SSRNet: In-Field Counting Wheat Ears Using Multi-Stage Convolutional Neural Network

Wang, Daoyong; Zhang, Dongyan; Yang, Guijun; Xu, Bo; Luo, Yaowu; Yang, Xiaodong

doi:10.1109/tgrs.2021.3093041

Cited by 26 publications

(14 citation statements)

References 34 publications

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“…State-of-the-art deep learning object detection algorithms have made significant progress in wheat spike detection in images [34,35]. The success of the wheat spike detection led to the high accuracy of in-field spike counting in former works [36][37][38][39]. However, small-sized, highly dense, and overlapping wheat spikes in UAV images can easily lead to error detection and miss detection.…”

Section: Introductionmentioning

confidence: 99%

A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5

et al. 2021

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Deep-learning-based object detection algorithms have significantly improved the performance of wheat spike detection. However, UAV images crowned with small-sized, highly dense, and overlapping spikes cause the accuracy to decrease for detection. This paper proposes an improved YOLOv5 (You Look Only Once)-based method to detect wheat spikes accurately in UAV images and solve spike error detection and miss detection caused by occlusion conditions. The proposed method introduces data cleaning and data augmentation to improve the generalization ability of the detection network. The network is rebuilt by adding a microscale detection layer, setting prior anchor boxes, and adapting the confidence loss function of the detection layer based on the IoU (Intersection over Union). These refinements improve the feature extraction for small-sized wheat spikes and lead to better detection accuracy. With the confidence weights, the detection boxes in multiresolution images are fused to increase the accuracy under occlusion conditions. The result shows that the proposed method is better than the existing object detection algorithms, such as Faster RCNN, Single Shot MultiBox Detector (SSD), RetinaNet, and standard YOLOv5. The average accuracy (AP) of wheat spike detection in UAV images is 94.1%, which is 10.8% higher than the standard YOLOv5. Thus, the proposed method is a practical way to handle the spike detection in complex field scenarios and provide technical references for field-level wheat phenotype monitoring.

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Section: Introductionmentioning

confidence: 99%

A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5

et al. 2021

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“…The performance of rape counting model is analyzed by using the common evaluation indexes of regression counting, which are Average Accuracy (Acc), Mean Absolute Error (MAE), Mean Squared Error (MSE), root Mean Absolute Error (rMAE), root Mean Squared Error (rMSE), relative root Mean Square Error (rrMSE) and [ 28 , 29 , 47 – 49 ]. The smaller the values of the metrics MAE, MSE, rMAE, rMSE, and rrMSE, the closer the values of the metrics Acc, and are to 1, which indicates the better performance of the model.…”

Section: Methodsmentioning

confidence: 99%

Automatic rape flower cluster counting method based on low-cost labelling and UAV-RGB images

Wang

Qiao

et al. 2023

Plant Methods

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Background The flowering period is a critical time for the growth of rape plants. Counting rape flower clusters can help farmers to predict the yield information of the corresponding rape fields. However, counting in-field is a time-consuming and labor-intensive task. To address this, we explored a deep learning counting method based on unmanned aircraft vehicle (UAV). The proposed method developed the in-field counting of rape flower clusters as a density estimation problem. It is different from the object detection method of counting the bounding boxes. The crucial step of the density map estimation using deep learning is to train a deep neural network that maps from an input image to the corresponding annotated density map. Results We explored a rape flower cluster counting network series: RapeNet and RapeNet+. A rectangular box labeling-based rape flower clusters dataset (RFRB) and a centroid labeling-based rape flower clusters dataset (RFCP) were used for network model training. To verify the performance of RapeNet series, the paper compares the counting result with the real values of manual annotation. The average accuracy (Acc), relative root mean square error (rrMSE) and $$R^2$$ R 2 of the metrics are up to 0.9062, 12.03 and 0.9635 on the dataset RFRB, and 0.9538, 5.61 and 0.9826 on the dataset RFCP, respectively. The resolution has little influence for the proposed model. In addition, the visualization results have some interpretability. Conclusions Extensive experimental results demonstrate that the RapeNet series outperforms other state-of-the-art counting approaches. The proposed method provides an important technical support for the crop counting statistics of rape flower clusters in field.

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“…As a result, existing research on leaf tip detection has been carried out mainly under controlled conditions in greenhouses [ 23 , 25 ]. The sparse canopy structure, complex soil background, and small size of leaves require a very large annotation workload to achieve accurate enough leaf counts in the field [ 26 ]. Further, monitoring the dynamics of the leaf number requires an even larger amount of highly accurate annotated images to train the deep learning model [ 27 – 29 ].…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage

Zhan

Liu

et al. 2023

Plant Phenomics

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The number of leaves at a given time is important to characterize plant growth and development. In this work, we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB images. The digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages (150,000 images with over 2 million labels). The realism of the images was then improved using domain adaptation methods before training deep learning models. The results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset, collecting measurements from 5 countries obtained under different environments, growth stages, and lighting conditions with different cameras (450 images with over 2,162 labels). Among the 6 combinations of deep learning models and domain adaptation techniques, the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance ( R 2 = 0.94, root mean square error = 8.7). Complementary studies show that it is essential to simulate images with sufficient realism (background, leaf texture, and lighting conditions) before applying domain adaptation techniques. Furthermore, the spatial resolution should be better than 0.6 mm per pixel to identify leaf tips. The method is claimed to be self-supervised since no manual labeling is required for model training. The self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping problems. The trained networks are available at https://github.com/YinglunLi/Wheat-leaf-tip-detection .

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SSRNet: In-Field Counting Wheat Ears Using Multi-Stage Convolutional Neural Network

Cited by 26 publications

References 34 publications

A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5

A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5

Automatic rape flower cluster counting method based on low-cost labelling and UAV-RGB images

Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage

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