Recognition Method for Weeds in Rapeseed Field Based on Faster R-CNN Deep Network

张乐,; 金秀,; 傅雷扬,; 李绍稳,

doi:10.3788/lop57.021508

Cited by 5 publications

(1 citation statement)

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“…The partitioning of datasets is a crucial aspect of building and optimizing DL models and holds significant practical importance. Typically shown in Figure 6, the original dataset is divided into a training set, a validation set, and a test set in a certain proportion [57] . The training set is utilized for building the model, the validation set is utilized for determining the network structure or parameters that control the complexity of the model, and the test set is used to evaluate the performance of the final optimal model.…”

Section: Dataset Partitioningmentioning

confidence: 99%

Review of deep learning-based weed identification in crop fields

Hu,

Oliver Wane,

Zhu

et al. 2023

International Journal of Agricultural and Biological Engineering

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Automatic weed identification and detection are crucial for precision weeding operations. In recent years, deep learning (DL) has gained widespread attention for its potential in crop weed identification. This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL. Through an analysis of relevant literature from both within and outside of China, the author summarizes the development history, research progress, and identification and detection methods of DL-based weed identification technology. Emphasis is placed on data sources and DL models applied to different technical tasks. Additionally, the paper discusses the challenges of time-consuming and laborious dataset preparation, poor generality, unbalanced data categories, and low accuracy of field identification in DL for weed identification. Corresponding solutions are proposed to provide a reference for future research directions in weed identification.

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Section: Dataset Partitioningmentioning

confidence: 99%

Review of deep learning-based weed identification in crop fields

Hu,

Oliver Wane,

Zhu

et al. 2023

International Journal of Agricultural and Biological Engineering

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Image Classification for Soybean and Weeds Based on ViT

Liang

Wang

Ling

2021

J. Phys.: Conf. Ser.

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Abstracts. In this paper, ViT deep neural network based on self-attention mechanism is used in classification for images of soybean and weeds. Firstly, the overall image is split into multiple tiles; with each tile regarded as a word, the whole image is regarded as a sentence, which can be used for image semantic recognition by natural language processing technology. We designed a ViT network with sequence length of 50, embedded dimension of 384, and self-attention module layers of 12. With soybean weed classification dataset, the network is trained, verified and tested. Experimental results showed that ViT network is superior in classification on dataset of soybean and weeds, with excellent generalization capability.

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Research and Experiment on a Chickweed Identification Model Based on Improved YOLOv5s

Yu,

Zhao,

et al. 2024

Agronomy

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Currently, multi-layer deep convolutional networks are mostly used for field weed recognition to extract and identify target features. However, in practical application scenarios, they still face challenges such as insufficient recognition accuracy, a large number of model parameters, and slow detection speed. In response to the above problems, using chickweed as the identification object, a weed identification model based on improved YOLOv5s was proposed. Firstly, the Squeeze-and-Excitation Module (SE) and Convolutional Block Attention Module (CBAM) were added to the model’s feature extraction network to improve the model’s recognition accuracy; secondly, the Ghost convolution lightweight feature fusion network was introduced to effectively identify the volume, parameter amount, and calculation amount of the model, and make the model lightweight; finally, we replaced the loss function in the original target bounding box with the Efficient Intersection over Union (EloU) loss function to further improve the detection performance of the improved YOLOv5s model. After testing, the accuracy of the improved YOLOv5s model was 96.80%, the recall rate was 94.00%, the average precision was 93.20%, and the frame rate was 14.01 fps, which were improved by 6.6%, 4.4%, 1.0%, and 6.1%, respectively, compared to the original YOLOv5s model. The model volume was 9.6 MB, the calculation amount was 13.6 GB, and the parameter amount was 5.9 MB, which decreased by 29.4%, 14.5%, and 13.2% compared with the original YOLOv5s model, respectively. This model can effectively distinguish chickweed between crops. This research can provide theoretical and technical support for efficient identification of weeds in complex field environments.

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Recognition Method for Weeds in Rapeseed Field Based on Faster R-CNN Deep Network

Cited by 5 publications

References 0 publications

Review of deep learning-based weed identification in crop fields

Review of deep learning-based weed identification in crop fields

Image Classification for Soybean and Weeds Based on ViT

Research and Experiment on a Chickweed Identification Model Based on Improved YOLOv5s

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