Affiliation:
1. School of Engineering, Anhui Agricultural University, Hefei 230036, China
2. State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471000, China
3. Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Wuhu 241000, China
Abstract
Most of the current crop row detection algorithms focus on extracting crop canopy rows as location information. However, for some high-pole crops, due to the transverse deviation of the position of the canopy and roots, the agricultural machinery can easily cause the wheel to crush the crop when it is automatically driven. In fact, it is more accurate to use the crop root row as the feature for its location calibration, so a method of crop root row detection is proposed in this paper. Firstly, the ROI (region of interest) of the crop canopy is extracted by a semantic segmentation algorithm, then crop canopy row detection lines are extracted by the horizontal strip division and the midpoint clustering method within the ROI. Next, the Crop Root Representation Learning Model learns the Representation of the crop canopy row and crop root row to obtain the Alignment Equation. Finally, the crop canopy row detection lines are modified according to the Alignment Equation parameters to obtain crop root row detection lines. The average processing time of a single frame image (960 × 540 pix) is 30.49 ms, and the accuracy is 97.1%. The research has important guiding significance for the intelligent navigation, tilling, and fertilization operation of agricultural machinery.
Funder
National key research and development of China plan sub-topic
National Natural Science Foundation of China project
Anhui Province university outstanding youth project
National key laboratory open of China project
Anhui Province key research and development plan project