基于线结构光源和机器视觉的高精度谷物测产系统研制

Development of yield monitoring system with high-precision based on linear structured light source and machine vision

针对精准农业中谷物产量信息的高精度获取需求,设计了基于计算机视觉的谷物测产系统,由工业相机、线结构光发生器、电感式接近开关和工控机等组成。提出了基于线结构光的谷堆厚度测量方法,根据所建立的谷物几何模型计算出谷堆的体积,并采用电感式接近开关克服了传统光电式谷物测产系统存在的误触发问题。同时,研究了不同转速下结构光测量误差,建立了基于转速的线结构光测量修正模型,使得测量误差从1.1%减小为0.33%。在室内台架上进行了测产试验,试验结果表明,未使用线结构光修正模型的最大测产误差为12.73%,在使用了线结构光测量修正模型之后,相对测产误差在4.27%以内,该研究可为谷物测产研究提供理论依据。

Gaining the precise yield information of a certain area is an important factor to assess the grain yield and the planting effects. Grain yield measurement system is one of the key techniques of the precision agriculture, and is also the foundation of realizing precision management. Because of the gap(around 10 mm) between the scrapper and the elevator, grains might be dropping through the gap and therefore triggers the yield sensor based on photoelectric sensor wrongly, so the generated result will not be accurate. Aiming at avoiding the problem mentioned above, a yield monitoring system based on linear structured light source and machine vision is developed. The yield sensor is made up of industrial camera, line structured light generator, proximity switch and industrial computer. When scraper passes the proximity switch in a certain position which can be adjusted, proximity switch will sense its move, which will generate a signal to trigger the industrial camera to capture the image at that time. Since proximity switch used in this sensor is based on inductance sensed, the leaky grain through the gap between the scrapper and the elevator will not trigger the switch, and the camera will be triggered correctly only by the signal of the proximity switch. A grains accumulation volume model is established to calculate the volume of the grains on the scrapper. The thickness of the grain on the scrapper is measured, after the calculation model of thickness is calibrated, the volume can be calculated according to the model established before. The computer is used to process the real-time image where the dropping grain will become noise. In order to eliminate all the noise in the image, K-Nearest Neighbor(KNN) algorithm is proposed, in which the basic idea is to move those area far away from the surroundings. The experimental result shows that this method works effectively. The grain accumulation volume then can be calculated if the thickness of the grain can be calculated precisely. A measuring method for the thickness based on linear structured light is proposed. According to the volume weight measured in advance, the yield can be calculated. Grain models of the same material of scrapper are made to simulate different grain thickness. And the actual grain thickness are 10.0, 21.8 and 33.4 mm. The experiment of the thickness calculation of the scrapper(with no grains) and grain model under different speeds(60, 180, 300, 420, 540 r/min) is carried out, and the results show that the error is between 0 and 1.1%. The error is bigger while the speed is faster, the reason for which is the image capture mode of rolling shutter of the CMOS camera. Rolling shutter is a method of image capture in which a still picture(in a still camera) or each frame of a video(in a video camera) is captured not by taking a snapshot of the entire scene at a single instant in time but rather by scanning across the scene rapidly, either vertically or horizontally. When the scrapper passes the proximity switch, the switch will trigger the camera to take the shot. During the time in which the camera take the shot, the scrapper is still moving, which will cause the distortion of the image, and the distortion will be worse while the scrapper moves faster. A modified for thickness calculation is proposed to fix the distortion, and the final measuring result error is less than 0.33%, much better than original 1.1%, which means the modification model is accurate and stable. At last, the experiment of the real-time yield monitoring is carried out on the self-designed experiment platform under different speeds using the methods and the devices mentioned above. The experiment results show that the measurement error is between 3.5% and 4.27%. This research provides a reference for yield monitoring.