Maize seedling information extraction from UAV images based on semi-automatic sample generation and Mask R-CNN model

Gao, Xiang; Zan, Xuli; Yang, Shuai; Zhang, Runda; Chen, Shuaiming; Zhang, Xiaodong; Liu, Zhe; Ma, Yuntao; Zhao, Yuanyuan; Li, Shaoming

doi:10.1016/j.eja.2023.126845

Cited by 8 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies in crop quantity estimation with ultrahigh-definition digital images primarily used deep learning techniques. For instance, Vong et al and Gao et al [35] employed the U-Net and Mask R-CNN models, respectively, for image segmentation. In another approach involving the use of object detection techniques in deep learning, Liu et al [34] selected YOLOv3, while Xu et al [36] and Cardellicchio et al [67] opted for YOLOv5 for accurate crop quantity estimations.…”

Section: Advantages Of the Proposed Methodsmentioning

confidence: 99%

“…Especially in terms of the estimations of maize quantities, Liu et al [34] used you only look once (YOLO) v3 to estimate the maize plant counts with a precision of 96.99%. Gao et al [35] fine-tuned the Mask Region-convolutional neural network (R-CNN) model for automatic maize identification, achieving an average precision (AP) @ 0.5 intersection over union (IOU) of 0.729 and an average recall (AR)@ 0.5IOU of 0.837. Xu et al [36], Xiao et al [22], and others used the YOLOv5 model to estimate the maize plant counts from UAV digital images.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mapping Maize Planting Densities Using Unmanned Aerial Vehicles, Multispectral Remote Sensing, and Deep Learning Technology

Shen,

Wang,

Zhao

et al. 2024

Drones

View full text Add to dashboard Cite

Maize is a globally important cereal and fodder crop. Accurate monitoring of maize planting densities is vital for informed decision-making by agricultural managers. Compared to traditional manual methods for collecting crop trait parameters, approaches using unmanned aerial vehicle (UAV) remote sensing can enhance the efficiency, minimize personnel costs and biases, and, more importantly, rapidly provide density maps of maize fields. This study involved the following steps: (1) Two UAV remote sensing-based methods were developed for monitoring maize planting densities. These methods are based on (a) ultrahigh-definition imagery combined with object detection (UHDI-OD) and (b) multispectral remote sensing combined with machine learning (Multi-ML) for the monitoring of maize planting densities. (2) The maize planting density measurements, UAV ultrahigh-definition imagery, and multispectral imagery collection were implemented at a maize breeding trial site. Experimental testing and validation were conducted using the proposed maize planting density monitoring methods. (3) An in-depth analysis of the applicability and limitations of both methods was conducted to explore the advantages and disadvantages of the two estimation models. The study revealed the following findings: (1) UHDI-OD can provide highly accurate estimation results for maize densities (R2 = 0.99, RMSE = 0.09 plants/m2). (2) Multi-ML provides accurate maize density estimation results by combining remote sensing vegetation indices (VIs) and gray-level co-occurrence matrix (GLCM) texture features (R2 = 0.76, RMSE = 0.67 plants/m2). (3) UHDI-OD exhibits a high sensitivity to image resolution, making it unsuitable for use with UAV remote sensing images with pixel sizes greater than 2 cm. In contrast, Multi-ML is insensitive to image resolution and the model accuracy gradually decreases as the resolution decreases.

show abstract

Section: Advantages Of the Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping Maize Planting Densities Using Unmanned Aerial Vehicles, Multispectral Remote Sensing, and Deep Learning Technology

Shen,

Wang,

Zhao

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

“…In the equation, P represents Precision, and R represents Recall. TP (True Positive) represents true positives, indicating the number of positive samples correctly predicted as positive; TN (True Negative) represents true negatives, indicating the number of negative samples correctly predicted as negative; FP (False Positive) represents false positives, indicating the number of negative samples incorrectly predicted as positive; and FN (False Negative) represents false negatives, indicating the number of positive samples incorrectly predicted as negative [26,27]. The calculation of each evaluation metric is as follows:…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

Research on Key Algorithm for Sichuan Pepper Pruning Based on Improved Mask R-CNN

