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The counting of cotton rows is dynamically a premise of accurate positioning of a visual plant protection vehicle, which can ensure that the vehicle can traverse at the end of a field. In this study, cotton during plant protection periods was taken as the research object, which presented a dynamic counting method of cotton rows based on Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM). Hogs of cotton rows and inter-row background were extracted as feature descriptors of images, then they were classified by an SVM classifier. In this way, cotton rows could be identified through classifying cotton rows and inter-row backgrounds. 7 000 positive and negative training samples were respectively prepared for HOG characteristics of training samples. Among them, positive samples contained a complete outline of cotton lines in width, by contrast, negative samples contained soil among lines, plastic films and incomplete outline of cotton lines, etc. Besides, 1 000 positive and negative test samples were prepared to optimize parameters of the HOG-SVM model. By setting a sliding window to training samples, hog features of sample images were extracted, meanwhile positive and negative samples were marked for initial training on SVM classifier parameters. The initially trained SVM classifier was used to classify and test the samples, and those with wrong classifications were picked out for re-marking and re-training. Such retraining processes would continue until all test samples were correctly classified. Then the final HOG-SVM model was solidified for video detections. In recognizing processes, the model was used to slide the window in sequence images and to detect. In the sequence images, the HOG-SVM model built in the previous step was used by the sliding window. In this way, cotton rows in the video could be detected. The same rows of cotton could be detected for many times. To accurately identify cotton rows, Non-Maximum Suppression (NMS) was used to normalize target windows of detected cotton rows. All candidate target windows were scored with confidence and sorted from high to low, then the one with the highest score was retained. In two consecutive images, NCC (Normalized Cross-Correlation) values of target windows of front and back frames were solved to match the same rows of cotton, realized dynamic tracking of cotton rows, and further realized counting. Visual perspectives would have different effects on the upper and lower ends of images. There was an adhesion among cotton rows at the upper end of images, and those at the lower end of images were lack of seedlings and lodging. Such interferences had impacts on identifications of cotton rows. To solve this problem, the Region of Interest (ROI) was set for video sequence images in this study, and recognition processes of all cotton rows were within the ROI region with a very complex cotton field environment. This method had strong robustness to change in a camera's view angles and natural environment, such as to change in the light as well as shook of branches and leaves caused by winds, etc. In this algorithm, the recognition rate of cotton rows was higher than 90% under the condition in which winds were lower than level-2 and the average detection time per frame was lower than 32 ms, which met requirements of actual field operations. This dynamic counting method of cotton rows was used as a basis for determining transverse distances of visual plant protection vehicles at field ends, which could be used in other semi-structured rows to plant low and short crops. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In a corn field with an unstructured environment, for the images collection by monocular vision, conventional path recognition algorithms are difficult to guarantee their robustness due to illumination variation, background reflection, shadow noise, and so on. In addition, the increase of the calculation amount of the algorithms caused by the complicated background information of the corn field environment affects the quickness and the real-timeliness of the corn harvester autonomous navigation, which leads to the difficulty of meeting the requirement for the operation efficiency of the corn field harvester and impedes the practical application of the mobile robot technology in agricultural production. The significant premise of visual navigation for a corn harvester is the precise and rapid detection of the navigation route. Unfortunately, some interferences will affect the extraction of the navigation route and the judgment for the edge of the corn field due to the corn shadow and weeds. The vision navigation has many technical advantages of adapting to the complicated field operating environment, has wide detection range, and has rich and complete information. This paper put forward the detection algorithms of the operation routes of the corn harvester and the judgement of the end of the corn field by analyzing the different color features of the visual navigation image. Here we studied the region of interest where lies the target straight line. To improve the rapidity of the navigation path recognition and meet the real-time requirements of autonomous navigation operations, for the color characteristic of the corn field environment, the clustering segmentation of the image was performed based on the analysis of red (R), green (G) and blue (B) components to achieve the respective clusters of the path and the corn field information. We used these color components to extract the target features of the inner and outside of the corn field respectively, and smooth the image using the moving average method with the set length. First, to remove the shadow interference of the corn columns, we compared the G component and the B component of a color image and smoothed the image using the moving average method. For the first frame, we used the distribution of the G component to find the alternate point; for other frames, we connected them with the previous frame to calculate the skew angles of the target line. Then, we extracted the navigation routes based on the Hough transform passing a known point. At last, it would be determined whether the corn harvester reached the end automatically according to the continuous mutation of the R component. The test proved that the processing time of detecting line was 50.1 ms per frame and the detection of the end was accurate and reliable, which could meet the demand of the real operation of a corn harvester in the field. It can be concluded that the navigation routes are extracted and the edge of the corn field is judged accurately, which satisfy the requirement of the practical application of the corn harvester in the field. In addition, the work can also provide the reference for the vision navigation routes' detection of sorghum and some high pole crops with mechanized harvesting. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)