地源热泵水蓄能型植物工厂降温系统夏季运行特性

针对田间环境复杂、秸秆形态多样、秸秆覆盖率判断主观性影响过大、补贴面积测量耗时耗力等问题，该文开展了秸秆覆盖率率自动识别方法研究和监测设备研制。首先，提出利用时频变换进行秸秆识别，设计高通滤波器提取了图像的频域特征进行自适应分割。基于集成分类器利用已有的秸秆识别数据训练支持向量机分类器，对秸秆图像进行再识别和筛选。最后，设计多尺度占比滤波器，对识别图像中的噪声和空洞进行修补，生成适应多种情况的秸秆覆盖率识别算法。与北斗定位模块、无线通讯模块、摄像头、传感器、服务器等设备共同组成秸秆覆盖率识别系统。试验结果表明，设备的秸秆覆盖率识别误差为4.55%，平均单张图像耗时0.05 s。研究结果满足保护性耕作中的自动化监测要求，可为保护性耕作作业质量评测提供有效的技术支持。

Operating characteristics of energy storage ground source heat pump cooling system in plant factory in summer

The crop residues auto-detection is an important technology for intelligent agriculture. However, the farmland condition is so complicated so that it''s difficult to detect the crop residues and calculate its coverage. The field is always unevenly distributed with clays and gullies, besides that, the crop residues have various shapes and sizes. Those variables affect the detection accuracy. The research shows that FFT (fast Fourier transformation) can distinguish both the high frequency and low frequency signals in the image. The high frequency signals characterize the outline of the image and the low frequency signals characterize the content of the image. The crop residues belong to high frequency information exactly. Meanwhile, SVM (support vector machine) has a high distinguishing ability for target recognition in complex backgrounds. It maps linearly indivisible data to high dimensional space through different kernel functions and converts it into linearly separable data. It establishes a maximum interval hyperplane in high dimensional space and two mutually parallel hyperplanes on both sides of the data hyperplane. On the basis above, this paper proposed a novel fusion algorithm for crop residues auto-detection with Fast Fourier Transformation (FFT) and Support Vector Machine (SVM). We also designed and realized an intelligent monitoring equipment for crop coverage auto-detection which consists camera, embedded board, location module, communication module and other sensors through 4G cloud network. In order to detect residues, we extracted the high frequency information (residues region) by designing high pass filter after FFT with different kernels. We tested three different filter kernels including square, circle and diamond filter with three shapes. The results of the experiments indicated that the square filter with size 11×11 was the best choice considering speed and accuracy. Then, an adaptive threshold segmentation method was put forward to process the normalization image we obtained. The threshold was set by calculate the energy score we defined based on the idea of integral. The experiments showed that 0.8 was the best threshold choice for our algorithm. It can preserve the most crop region and remove the un-crop region as much as possible. We used the segmentation result as the detection mask for the next step. On the other hand, we trained a SVM classifier using our database with different input sizes and kernels and selected the parameter group with best performance as final choice. The classifier was utilized to re-classify the adaptive threshold segmentation result. Finally, we found that the traditional image erosion and dilation algorithm treated all pixels without considering its credibility. So it was prone to misprocessing (including etching away areas that should be preserved, or expanding the noise area that should be removed). To avoid this misprocessing, we raised a multi-scale filter to erase noises and fill tiny holes which was similar to image pyramid. There were two filters with size 3×3 and 7×7. The bigger one was to filter the image noise with tinny size and the smaller one was to expand the residues region with holes. We benchmarked the algorithm on our database and the results indicated that our method was state of the art. The error was about 4.55% and it just used 0.05 s.