基于多纹理特征融合的麦田收割边界检测

自主导航是智能化农机完成收割作业的重要保障。该研究针对多云天气下光照易变化导致单一特征难以应对麦田环境的问题，提出基于多纹理特征融合的麦田收割边界检测方法。通过构建由图像熵特征和方向梯度特征组成的二维特征向量对麦田收割区域与未收割区域进行分类。其中，根据图像熵特征提取的特点，提出基于滑动窗口的直方图统计方法加速图像熵特征提取速度，较传统熵特征提取方法，本文方法耗时减少49.52%。在提取二维特征基础上，根据特征直方图分布特点，结合最大熵阈值分割算法对麦田图像进行初步分类，然后通过去除小连通区域对误分类区域进行剔除，进而运用Canny算子提取边缘轮廓点，得到分布于收割边界附近的待拟合点。最后，通过Ransac算法对拟合直线进行区域限制，得到较为准确的收割边界。试验结果表明，相比传统基于Adaboost集成学习算法提取收割边界，本文算法处理240像素×1 280像素的图像平均耗时为0.88 s，提速约73.89%；在不同光照条件下，收割边界平均检测率为89.45%，提高47.28个百分点，其中弱光照下检测率为90.41%，提高46.19个百分点，局部强光照下检测率为88.26%，提高46.00个百分点，强光照下检测率为89.68%，提高49.64个百分点。研究结果可为田间农机导航线识别提供参考。

Detection of the wheat-harvesting boundary in wheat field based on multi-texture fusion

Autonomous navigation is an important guarantee for intelligent agricultural machinery to complete harvesting operations. Navigation line recognition is an important step in achieving autonomous navigation of intelligent agricultural machinery. Real-time and accurate detection of harvesting navigation lines can effectively accelerate the progress of operations and reduce crop loss rates. Visual sensors have the advantages of low cost and rich information acquisition, making them widely used in route detection. Frequent changes in lighting under cloudy weather make it difficult for a single feature to cope with wheat field environments. In this study, a wheat harvesting boundary detection method based on multi texture feature fusion was proposed. In the harvested area, the characteristics of low stubble and no top wheat ears result in differences in uniformity, density, and other aspects when harvesting under both light and backlight compared to the non-harvested area. At the same time, due to the exposed wheat stubble in the harvested area, it appears more regular on the image, and its gradient direction is more consistent than that in the non-harvested area. Therefore, a two-dimensional feature vector composed of image entropy and directional gradient was constructed to classify the harvested and unharvested areas of the wheat field, and then the harvesting boundary was extracted. Due to the susceptibility of image entropy features to perspective phenomena, this paper compared the entropy features of different regions in the image when fixing the camera installation angle and height, and ultimately selected the middle region of the image as the region of interest. After analyzing the characteristics of image entropy feature extraction, a histogram statistical method based on sliding windows was proposed to accelerate the speed of image entropy feature extraction. By further dividing the window to be calculated into several sub windows, sliding and combining these sub windows significantly reduced the computation. The entropy feature extraction in this paper took 0.53 seconds, which is 49.52% faster than the traditional method of directly extracting entropy from the entire window. After extracting two-dimensional features, based on the distribution characteristics of the feature histogram and combined with the maximum entropy threshold segmentation algorithm, the wheat field image was preliminarily classified. For entropy features, low noise points affected the threshold segmentation effect. By comparing the impact of discarding different proportions of low entropy points on the segmentation threshold, this paper discarded 10% of low entropy points, enhancing the threshold segmentation effect. The method of removing small connected regions was used to eliminate the misclassified regions in the initial classification binary image. Then, the Canny operator was used to extract edge contour points distributed near the harvesting boundary. Due to the consistent direction between the harvesting boundary and the agricultural machinery''s forward direction, the Ransac algorithm was used to restrict the fitting line in the region, obtaining the accurate harvesting boundary. In order to verify the feasibility of the method proposed in this article, wheat field images under different lighting conditions were collected, including 2 200 weak light images, 758 local strong light images, and 1 134 strong light images. After selecting the region of interest, the size of the image to be processed was 240 pixels×1 280 pixels. The algorithm in this paper took an average of 0.88 s to process each image on laptops. The detection rates under weak light, local strong light, and strong light were 90.41%, 88.26%, and 89.68%, respectively, with an average of 89.45%. Compared with the traditional method of using Adaboost algorithm for machine learning, the detection speed of this algorithm was improved by 73.89%, and the detection rate was increased by 46.19 percentage points, 46.00 percentage points, and 49.64 percentage points under weak light, local strong light, and strong light, respectively. Compared with traditional algorithms, the method proposed in this paper significantly improved real-time performance and detection rate, which can provide reference for the detection of field agricultural machinery navigation lines.