An image segmentation method based on deep learning for damage assessment of the invasive weed Solanum rostratum Dunal

Wang, Qifan; Xiao, Xuepeng; Yuan, Hongliang; Zhu, Jiajun; Fan, Caihu; Zhang, Jinlin

doi:10.1016/j.compag.2021.106320

Cited by 28 publications

(15 citation statements)

References 26 publications

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“…The SE-YOLOv5x model yielded an F1-score as high as 97.14%, and a test time of a single image of 19.1 ms. Table 5 shows the related research results on plant identification based on CNN in recent years. Specifically, Wang et al [29] proposed a DeepSolanum-Net model to identify solanum rostratum dunal plants. The model achieved an F1-score of 0.901 with a test time of 131.88 ms. Zou et al [30] developed a modified U-Net to segment the green bristlegrass in complex background, with an F1-score of 0.936 and a test time of 51.71 ms.…”

Section: Discussionmentioning

confidence: 99%

SE-YOLOv5x: An Optimized Model Based on Transfer Learning and Visual Attention Mechanism for Identifying and Localizing Weeds and Vegetables

Zhang

et al. 2022

Agronomy

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Weeds in the field affect the normal growth of lettuce crops by competing with them for resources such as water and sunlight. The increasing costs of weed management and limited herbicide choices are threatening the profitability, yield, and quality of lettuce. The application of intelligent weeding robots is an alternative to control intra-row weeds. The prerequisite for automatic weeding is accurate differentiation and rapid localization of different plants. In this study, a squeeze-and-excitation (SE) network combined with You Only Look Once v5 (SE-YOLOv5x) is proposed for weed-crop classification and lettuce localization in the field. Compared with models including classical support vector machines (SVM), YOLOv5x, single-shot multibox detector (SSD), and faster-RCNN, the SE-YOLOv5x exhibited the highest performance in weed and lettuce plant identifications, with precision, recall, mean average precision (mAP), and F1-score values of 97.6%, 95.6%, 97.1%, and 97.3%, respectively. Based on plant morphological characteristics, the SE-YOLOv5x model detected the location of lettuce stem emerging points in the field with an accuracy of 97.14%. This study demonstrates the capability of SE-YOLOv5x for the classification of lettuce and weeds and the localization of lettuce, which provides theoretical and technical support for automated weed control.

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Section: Discussionmentioning

confidence: 99%

SE-YOLOv5x: An Optimized Model Based on Transfer Learning and Visual Attention Mechanism for Identifying and Localizing Weeds and Vegetables

Zhang

et al. 2022

Agronomy

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“…In this improved algorithm, neither Equation (11) nor Equation (10) reduces the time complexity of the cuckoo search algorithm. The time complexity of the cuckoo search algorithm is still O(T × NP × D), where T represents the number of iterations, NP represents the scale of the CS algorithm, and D represents the dimension.…”

Section: Time Complexity Analysismentioning

confidence: 99%

“…R i, j = N i, j + r (N k, j + g) (11) where R i,j represents the nest location after variation. N i,j shows the nest location before variation.…”

Section: The Improved Cuckoo Algorithmmentioning

confidence: 99%

“…However, the existing UAV image processing solutions [7,8] may not achieve the ideal goal that well balances the above two mentioned demands. Some progress has been made in the areas of environmental monitoring [9], image analysis [10], image classification [11][12][13], change detection [14], and image retrieval of geography [15]. However, the effects of traditional methods of image edge detection are not satisfactory, as UAV images are usually high-resolution images.…”

Section: Introductionmentioning

confidence: 99%

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Quaternion‐based improved cuckoo algorithm for colour UAV image edge detection

Liu

2021

IET Image Processing

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With the extensive application of unmanned aerial vehicles (UAVs), there is an increasing demand for fast processing of coloured UAV images. The coloured UAV image pixels are usually represented by quaternion vectors with three bands of visible light corresponding to the three imaginary parts of the pure imaginary quaternion. Accordingly, the colour image edge points can be determined based on the quaternion polar coordinating the rotation principle. Here, a quaternion‐based improved cuckoo algorithm is proposed to perform fast processing for UAVs images. In particular, a novel guiding equation is used to optimize the positions of the improved cuckoo algorithm before the Levi flight. Furthermore, a novel disturbance equation is used to obtain a varied location for the next location after the Levi flight. Comprehensive experiments are conducted to evaluate the performance of the proposed solution. The experimental results showed that the proposed method significantly reduces the image processing time and remarkably improves the quality.

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“…We emphasize our approach for building a deep learning model in an effort to provide a beginning road map to aid conservation researchers considering the use of AI for drone-based census of other plant species. Many studies have used drones along with deep learning models to collect data in agriculture [10][11][12][13], and there are publications describing this approach for a variety of wild organisms, including plant species in general [14][15][16][17][18][19] and especially invasive species [20][21][22][23]. Interest in using drone imagery as a tool in rare plant conservation is increasing [14], but to our knowledge no published studies to date have successfully applied a deep learning approach to drone-acquired imagery with the goal of enumerating individuals of a rare plant species.…”

Section: Introductionmentioning

confidence: 99%

Drones, Deep Learning, and Endangered Plants: A Method for Population-Level Census Using Image Analysis

Rominger

Meyer

2021

Drones

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A census of endangered plant populations is critical to determining their size, spatial distribution, and geographical extent. Traditional, on-the-ground methods for collecting census data are labor-intensive, time-consuming, and expensive. Use of drone imagery coupled with application of rapidly advancing deep learning technology could greatly reduce the effort and cost of collecting and analyzing population-level data across relatively large areas. We used a customization of the YOLOv5 object detection model to identify and count individual dwarf bear poppy (Arctomecon humilis) plants in drone imagery obtained at 40 m altitude. We compared human-based and model-based detection at 40 m on n = 11 test plots for two areas that differed in image quality. The model out-performed human visual poppy detection for precision and recall, and was 1100× faster at inference/evaluation on the test plots. Model inference precision was 0.83, and recall was 0.74, while human evaluation resulted in precision of 0.67, and recall of 0.71. Both model and human performance were better in the area with higher-quality imagery, suggesting that image quality is a primary factor limiting model performance. Evaluation of drone-based census imagery from the 255 ha Webb Hill population with our customized YOLOv5 model was completed in <3 h and provided a reasonable estimate of population size (7414 poppies) with minimal investment of on-the-ground resources.

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An image segmentation method based on deep learning for damage assessment of the invasive weed Solanum rostratum Dunal

Cited by 28 publications

References 26 publications

SE-YOLOv5x: An Optimized Model Based on Transfer Learning and Visual Attention Mechanism for Identifying and Localizing Weeds and Vegetables

SE-YOLOv5x: An Optimized Model Based on Transfer Learning and Visual Attention Mechanism for Identifying and Localizing Weeds and Vegetables

Quaternion‐based improved cuckoo algorithm for colour UAV image edge detection

Drones, Deep Learning, and Endangered Plants: A Method for Population-Level Census Using Image Analysis

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