[论文解读] Machine Vision-Based Crop-Load Estimation Using YOLOv8

Dawood Ahmed, Ranjan Sapkota+2 more|arXiv (Cornell University)|Apr 26, 2023

Smart Agriculture and AI被引用 17

一句话总结

该论文提出基于 YOLOv8 的机器视觉系统，使用 RGB-D 数据识别苹果树干和分支，估算分支直径和肢体截面积，并预测每个分支的作物负载以用于机器人修剪和疏果。它报告与地面实测对齐的误差，并在商业果园中证明可行性。

ABSTRACT

Labor shortages in fruit crop production have prompted the development of mechanized and automated machines as alternatives to labor-intensive orchard operations such as harvesting, pruning, and thinning. Agricultural robots capable of identifying tree canopy parts and estimating geometric and topological parameters, such as branch diameter, length, and angles, can optimize crop yields through automated pruning and thinning platforms. In this study, we proposed a machine vision system to estimate canopy parameters in apple orchards and determine an optimal number of fruit for individual branches, providing a foundation for robotic pruning, flower thinning, and fruitlet thinning to achieve desired yield and quality.Using color and depth information from an RGB-D sensor (Microsoft Azure Kinect DK), a YOLOv8-based instance segmentation technique was developed to identify trunks and branches of apple trees during the dormant season. Principal Component Analysis was applied to estimate branch diameter (used to calculate limb cross-sectional area, or LCSA) and orientation. The estimated branch diameter was utilized to calculate LCSA, which served as an input for crop-load estimation, with larger LCSA values indicating a higher potential fruit-bearing capacity.RMSE for branch diameter estimation was 2.08 mm, and for crop-load estimation, 3.95. Based on commercial apple orchard management practices, the target crop-load (number of fruit) for each segmented branch was estimated with a mean absolute error (MAE) of 2.99 (ground truth crop-load was 6 apples per LCSA). This study demonstrated a promising workflow with high performance in identifying trunks and branches of apple trees in dynamic commercial orchard environments and integrating farm management practices into automated decision-making.

研究动机与目标

• 通过实现自动修剪、疏果和采收工作流来缓解果品生产中的劳动力短缺。
• 开发基于视觉的方法以识别树干/分支并估算影响果载量的几何参数。
• 创建一种将传感器数据与机器学习结合的工作流，以为自动化系统提供作物负载决策信息。

提出的方法

• 使用 Microsoft Azure Kinect DK 的 RGB-D 数据获取颜色和深度信息。
• 在休眠期应用基于 YOLOv8 的实例分割来识别树干和分支。
• 对分割结构进行主成分分析以估算分支直径和方向。
• 根据估算的分支直径计算肢体截面积（LCSA），以作为作物负载估算的输入。
• 基于 LCSA 估算分段分支的目标作物负载，并对地面实测值报告 MAE。

实验结果

研究问题

• RQ1 基于 YOLOv8 的实例分割是否能够在商业苹果园的休眠期准确识别树干和分支？
• RQ2 如何从 RGB-D 数据中较好地估算分支直径和方向，并用于计算 LCSA 以进行作物负载估算？
• RQ3 与地面实测相比，预测的每个分支作物负载的准确性（MAE）如何？
• RQ4 所提工作流在真实果园环境中是否足够鲁棒，能够与自动化修剪和疏果平台集成？
• RQ5 在真实环境中可以实现的实际性能指标（如深度精度、分割性能）有哪些？

主要发现

• 分支直径估计的平均误差为 2.08 mm（直径估计误差）。
• 每个分支的作物负载估算的平均绝对误差为 2.99 个苹果，其中地面实测为每个 LCSA 6 个苹果。
• 该方法在动态的商业果园环境中实现了对树干和分支的高性能识别。
• 该工作流支持将农场管理实践整合到自动化的修剪和疏果决策中。
• 该系统在实际环境中证明了使用 RGB-D 数据和 YOLOv8 进行与作物负载相关决策的可行性。