Traits of insect herbivores and target weeds associated with greater biological weed control establishment and impact

Panta, Sujan; Schwarzländer, Mark; Weyl, Philip S. R.; Hinz, Hariet L.; Winston, Rachel L.; Eigenbrode, Sanford D.; Harmon, Bradley L.; Bacher, Sven; Paynter, Quentin

doi:10.1007/s10526-024-10245-6

Cited by 8 publications

(2 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In China, there are 1,430 species (varieties) of agricultural weeds, leading to a reduction in grain production of up to 60 million tons and economic losses of up to 220 billion yuan. Current main weeding control methods include biological weeding, chemical weeding, traditional mechanical weeding and targeted weeding [1][2][3]. Biological weeding involves introducing foreign organisms to suppress weed growth, but these organisms can threaten local ecosystems [4]; Extensive application of herbicides plays a crucial role in controlling weeds and boosting agricultural efficiency, yet, this extensive application can induce resistance in weeds and significantly affect soil, biota, and human health [5]; Traditional mechanical weeding is effective pre-emergence but post-emergence inaccuracy can irreversibly damage crops; Targeted weeding technology, which focuses on the specific locations of weeds, has gradually become a focal point in the era of precision agriculture and Agriculture 4.0 [6].…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv8-seg Based on Multi-scale Feature Fusion and Deformable Convolution for Weed Precision Segmentation

Lyu,

Lu,

2024

Preprint

View full text Add to dashboard Cite

Laser-targeted weeding methods further enhance the sustainable development of green agriculture, with one key technology being the improvement of weed localization accuracy. Here, we propose an improved YOLOv8 instance segmentation based on bidirectional feature fusion and deformable convolution (BFFDC-YOLOv8-seg) to address the challenges of insufficient weed localization accuracy in complex environments with resource-limited laser weeding devices. Initially, by training on extensive datasets of plant images, the most appropriate model scale and training weights are determined, facilitating the development of a lightweight network. Subsequently, the introduction of the Bidirectional Feature Pyramid Network (BiFPN) during feature fusion effectively prevents the omission of weeds. Lastly, the use of Dynamic Snake Convolution (DSConv) to replace some convolutional kernels enhances flexibility, benefiting the segmentation of weeds with elongated stems and irregular edges. Experimental results indicate that the BFFDC-YOLOv8-seg model achieves a 4.9% increase in precision, an 8.1% increase in recall rate, and a 2.8% increase in mAP50 value to 98.8% on a vegetable weed dataset compared to the original model. It also shows improved mAP50 over other typical segmentation models such as Mask R-CNN, YOLOv5-seg, and YOLOv7-seg by 10.8%, 13.4%, and 1.8%, respectively. Furthermore, the model achieves a detection speed of 24.8 FPS on the Jetson Orin nano standalone device, with a model size of 6.8MB that balances between size and accuracy. The model meets the requirements for real-time precise weed segmentation, suitable for complex vegetable field environments and resource-limited laser weeding devices.

show abstract

Section: Introductionmentioning

confidence: 99%

Improved YOLOv8-seg Based on Multi-scale Feature Fusion and Deformable Convolution for Weed Precision Segmentation

Lyu,

Lu,

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In China, there are 1430 species (varieties) of agricultural weeds, leading to a reduction in grain production of up to 60 million tons and economic losses of up to USD 30 billion. Current main weeding control methods include biological weeding, chemical weeding, traditional mechanical weeding, and targeted weeding [1][2][3]. Biological weeding involves introducing foreign organisms to suppress weed growth, but these organisms can threaten local ecosystems [4].…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv8-Seg Based on Multiscale Feature Fusion and Deformable Convolution for Weed Precision Segmentation

Lyu,

Lu,

2024

Applied Sciences

View full text Add to dashboard Cite

Laser-targeted weeding methods further enhance the sustainable development of green agriculture, with one key technology being the improvement of weed localization accuracy. Here, we propose an improved YOLOv8 instance segmentation based on bidirectional feature fusion and deformable convolution (BFFDC-YOLOv8-seg) to address the challenges of insufficient weed localization accuracy in complex environments with resource-limited laser weeding devices. Initially, by training on extensive datasets of plant images, the most appropriate model scale and training weights are determined, facilitating the development of a lightweight network. Subsequently, the introduction of the Bidirectional Feature Pyramid Network (BiFPN) during feature fusion effectively prevents the omission of weeds. Lastly, the use of Dynamic Snake Convolution (DSConv) to replace some convolutional kernels enhances flexibility, benefiting the segmentation of weeds with elongated stems and irregular edges. Experimental results indicate that the BFFDC-YOLOv8-seg model achieves a 4.9% increase in precision, an 8.1% increase in recall rate, and a 2.8% increase in mAP50 value to 98.8% on a vegetable weed dataset compared to the original model. It also shows improved mAP50 over other typical segmentation models such as Mask R-CNN, YOLOv5-seg, and YOLOv7-seg by 10.8%, 13.4%, and 1.8%, respectively. Furthermore, the model achieves a detection speed of 24.8 FPS on the Jetson Orin nano standalone device, with a model size of 6.8 MB that balances between size and accuracy. The model meets the requirements for real-time precise weed segmentation, and is suitable for complex vegetable field environments and resource-limited laser weeding devices.

show abstract

Prioritizing weeds for biological control development in the western USA: adaptation of the Biological Control Target Selection system

Winston,

Schwarzländer,

Hinz

et al. 2024

BioControl

View full text Add to dashboard Cite

Nonnative invasive plants (weeds) negatively impact native biodiversity, ecosystem services, agriculture, and the economic interests and health of humans. Since 1902, biological weed control has been employed as a cost-effective and sustainable management option for weeds in the USA. However, biological control is not appropriate for all weeds, nor is sufficient funding available to develop biological control for all weed species. Researchers in South Africa recently developed a Biological Control Target Selection (BCTS) system as an objective, transparent, and simple approach to prioritizing weeds as targets for biological control. The system includes multiple attributes pertaining to the: (1) impact and importance of the target weed, (2) likelihood of achieving success, and (3) investment required to develop and implement biological control. Attributes are scored based on available literature, and the overall score for each weed is used to rank species according to their potential to result in successful biological control programs. This paper describes the adaptation of the BCTS for application to weeds in the western USA not already targeted for biological control.

show abstract

Traits of insect herbivores and target weeds associated with greater biological weed control establishment and impact

Cited by 8 publications

References 47 publications

Improved YOLOv8-seg Based on Multi-scale Feature Fusion and Deformable Convolution for Weed Precision Segmentation

Improved YOLOv8-seg Based on Multi-scale Feature Fusion and Deformable Convolution for Weed Precision Segmentation

Improved YOLOv8-Seg Based on Multiscale Feature Fusion and Deformable Convolution for Weed Precision Segmentation

Prioritizing weeds for biological control development in the western USA: adaptation of the Biological Control Target Selection system

Contact Info

Product

Resources

About