Drought stress impact on the performance of deep convolutional neural networks for weed detection in Bahiagrass

Zhuang, Jiayao; Jin, Xiaoyue; Chen, Yong; Meng, Weibin; Wang, Yundi; Yu, Jialin; Muthukumar, Bagavathiannan

doi:10.1111/gfs.12583

Cited by 9 publications

(6 citation statements)

References 44 publications

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“…This limitation may be overcome by increasing the number of training images. Zhuang et al [28] obtained similar low P and R values when using YOLOv3 for R. scabra detection in drought-stressed and unstressed turfgrasses. In this research, authors argued that high background variability in the training dataset increases cause a less efficient feature extraction and consequently decreases P and R metrics.…”

Section: Resultsmentioning

confidence: 81%

Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios

et al. 2023

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The advancement of computer vision technology has allowed for the easy detection of weeds and other stressors in turfgrasses and agriculture. This study aimed to evaluate the feasibility of single shot object detectors for weed detection in lawns, which represents a difficult task. In this study, four different YOLO (You Only Look Once) object detectors version, along with all their various scales, were trained on a public ‘Weeds’ dataset with 4203 digital images of weeds growing in lawns with a total of 11,385 annotations and tested for weed detection in turfgrasses. Different weed species were considered as one class (‘Weeds’). Trained models were tested on the test subset of the ‘Weeds’ dataset and three additional test datasets. Precision (P), recall (R), and mean average precision (mAP_0.5 and mAP_0.5:0.95) were used to evaluate the different model scales. YOLOv8l obtained the overall highest performance in the ‘Weeds’ test subset resulting in a P (0.9476), mAP_0.5 (0.9795), and mAP_0.5:0.95 (0.8123), while best R was obtained from YOLOv5m (0.9663). Despite YOLOv8l high performances, the outcomes obtained on the additional test datasets have underscored the necessity for further enhancements to address the challenges impeding accurate weed detection.

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Section: Resultsmentioning

confidence: 81%

Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios

et al. 2023

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“…Previous research 50 has demonstrated that increasing the number of training images has a positive impact on the performance of neural networks, resulting in higher accuracy values approaching 1. Consequently, as the number of training images increased, the comparative improvement of SSL methods over the FSL wileyonlinelibrary.com/journal/ps model diminished.…”

Section: Resultsmentioning

confidence: 99%

“…2, each image block was assigned one of two labels: 'turf' or 'weed', with 'turf' representing the sub-images without weeds, whereas 'weed' representing the sub-images containing weeds. To evaluate the performance of the SSL method on varying numbers of labeled images, the labeled data were divided into four quantity levels (50,100,200, and 400 labels per class) per data set, as shown in Table 1. All data sets comprise training data with labels, unlabeled training data, validation data, and testing data.…”

Section: Data Set Preparationmentioning

confidence: 99%

Semi‐supervised learning methods for weed detection in turf

Liu,

Zhai,

et al. 2024

Pest Management Science

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BACKGROUNDAccurate weed detection is a prerequisite for precise automatic precision herbicide application. Previous research has adopted the laborious and time‐consuming approach of manually labeling and processing large image data sets to develop deep neural networks for weed detection. This research introduces a novel semi‐supervised learning (SSL) approach for detecting weeds in turf. The performance of SSL was compared with that of ResNet50, a fully supervised learning (FSL) method, in detecting and differentiating sub‐images containing weeds from those containing only turfgrass.RESULTSCompared with ResNet50, the evaluated SSL methods, Π‐model, Mean Teacher, and FixMatch, increased the classification accuracy by 2.8%, 0.7%, and 3.9%, respectively, when only 100 labeled images per class were utilized. FixMatch was the most efficient and reliable model, as it exhibited higher accuracy (≥0.9530) and F1 scores (≥0.951) with fewer labeled data (50 per class) in the validation and testing data sets than the other neural networks evaluated.CONCLUSIONThese results reveal that the SSL deep neural networks are capable of being highly accurate while requiring fewer labeled training images, thus being more time‐ and labor‐efficient than the FSL method. © 2024 Society of Chemical Industry.

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“…Weed detection using machine vision relies on features, like plant color, leaf texture, shape and patterns. Drought stress can impact leaf color and morphological features in plants, potentially affecting the reliability of machine vision-based weed detection [89]. But they still lack universal segmentation capabilities for different crop varieties with varying leaf shapes and canopy structures.…”

Section: Biological Morphological Featuresmentioning

confidence: 99%

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

Qu,

2024

Agronomy

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Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To achieve precise crop identification, it is essential to employ suitable sensors and optimized algorithms. Deep learning plays a crucial role in enhancing weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, effectively reducing the overall cost of agricultural production. This paper provides a thorough overview of the application of deep learning for crop and weed recognition in smart agricultural equipment. Starting with an overview of intelligent agricultural tools, sensors and identification algorithms, the discussion delves into instructive examples, showcasing the technology’s prowess in distinguishing between weeds and crops. The narrative highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing prospects. By marrying cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture.

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Drought stress impact on the performance of deep convolutional neural networks for weed detection in Bahiagrass

Cited by 9 publications

References 44 publications

Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios

Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios

Semi‐supervised learning methods for weed detection in turf

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

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