We propose and add three innovative modules - EdgeAttention, EdgeLoss and EdgeBoost - into EdgeYOLO, to enhance the segmentation accuracy on edge portion. Precision and mean Edge Error greatly improve from 0.943 and 5.641% to 0.986 and 2.963%, respectively. Meanwhile, EdgeYOLO keeps lightweight and efficient, with 48.7 M weights size and 76.34 FPS. Codes and datasets: https://github.com/AlexTraveling/TomatoScanner.
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