Crop positioning for robotic intra-row weeding based on machine vision

Li Nan, Zhang Chunlong, Chen Ziwen, Ma Zenghong, Sun Zhe, Yuan Ting, Li Wei, Zhang Junxiong

Abstract


A machine-vision-based method of locating crops is described in this research. This method was used to provide real-time positional information of crop plants for a mechanical intra-row weeding robot. Within the normalized red, green, and blue chromatic coordinates (rgb), a modified excess green feature (g-r>T & g-b>T) was used to segment plant material from back ground in color images. The threshold T was automatically selected by the maximum variance (OTSU) algorithm to cope with variable natural light. Taking into account the geometry of the camera arrangement and the crop row spacing, the target regions covering the crop rows were defined based on a pinhole camera model. According to the statistical variation in the pixel histogram in each target region, locations of the crop plants were initially estimated. To obtain the accurate locations of crops, median filtering was conducted locally in the bounding boxes of the crops close to the bottom of the images. For the lateral guidance of the robot, a novel method of calculating lateral offset was proposed based on a simplified match between a template and the detected crops. Field experiments were conducted under three different illumination conditions. The results showed that the accurate identification rates on lettuce, cauliflower and maize were all above 95%. The positional error as within ±15 mm, and the average processing time for a 640×480 image was 31 ms. The method was adequate to meet the technical requirement of the weeding robot, and laid a foundation for robotic weeding in commercial production system.
Keywords: mechanical weeding, computer vision, real-time image processing, crop sensing, precision agriculture
DOI: 10.3965/j.ijabe.20150806.1932

Citation: Li N, Zhang C L, Chen Z W, Ma Z H, Sun Z, Yuan T, et al. Crop positioning for robotic intra-row weeding based on machine vision. Int J Agric & Biol Eng, 2015; 8(6): 20－29.

Keywords


mechanical weeding, computer vision, real-time image processing, crop sensing, precision agriculture

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References


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