Weed25: A deep learning dataset for weed identification

Wang, Pei; Tang, Yin; Luo, Fan; Wang, Lihong; Li, Chengsong; Niu, Qi; Li, Hui

doi:10.3389/fpls.2022.1053329

Cited by 26 publications

(11 citation statements)

References 35 publications

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“…This experiment is based on the publicly available dataset Weed25 [36], with farmland and grassland as the research object. The dataset contains a total of 14,035 images and 25 weed categories.…”

Section: Dataset Productionmentioning

confidence: 99%

RVDR-YOLOv8: A Weed Target Detection Model Based on Improved YOLOv8

Ding,

Jiang,

Song

et al. 2024

Electronics

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Currently, weed control robots that can accurately identify weeds and carry out removal work are gradually replacing traditional chemical weed control techniques. However, the computational and storage resources of the core processing equipment of weeding robots are limited. Aiming at the current problems of high computation and the high number of model parameters in weeding robots, this paper proposes a lightweight weed target detection model based on the improved YOLOv8 (You Only Look Once Version 8), called RVDR-YOLOv8 (Reversible Column Dilation-wise Residual). First, the backbone network is reconstructed based on RevCol (Reversible Column Networks). The unique reversible columnar structure of the new backbone network not only reduces the computational volume but also improves the model generalisation ability. Second, the C2fDWR module is designed using Dilation-wise Residual and integrated with the reconstructed backbone network, which improves the adaptive ability of the new backbone network RVDR and enhances the model’s recognition accuracy for occluded targets. Again, GSConv is introduced at the neck end instead of traditional convolution to reduce the complexity of computation and network structure while ensuring the model recognition accuracy. Finally, InnerMPDIoU is designed by combining MPDIoU with InnerIoU to improve the prediction accuracy of the model. The experimental results show that the computational complexity of the new model is reduced by 35.8%, the number of parameters is reduced by 35.4% and the model size is reduced by 30.2%, while the mAP50 and mAP50-95 values are improved by 1.7% and 1.1%, respectively, compared to YOLOv8. The overall performance of the new model is improved compared to models such as Faster R-CNN, SSD and RetinaNet. The new model proposed in this paper can achieve the accurate identification of weeds in farmland under the condition of limited hardware resources, which provides theoretical and technical support for the effective control of weeds in farmland.

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Section: Dataset Productionmentioning

confidence: 99%

RVDR-YOLOv8: A Weed Target Detection Model Based on Improved YOLOv8

Ding,

Jiang,

Song

et al. 2024

Electronics

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“…1 Weed damage causes annual losses of up to 13.2% of global agricultural yield, seriously threatening global food security. 2 Herbicides are the most economical and effective way to manage weeds. 3,4 However, overusing some herbicides in high dosages (up to 2 to 3 kg ai/ha) has adversely affected the ecological system.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Statistics show that the yearly global yield losses due to plant diseases, pests, and grasses range from 20 to 40% . Weed damage causes annual losses of up to 13.2% of global agricultural yield, seriously threatening global food security . Herbicides are the most economical and effective way to manage weeds. , However, overusing some herbicides in high dosages (up to 2 to 3 kg ai/ha) has adversely affected the ecological system. , Therefore, current weed management systems are in high demand for new herbicides with relatively environmentally benign properties and low application rates .…”

Section: Introductionmentioning

confidence: 99%

Understanding the Effects of Ligand Configuration on Protoporphyrinogen IX Oxidase with Rationally Designed 3-(N-Phenyluracil)but-2-enoates

Yang,

Liu,

et al. 2024

J. Agric. Food Chem.

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Protoporphyrinogen IX oxidase (PPO, EC 1.3.3.4) is a promising target for green herbicide discovery. However, the ligand configuration effects on PPO activity were still poorly understood. Herein, we designed 3-(N-phenyluracil)but-2-enoates using our previously developed active fragments exchange and link (AFEL) approach and synthesized a series of novel compounds with nanomolar ranges of Nicotiana tabacum PPO (NtPPO) inhibitory potency and promising herbicidal potency. Our systematic structure−activity relationship investigations showed that the E isomers of 3-(N-phenyluracil)but-2-enoates displayed improved bioactivity than their corresponding Z isomers. Using molecular simulation studies, we found that the E isomers showed a relatively lower entropy change and could sample more stable binding conformation to the receptor than the Z isomers. Our density functional theory (DFT) calculations showed that the E isomers showed higher chemical reactivity and lower electronic chemical potential than their corresponding Z isomers. Compound E-Ic emerged as the optimal compound with a K i value of 3.0 nM against NtPPO, exhibiting a broader spectrum of weed control than saflufenacil at 37.5−75 g ai/ha and also safe to maize at 75 g ai/ha, which could be considered as a promising lead herbicide for further development.

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“…To address this challenge, efforts are called for to develop large-scale publicly accessible image datasets and benchmarking models. In recent years, a number of labeled weed datasets, alongside AI models trained on the datasets for performance benchmarking, have been created and released [13], such as DeepWeeds [14], Weed25 [15], CottonWeedID15 [16], and CottonWeedDet12 [17], just to name a few. The availability of these datasets inspires research into deep learning-based weed recognition [18].…”

Section: Introductionmentioning

confidence: 99%

OpenWeedGUI: An Open-Source Graphical Tool for Weed Imaging and YOLO-Based Weed Detection

Xu,

Lu,

Deng

2024

Electronics

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Weed management impacts crop yield and quality. Machine vision technology is crucial to the realization of site-specific precision weeding for sustainable crop production. Progress has been made in developing computer vision algorithms, machine learning models, and datasets for weed recognition, but there has been a lack of open-source, publicly available software tools that link imaging hardware and offline trained models for system prototyping and evaluation, hindering community-wise development efforts. Graphical user interfaces (GUIs) are among such tools that can integrate hardware, data, and models to accelerate the deployment and adoption of machine vision-based weeding technology. This study introduces a novel GUI called OpenWeedGUI, designed for the ease of acquiring images and deploying YOLO (You Only Look Once) models for real-time weed detection, bridging the gap between machine vision and artificial intelligence (AI) technologies and users. The GUI was created in the framework of PyQt with the aid of open-source libraries for image collection, transformation, weed detection, and visualization. It consists of various functional modules for flexible user controls and a live display window for visualizing weed imagery and detection. Notably, it supports the deployment of a large suite of 31 different YOLO weed detection models, providing flexibility in model selection. Extensive indoor and field tests demonstrated the competencies of the developed software program. The OpenWeedGUI is expected to be a useful tool for promoting community efforts to advance precision weeding technology.

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Weed25: A deep learning dataset for weed identification

Cited by 26 publications

References 35 publications

RVDR-YOLOv8: A Weed Target Detection Model Based on Improved YOLOv8

RVDR-YOLOv8: A Weed Target Detection Model Based on Improved YOLOv8

Understanding the Effects of Ligand Configuration on Protoporphyrinogen IX Oxidase with Rationally Designed 3-(N-Phenyluracil)but-2-enoates

OpenWeedGUI: An Open-Source Graphical Tool for Weed Imaging and YOLO-Based Weed Detection

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