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Mapping coffee yield with computer vision
Precision Agriculture ( IF 6.2 ) Pub Date : 2022-06-01 , DOI: 10.1007/s11119-022-09924-0
Helizani Couto Bazame, José Paulo Molin, Daniel Althoff, Maurício Martello, Lucas De Paula Corrêdo

Yield maps guide investigations into the causes of spatial and temporal variations in crop yields. The objective of this work was to implement an algorithm based on computer vision to quantify the number of coffee fruits and to build yield maps. Data were collected in two areas of a commercial Arabica coffee (Coffea arabica) plantation. The images of the coffee fruits were taken from 90 videos acquired during the harvest. The You Only Look Once version 4 (YOLOv4) model was used for the detection and counting of coffee fruits. Geographic coordinates were registered at the same time the videos were recorded and associated with video frames. The number of coffee fruit detections for each frame was converted into yield considering the average distance covered by the harvester and the distance between coffee rows of 4 m. The yield maps were interpolated from the video frames’ respective geographic coordinates. The YOLOv4 model had a mean average precision of 83.5%. The yield map estimated from the detections obtained by the computer vision model was able to explain 81% of the variance of the reference yield map. The main contributions of the proposed methodology are its low implementation cost and the independence of specific brands of coffee harvesters for the implementation of the image capture structure. Another advantage of this methodology is the possibility of saving raw data from the entire harvest. Thus, its user can pursue further improvements for the model in the future and validate its performance under different scenarios.



中文翻译:

用计算机视觉绘制咖啡产量

产量地图指导调查作物产量空间和时间变化的原因。这项工作的目的是实现一种基于计算机视觉的算法来量化咖啡果实的数量并构建产量图。在商业阿拉比卡咖啡( Coffea arabica)的两个区域收集数据) 种植园。咖啡果实的图像取自收获期间采集的 90 个视频。You Only Look Once 第 4 版 (YOLOv4) 模型用于检测和计数咖啡果实。地理坐标在录制视频的同时注册并与视频帧相关联。考虑到收割机覆盖的平均距离和咖啡行之间的距离为 4 m,将每帧的咖啡果实检测数转换为产量。产量图是从视频帧各自的地理坐标插值得到的。YOLOv4 模型的平均精度为 83.5%。从计算机视觉模型获得的检测估计的产量图能够解释参考产量图的 81% 的方差。所提出的方法的主要贡献是其低实施成本和特定品牌的咖啡收割机对于实施图像捕获结构的独立性。这种方法的另一个优点是可以保存整个收获的原始数据。因此,其用户可以在未来对模型进行进一步改进,并验证其在不同场景下的性能。

更新日期:2022-06-02
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