Deep Learning Techniques for In-Crop Weed Identification: A Review

Hu, Kun; Wang, Zhiyong; Coleman, Guy; Bender, Asher; Yao, Tingting; Zeng, Shan; Song, Dezhen; Schumann, Arnold; Walsh, Michael

doi:10.48550/arxiv.2103.14872

Cited by 6 publications

(6 citation statements)

References 66 publications

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“…Compared with traditional machine learning, deep learning uses a convolutional neural network to extract multi-scale and multi-dimensional spatial semantic feature information of weeds and independently obtains useful features of the target, which solves the disadvantages of traditional methods to extract weeds and crop features and effectively improves the recognition and detection accuracy of crops and weeds. In recent years, deep learning methods have been widely used in the identification and location of crops and weeds [26].…”

Section: Introductionmentioning

confidence: 99%

Accurate Detection and Precision Spraying of Corn and Weeds Using the Improved YOLOv5 Model

et al. 2023

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Section: Introductionmentioning

confidence: 99%

Accurate Detection and Precision Spraying of Corn and Weeds Using the Improved YOLOv5 Model

et al. 2023

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“…Out of a total of 221 authors in these papers, the presented 13 authors in Figure 11 have the strongest co-authorship links. The colors red and green represent two clusters of co-authorship links with the author Arnold W. Schumann participating in the red cluster (in the years 2020 and 2021) [ 38 ] as well as in the green cluster (in 2019) [ 80 , 81 ].…”

Section: Slr Methodologymentioning

confidence: 99%

“…In [ 38 ], the authors discuss DL techniques and architecture. In the former, they discuss Artificial Neural Networks (ANN), CNN, and Graph Convolutional Networks (GCN), and in the latter, they discuss image classification, object detection, semantic segmentation, and instance segmentation.…”

Section: Related Surveysmentioning

confidence: 99%

Weed Detection Using Deep Learning: A Systematic Literature Review

Murad

Mahmood

Forkan

et al. 2023

Sensors

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Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.

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“…Deep learning uses a convolutional neural network to extract diverse and multi-dimensional features of weeds, solving the limitations of traditional methods and improving crop and weed detection accuracy [14] . Over the past few years, the utilization of deep learning techniques has become increasingly prevalent in recognizing and pinpointing both crops and weeds [15] . Sun et al [16] used a combination of convolution and full pooling to classify weeds and seedlings for identification.…”

Section: Introductionmentioning

confidence: 99%

A study based on weed image recognition of a corn-weed control robot

Li,

Zhao

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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Based on the ability of YOLOv5 algorithm for maize weed image recognition, an improved model is proposed to promote the precision of the model for weed identification by adding the attention mechanism ACmix. The backbone network module has been enhanced by incorporating the ACmix module to substitute the 3×3 convolution kernel, while the detection head part has been upgraded through replacement of the 3×3 convolution with the CBAM attention mechanism. The detection accuracy of the model is improved by learning the feature map information at multiple target scales simultaneously. To explore the performance of the model, we collected large number of corn and weed images, built our own dataset and labeled it using the Labelimg tool. Regarding the preprocessing of data, techniques including data augmentation were utilized to improve the model's ability to generalize. We also performed experimental validation using the dataset, and the results show that our improved model achieves significant improvements in both detection and classification tasks of corn weed images compared to the original YOLOv5 algorithm. Finally, we conducted comparative experiments on the ACmix attention mechanism and compared it with other commonly used attention mechanisms. Through training experiments on the established dataset, it is found that the improved model achieves 94.3%precision and 82.5 %recall. Compared with the original YOLOv5 model, the precision improved by 3.0% and recall improved by 1.5%; compared with YOLOv4, the precision improved by 4.6% and recall improved by 1.8%. The results show that the ACmix attention mechanism has advantages in improving model performance. In summary, this paper proposes an improved method based on YOLOv5 deep learning neural network to improve the accuracy of maize weed image recognition by adding the attention mechanism ACmix, which has practical application value. A corn seedling weed dataset was established to provide a data base for subsequent research on corn weeds.

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Deep Learning Techniques for In-Crop Weed Identification: A Review

Cited by 6 publications

References 66 publications

Accurate Detection and Precision Spraying of Corn and Weeds Using the Improved YOLOv5 Model

Accurate Detection and Precision Spraying of Corn and Weeds Using the Improved YOLOv5 Model

Weed Detection Using Deep Learning: A Systematic Literature Review

A study based on weed image recognition of a corn-weed control robot

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