冬油菜田杂草探测光谱传感器设计与应用

Design and application of spectrum sensor for weed detection used in winter rape field

杂草的精确识别是对靶施药和自动化机械除草的关键前提,基于光谱分析技术的光谱传感器可以实现快速、无损的杂草识别。该文以冬油菜苗期杂草为研究对象,根据试验选取的4个特征波长点(595、710、755和950 nm),设计了一种能自动识别杂草的光谱传感器。根据光学系统原理和田间实际操作要求,提出了该光谱传感器的结构设计方案,选择了合适的光学器件,并开发了光谱传感器信号调理电路。对光谱传感器进行了标定和试验验证,根据便携式光谱仪和光谱传感器在4个波长下的测量结果建立了相应的标定方程,方程的决定系数分别为0.799、0.812、0.892和0.867,验证试验结果的相对误差绝大多数都在10%以内,可以识别冬油菜苗期田间杂草。该传感器为杂草自动探测装置的开发提供了参考。

Due to lack of weed identification and positioning equipment,farmers usually use large area uniform spraying of chemical herbicides, which not only wastes herbicides and labor, but also leads to ecological environment pollution and agricultural product quality problem. At the same time, the weed control accuracy using existing mechanical weed control method is low with a high crop injury rate. Therefore, accurate weed identification is a key issue in target pesticide application and mechanical weed control. There are three kinds of weed identification method： image-based weed identification method, spectrum-based weed identification method, and spectral-image-based weed identification method. At present, spectrum sensor based on spectrum analysis has been most widely accepted in actual weed control due to its advantages of simple system configuration, lossless and high processing speed. Based on the four characteristic wavelengths（595, 710, 755 and 950 nm） selected by the investigation of weeds in the winter rape field, in this paper, we presented our research on weed spectrum sensor. According to the principle of optical system and the actual operation requirements in field, the structure design scheme of the spectrum sensor was proposed, which consisted of five parts, active light source, convex lens, light filter, photocell, and signal conditioning device. As the field measurement results vulnerable to weather conditions, we used LED as an active light source. There were four optical channels（595, 710, 755 and 950 nm） and three active LED light sources in the spectrum sensor. A K9 lenticular lens with diameter of 16 mm and focal length of 16 mm was chosen. A narrow-band interference filter with a center wavelength of 595, 710, 755, 950 nm was applied, whose half-peak bandwidth is 10 nm and aperture is 16 mm. The size of the photocell is 2.65 mm × 2.65 mm. The convex lens, the light filter and the photocell were sequentially arranged in the optical channels in order to detect the spectral information. Signal processing circuits were developed to meet the reliable output signal amplification, filtering and other requirements without distortion. The spectral distance of the spectral sensor is 400-700 mm while the diameter of the field of view is 60-100 mm. After that, the spectral sensor was applied to do calibration test and experimental verification. The calibration test established four mathematical models between the four output results by intelligent spectral sensor and the four measuring results of Field Spec？3 spectrometer. The determination coefficients of each model were 0.799, 0.812, 0.892, and 0.867. The results of the experimental verification showed that most of the relative errors were within 10%, indicating that the designed sensor could separate winter rape from weeds. Therefore, the spectrum sensor could make its contribution to the exploration of weed automatic identification equipment. Experiments on actual weed identification showed that the average recognition rate was 90.7%, which had a good weed recognition effect. The main factors affecting the recognition results were the nature light and the mechanism vibration. How to reduce the influence of interference factors on the recognition precision will be the focus of the next research.