An unmanned ground-based canopy density measurement system to support precision spraying in apple orchards was developed to precisely apply pesticides to orchard canopies. The automated measurement system was comprised of a light detection and ranging (LiDAR) sensor, an interface box for data transmission, and a laptop computer. A data processing and analysis algorithm was developed to measure point cloud indices from the LiDAR sensor to describe the distribution of tree canopy density within four sections according to the position of the trellis wires. Experiments were conducted in two orchard sites, one with GoldRush (larger trees) and the other one with Fuji (smaller trees) apple trees. Tree leaves were counted manually from each section separated by trellis wires. Field evaluation results showed a strong correlation of 0.95 (R² = 89.30%) between point cloud data and number of leaves for the Fuji block and a correlation of 0.82 (R² = 67.16%) was obtained for the GoldRush block. A strong correlation of 0.98 (R² = 95.90%) was achieved in the relationship between canopy volume and number of leaves. Finally, a canopy density map was generated to provide a graphical view of the tree canopy density in different sections. Since accurate canopy density information was computed, it is anticipated that the developed prototype system can guide the sprayer unit for reducing excessive pesticide use in orchards.

开发了一种无人驾驶的地面冠层密度测量系统，以支持在苹果园中进行精确喷洒，以将农药精确地施用于果园冠层。自动化测量系统包括一个光检测和测距（LiDAR）传感器，一个用于数据传输的接口盒和一台笔记本电脑。开发了一种数据处理和分析算法，用于测量来自LiDAR传感器的点云指数，以根据网格线的位置描述四部分内树冠密度的分布。在两个果园地点进行了实验，一个地点使用GoldRush（大树），另一个使用富士（小树）苹果树。从由网格线分开的每个部分手动计数树叶。现场评估结果显示0.95（R 点云数据和Fuji块的叶数之间的关系为² = 89.30％ ，GoldRush块的相关系数为0.82（R ² = 67.16％）。 冠层体积与叶片数之间的关系达到0.98（R ² = 95.90％）的强相关性。最后，生成树冠密度图以提供不同部分中树冠密度的图形视图。由于计算了准确的树冠密度信息，因此可以预期，开发的原型系统可以引导喷雾器单元减少果园中过多的农药使用。