Abstract
Weed control is a substantial challenge in field management. A better weed control method at an earlier growth stage is important for increasing yields. As a promising weed control technique, intelligent weeding based on machine vision can avoid the harm of chemical weeding. For machine vision, it is critical to extract and segment crops from their background. However, there is still no optimal solution for object tracking with occlusion under a similar color background. In this study, it was found that the gray distribution of a maize canopy follows the gradient law. Therefore, the recognition method based on the HLS-SVM (HLS color space and Support Vector Machine) and on the grayscale gradient was developed. First, the HLS color space was used to detect the maize canopy. Second, the SVM method was used to segment the central region of the maize canopy. Finally, the maize canopy center was identified according to the gradient law. The results showed that the average segmentation time was 0.49 s, the average segmentation quality was 87.25%, and the standard deviation of the segmentation was 3.57%. The average recognition rate of the center position was 93.33%. This study provided a machine vision method for intelligent weeding agricultural equipment as well as a theoretical reference for further agricultural machine vision research.
Highlights
Maize is one of the three largest food crops in the world, and approximately 1/3 of the people in the world use maize as its primary food source [1]
Weed control methods are mainly divided into manual weeding, chemical weeding, mechanical weeding and automatic intelligent weeding
This research work developed the maize canopy center recognition method based on the HLS-SVM (HLS color space and Support Vector Machine)and gray gradient law and achieved the accurate identification of maize center positions
Summary
Maize is one of the three largest food crops in the world, and approximately 1/3 of the people in the world use maize as its primary food source [1]. The segmentation methods based on learning are mainly divided into two categories. Under similar color background characteristics, the real-time maize canopy center position recognition needs to be solved urgently. This research work developed the maize canopy center recognition method based on the HLS-SVM (HLS color space and Support Vector Machine)and gray gradient law and achieved the accurate identification of maize center positions. Research on maize canopy center recognition based on nonsignificant color difference segmentation after filtering. It was found that all the grayscale images, from the canopy center to the canopy periphery, followed the increasing gradient law, which is the unique attribute of the chlorophyll distribution of the maize canopy This feature can be used to segment the different central hierarchies of the canopy, which provides a favorable condition for more accurate canopy center identification. The threshold of the gray level was set to 30, and the maize canopy center was identified by threshold segmentation
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