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In unstructured and complicated filed operation environment, the realization of autonomous navigation turning of agricultural machinery at the headland area of field is one of the key technical bottlenecks for achieving the autonomous navigation walking of agricultural machinery throughout the field. The primary task of realizing the former is to timely and accurately perceive the spatial position information of the headland area, especially the location information of the headland boundary. Based on machine vision technology, whether the headland appeared in the image or not was firstly determined according to the jumping characteristics of the gray values of pixels inside and outside the field. Specifically, two metric values of positive and negative distribution deviations were established to describe the positive and negative dispersion degree between the average pixel gray values of different rows in the image. When one of the two metric values was larger than the judgment threshold, that was, the distribution of the average values was relatively dispersed, it can be considered that the jumping characteristics had occurred and it was judged that the headland was appearing in the image. Subsequently, the image was evenly divided into eight sub-processing regions along the horizontal direction. For each sub-processing region, the distribution curve of the row gray average values was obtained and smoothed by local weighted regression method. The in-order outlier parameter was established, and based on the degree of sequential outlier of the row gray average values corresponding to the peak points or trough points on the smoothed curve, the position coordinates of the jumping peak point and pre-jumping trough point were determined, and accordingly the pixel coordinates of the jumping feature points were determined. Finally, all the jumping feature points were fitted linearly based on the robust regression method to obtain the main-body extended azimuth line of the irregular headland boundary. In the end, the main-body extension azimuth line was moved down in parallel until the average gray value of the pixels on the line was close to the corresponding gray distribution characteristic of the pixels inside the field, which was considered to be shifted to the safe position, thus the boundary line for safe turning of agricultural machinery at the current headland of field was obtained. The test results showed that the accuracy rate of judging whether the headland appeared or not was not less than 96%, the detection accuracy rate of headland boundary line was not less than 92%, and the processing time of single frame image was not higher than 0.52s based on Matlab platform. It can provide a fast, accurate and reliable technical support for agricultural machinery to implement automatic navigation turning safely at the headland of field