| 1 | Crop positioning for robotic intra-row weeding based on machine vision显示文摘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. | Li Nan Zhang Chunlong Chen Ziwen Ma Zenghong Sun Zhe Yuan Ting Li Wei Zhang Junxiong | 2015 | International Journal of Agricultural and Biological Engineering2015，8，6： | 4 |
| 2 | Lightweight detection method for lotus seedpod in natural environment显示文摘In order to solve the problems of the current target detection algorithms,such as poor discrimination of occluded targets,multiple parameters,complex networks,large amounts of computation,and not conducive to the deployment of mobile terminals,a lightweight lotus seedpod detection method based on YOLOv5s model was proposed in this study.First,the dataset was augmented by using a combination of offline and online augmentation,which improved the adaptability and robustness of the model in complex environments.Then,a lightweight Ghost convolution module was introduced to replace the original convolution,and a lightweight bidirectional feature pyramid network was designed,which could enhance the feature extraction and fusion capability of the network and reduce the amount of calculation and model size;On this basis,the combination of WIoU loss function and Mish activation function was adopted to improve the accuracy of feature extraction.Finally,the knowledge distillation training strategy was used to ensure the proposed lightweight model has the learning ability of a complex network model,improving the recall and precision of model detection.The results of the ablation study show that the proposed method effectively improves the detection performance of the YOLOv5s model for lotus seedpods.The mean average precision of the improved model was 89.7%,compared with the original YOLOv5s model increased by 2.8%,and the parameters and FLOPs were reduced by 2.36M and 7.3G,respectively.Compared with other detection algorithm models,the proposed algorithm model has the advantages of less computation,smaller model size,and higher detection precision.Therefore,the proposed improved optimization method based on the YOLOv5s model can effectively detect lotus seedpods,which provides theoretical research and technical support for intelligent picking of lotus seedpods in the actual operating environment. | Tao Tang Xu Wang Zenghong Ma Weiwei Hong Gaohong Yu Bingliang Ye | 2023 | International Journal of Agricultural and Biological Engineering2023，16，6： | 0 |
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