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首页> 外文期刊>Biosystems Engineering >Multispectral-based leaf detection system for spot sprayer application to control citrus psyllids.
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Multispectral-based leaf detection system for spot sprayer application to control citrus psyllids.

机译:基于多光谱的叶片检测系统,适用于点喷器控制柑橘木虱。

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摘要

A leaf detection module was developed for integration with a spot-sprayer system for controlling citrus psyllids. Since psyllids feed only on young citrus leaves, the module was designed to detect young leaves for selective spraying. The module comprised a four-band (570, 670, 750, and 870 nm) active optical sensor, a control box, and a data logger. Commands for operating the sensor and classifying leaves were embedded in a Java program run from a computer. The classification algorithm involved calculating different vegetation indices (using the sensor's spectral reflectance data) and implementing either the Euclidean Distance (ED) or the Matching Measures (MM) classifier. The module was tested under dynamic conditions using young-leaf-detection efficiency ( eta YLD) and leaf-discrimination efficiency ( eta LD) as performance criteria. ED classifier performed better than MM. Both eta YLD and eta LD decreased, with increasing variability, as target distance (TD) increased from 55 to 85 cm. TD of 55 cm gave the best performance ( eta YLD=100%, eta LD=96.1%, and repeatability error, %eRmax=0%). The system test comparing target length at three travel speeds for a leaf target, 15-cm long across the sensor's scanning path, confirmed that detected target length increased with travel speed. This was attributed to the diverging angle of the light beamed on the target, the effective sensing area, and the processing time difference between readings when the target first enters the sensor's field of view and when it has left. Overall, these results show good potential for the leaf detection module to be coupled with a spot sprayer.
机译:开发了一种叶片检测模块,用于与控制柑橘木虱的点喷系统集成。由于木虱仅以柑橘幼叶为食,因此该模块旨在检测幼叶以进行选择性喷雾。该模块包括一个四波段(570、670、750和870 nm)有源光学传感器,一个控制盒和一个数据记录器。用于操作传感器和对叶子进行分类的命令嵌入在从计算机运行的Java程序中。分类算法包括计算不同的植被指数(使用传感器的光谱反射率数据)并实现欧氏距离(ED)或匹配度量(MM)分类器。该模块在动态条件下使用嫩叶检出效率(eta YLD )和叶区分效率(eta LD )作为性能标准进行了测试。 ED分类器的性能优于MM。随着目标距离(TD)从55厘米增加到85厘米,eta YLD 和eta LD 均随着可变性的增加而降低。 55厘米的TD表现最佳(eta YLD = 100%,eta LD = 96.1%,重复性误差%e Rmax = 0%)。系统测试比较了三种目标速度下的目标目标长度,该目标目标是跨传感器扫描路径长15厘米的叶子目标,确认检测到的目标长度随目标速度的增加而增加。这归因于在目标上首次进入传感器视野和离开目标时光束在目标上的发散角,有效感应区域以及读数之间的处理时间差。总体而言,这些结果表明叶片检测模块与点喷器结合的潜力很大。

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