Intelligent intra-row robotic weeding system combining deep learning technology with a targeted weeding mode

Quan, Longzhe; Jiang, Wei; Li, Hailong; Li, Hengda; Wang, Qi; Chen, Liqing

doi:10.1016/j.biosystemseng.2022.01.019

Cited by 44 publications

(27 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…appearance features of grasses, the identification of Gramineae weeds was relatively difficult. Quan et al (2022) used the trained model to identify the broadleaf and Gramineae weeds in the maize fields, resulting in an average accuracy of 94.70% and 87.10%, respectively. It could be seen from the confusion matrix (Figure 7) that the predicted values of crabgrass and green foxtail were both also lower than 0.9 in our research.…”

Section: Discussionmentioning

confidence: 99%

“…Because of the narrowness and insignificant appearance features of grasses, the identification of Gramineae weeds was relatively difficult. Quan et al. (2022) used the trained model to identify the broadleaf and Gramineae weeds in the maize fields, resulting in an average accuracy of 94.70% and 87.10%, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…The prevalent target detection networks are composed of Faster R-CNN, Single Shot Detector, You Only Look Once (YOLO) model, etc. (Redmon et al, 2016;Ren et al, 2017;Quan et al, 2022). Dyrmann et al (2016) used convolutional neural networks to identify 22 different plants with a total of 10,413 images.…”

Section: Introductionmentioning

confidence: 99%

“…The prevalent target detection networks are composed of Faster R-CNN, Single Shot Detector, You Only Look Once (YOLO) model, etc . ( Redmon et al., 2016 ; Ren et al., 2017 ; Quan et al., 2022 ). Dyrmann et al.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Weed25: A deep learning dataset for weed identification

et al. 2022

View full text Add to dashboard Cite

Weed suppression is an important factor affecting crop yields. Precise identification of weed species will contribute to automatic weeding by applying proper herbicides, hoeing position determination, and hoeing depth to specific plants as well as reducing crop injury. However, the lack of datasets of weeds in the field has limited the application of deep learning techniques in weed management. In this paper, it presented a dataset of weeds in fields, Weed25, which contained 14,035 images of 25 different weed species. Both monocot and dicot weed image resources were included in this dataset. Meanwhile, weed images at different growth stages were also recorded. Several common deep learning detection models—YOLOv3, YOLOv5, and Faster R-CNN—were applied for weed identification model training using this dataset. The results showed that the average accuracy of detection under the same training parameters were 91.8%, 92.4%, and 92.15% respectively. It presented that Weed25 could be a potential effective training resource for further development of in-field real-time weed identification models. The dataset is available at https://pan.baidu.com/s/1rnUoDm7IxxmX1n1LmtXNXw; the password is rn5h.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Weed25: A deep learning dataset for weed identification

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Competition for water, nutrients, space, and sunlight between weeds and Peucedani Radix ( Li et al., 2021 ) significantly reduces the yield of Peucedani Radix, causing huge economic losses. However, the main economic value of Peucedani Radix lies in its buried rhizome, which mechanical weeding operations can damage along with plant stems ( Quan et al., 2022 ). At present, Peucedani Radix weeding is mainly achieved using sprayed herbicides; however, spraying herbicides over a large area on water, air, and soil can lead to environmental problems ( Villette et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Design and operation of a Peucedani Radix weeding device based on YOLOV5 and a parallel manipulator

et al. 2023

View full text Add to dashboard Cite

To avoid excessive use of herbicides in the weeding operations of Peucedani Radix, a common Chinese herb, a precision seedling avoidance and weeding agricultural robot was designed for the targeted spraying of herbicides. The robot uses YOLOV5 combined with ExG feature segmentation to detect Peucedani Radix and weeds and obtain their corresponding morphological centers. Optimal seedling avoidance and precise herbicide spraying trajectories are generated using a PSO-Bezier algorithm based on the morphological characteristics of Peucedani Radix. Seedling avoidance trajectories and spraying operations are executed using a parallel manipulator with spraying devices. The validation experiments showed that the precision and recall of Peucedani Radix detection were 98.7% and 88.2%, respectively, and the weed segmentation rate could reach 95% when the minimum connected domain was 50. In the actual Peucedani Radix field spraying operation, the success rate of field precision seedling avoidance herbicide spraying was 80.5%, the collision rate between the end actuator of the parallel manipulator and Peucedani Radix was 4%, and the average running time of the parallel manipulator for precision herbicide spraying on a single weed was 2 s. This study can enrich the theoretical basis of targeted weed control and provide reference for similar studies.

show abstract

Corn‐Soybean and Rice‐Corn‐Soybean Cropping Systems

2023

Weed Science and Weed Management in Rice and Cereal‐Based Cropping Systems

View full text Add to dashboard Cite

Intelligent intra-row robotic weeding system combining deep learning technology with a targeted weeding mode

Cited by 44 publications

References 27 publications

Weed25: A deep learning dataset for weed identification

Weed25: A deep learning dataset for weed identification

Design and operation of a Peucedani Radix weeding device based on YOLOV5 and a parallel manipulator

Corn‐Soybean and Rice‐Corn‐Soybean Cropping Systems

Contact Info

Product

Resources

About