Zhang,

Liang

et al. 2024

Sustainability

View full text Add to dashboard Cite

This Research proposes an intelligent pruning method based on the improved Mask R-CNN (Mask Region-based Convolutional Neural Network) model to address the shortcomings of intelligent pruning technology for Sichuan pepper trees. Utilizing ResNeXt-50 as the backbone network, the algorithm optimizes the anchor boxes in the RPN (Region Proposal Network) layer to adapt to the complex morphology of pepper tree branches, thereby enhancing target detection and segmentation performance. Further reducing the quantization error of the RoI (Region of Interest) Align layer through bilinear interpolation, the algorithm innovatively introduces edge loss (Ledge) into the loss function to address the issue of blurred edge features caused by the overlap between retained and pruned branches. Experimental results demonstrate the outstanding performance of the improved Mask R-CNN model in segmenting and identifying pepper tree branches, achieving recognition accuracies of 92.2%, 96.3%, and 85.6% for Upright branches, Centripetal branches, and Competitive branches, respectively, while elevating the recognition accuracy of retained branches to 94.4%. Compared to the original Mask R-CNN, the enhanced model exhibits a 6.7% increase in the recognition rate of retained branches and a decrease of 0.12 in loss value, significantly enhancing recognition effectiveness. The research findings not only provide an effective tool for the precise pruning of pepper trees but also offer valuable insights for implementing intelligent pruning strategies for other fruit trees.

show abstract

“…In the wake of deep learning, scholars have increasingly applied convolutional neural networks (CNNs) in agronomy research, with relatively good results in the identification of pests and diseases [32], classification and screening of weeds in the field [33], collection of key information about a crop [34], and crop growth prediction [35]. For instance, using a fine-tuned Mask region-based CNN (R-CNN) model, Gao et al [36] proposed an automatic maize seedling identification method that is adaptable to various developmental stages of maize seedlings to quickly and accurately extract phenotypic information from field environments while reducing labor costs. The model had an average detection accuracy of 88.70%, with average accuracies of detection of seedling emergence in 2019, 2020, and 2021 of 98.87%, 95.70%, and 98.77%, respectively.…”

Section: Introductionmentioning

confidence: 99%

A Phenotypic Extraction and Deep Learning-Based Method for Grading the Seedling Quality of Maize in a Cold Region

Zhang,

Lu,

Guan

et al. 2024

Agronomy

View full text Add to dashboard Cite

Background: Low-temperature stress significantly restricts maize germination, seedling growth and development, and yield formation. However, traditional methods of evaluating maize seedling quality are inefficient. This study established a method of grading maize seedling quality based on phenotypic extraction and deep learning. Methods: A pot experiment was conducted using different low-temperature combinations and treatment durations at six different stages between the sowing and seedling phases. Changes in 27 seedling quality indices, including plant morphology and photosynthetic performance, were investigated 35 d after sowing and seedling quality grades were classified based on maize yield at maturity. The 27 quality indices were extracted, and a total of 3623 sample datasets were obtained and grouped into training and test sets in a 3:1 ratio. A convolutional neural network-based grading method was constructed using a deep learning model. Results: The model achieved an average precision of 98.575%, with a recall and F1-Score of 98.7% and 98.625%, respectively. Compared with the traditional partial least squares and back propagation neural network, the model improved recognition accuracy by 8.1% and 4.19%, respectively. Conclusions: This study provided an accurate grading of maize seedling quality as a reference basis for the standardized production management of maize in cold regions.

show abstract

Maize seedling information extraction from UAV images based on semi-automatic sample generation and Mask R-CNN model

Cited by 8 publications

References 26 publications

Mapping Maize Planting Densities Using Unmanned Aerial Vehicles, Multispectral Remote Sensing, and Deep Learning Technology

Mapping Maize Planting Densities Using Unmanned Aerial Vehicles, Multispectral Remote Sensing, and Deep Learning Technology

Research on Key Algorithm for Sichuan Pepper Pruning Based on Improved Mask R-CNN

A Phenotypic Extraction and Deep Learning-Based Method for Grading the Seedling Quality of Maize in a Cold Region

Contact Info

Product

Resources

About