Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning

Wang, Yiding; Qin, Yuxin; Cui, Jiali

doi:10.3389/fpls.2021.645899

Cited by 38 publications

(26 citation statements)

References 38 publications

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“…The R 2 was 0.9722. Wang et al (2021) proposed an occlusion robust wheat spike counting algorithm based on EfficientDet-D0 with the CBAM attention module. It was the network that focused more on small wheat spikes with the counting accuracy which was 94% and the false detection rate was 5.8% on the GWHD dataset.…”

Section: Introductionmentioning

confidence: 99%

Wheat Spike Detection and Counting in the Field Based on SpikeRetinaNet

Cheng

Chen

et al. 2022

Front. Plant Sci.

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The number of wheat spikes per unit area is one of the most important agronomic traits associated with wheat yield. However, quick and accurate detection for the counting of wheat spikes faces persistent challenges due to the complexity of wheat field conditions. This work has trained a RetinaNet (SpikeRetinaNet) based on several optimizations to detect and count wheat spikes efficiently. This RetinaNet consists of several improvements. First, a weighted bidirectional feature pyramid network (BiFPN) was introduced into the feature pyramid network (FPN) of RetinaNet, which could fuse multiscale features to recognize wheat spikes in different varieties and complicated environments. Then, to detect objects more efficiently, focal loss and attention modules were added. Finally, soft non-maximum suppression (Soft-NMS) was used to solve the occlusion problem. Based on these improvements, the new network detector was created and tested on the Global Wheat Head Detection (GWHD) dataset supplemented with wheat-wheatgrass spike detection (WSD) images. The WSD images were supplemented with new varieties of wheat, which makes the mixed dataset richer in species. The method of this study achieved 0.9262 for mAP50, which improved by 5.59, 49.06, 2.79, 1.35, and 7.26% compared to the state-of-the-art RetinaNet, single-shot multiBox detector (SSD), You Only Look Once version3 (Yolov3), You Only Look Once version4 (Yolov4), and faster region-based convolutional neural network (Faster-RCNN), respectively. In addition, the counting accuracy reached 0.9288, which was improved from other methods as well. Our implementation code and partial validation data are available at https://github.com/wujians122/The-Wheat-Spikes-Detecting-and-Counting.

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Section: Introductionmentioning

confidence: 99%

Wheat Spike Detection and Counting in the Field Based on SpikeRetinaNet

Cheng

Chen

et al. 2022

Front. Plant Sci.

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“…Thus, models developed from the field are more realistic of real-world conditions for wheat cultivation. Several in-field spike detection and counting studies have been conducted (David et al, 2020(David et al, , 2021Xu et al, 2020;Wang et al, 2021). Among them, David et al (2021) constructed a more diverse and less noisy Global Wheat Head Detection (GWHD) dataset, which promoted the development of wheat spike detection.…”

Section: Introductionmentioning

confidence: 99%

Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model

Zhang

Steffenson

et al. 2022

Front. Plant Sci.

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Precise segmentation of wheat spikes from a complex background is necessary for obtaining image-based phenotypic information of wheat traits such as yield estimation and spike morphology. A new instance segmentation method based on a Hybrid Task Cascade model was proposed to solve the wheat spike detection problem with improved detection results. In this study, wheat images were collected from fields where the environment varied both spatially and temporally. Res2Net50 was adopted as a backbone network, combined with multi-scale training, deformable convolutional networks, and Generic ROI Extractor for rich feature learning. The proposed methods were trained and validated, and the average precision (AP) obtained for the bounding box and mask was 0.904 and 0.907, respectively, and the accuracy for wheat spike counting was 99.29%. Comprehensive empirical analyses revealed that our method (Wheat-Net) performed well on challenging field-based datasets with mixed qualities, particularly those with various backgrounds and wheat spike adjacence/occlusion. These results provide evidence for dense wheat spike detection capabilities with masking, which is useful for not only wheat yield estimation but also spike morphology assessments.

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“…With the breakthrough of hardware technology, deep learning (DL; Bengio et al, 2017 ) became the mainstream data processing method in recent years. Among many DL approaches, the convolutional neural network (CNN) is the most popular and representative one in computer vision and imaging processing communities ( Ioffe and Szegedy, 2015 ; Simonyan and Zisserman, 2015 ; Szegedy et al, 2015 ; He et al, 2016 ; Krizhevsky et al, 2017 ; Wang et al, 2021 ; Yang et al, 2021 ). Different from ML methods, CNN can integrate feature derivation, feature learning, and classifier into a single architecture.…”

Section: Introductionmentioning

confidence: 99%

Plant Species Classification Based on Hyperspectral Imaging via a Lightweight Convolutional Neural Network Model

et al. 2022

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In recent years, many image-based approaches have been proposed to classify plant species. Most methods utilized red green blue (RGB) imaging materials and designed custom features to classify the plant images using machine learning algorithms. Those works primarily focused on analyzing single-leaf images instead of live-crown images. Without considering the additional features of the leaves’ color and spatial pattern, they failed to handle cases that contained leaves similar in appearance due to the limited spectral information of RGB imaging. To tackle this dilemma, this study proposes a novel framework that combines hyperspectral imaging (HSI) and deep learning techniques for plant image classification. We built a plant image dataset containing 1,500 images of 30 different plant species taken by a 470–900 nm hyperspectral camera and designed a lightweight conventional neural network (CNN) model (LtCNN) to perform image classification. Several state-of-art CNN classifiers are chosen for comparison. The impact of using different band combinations as the network input is also investigated. Results show that using simulated RGB images achieves a kappa coefficient of nearly 0.90 while using the combination of 3-band RGB and 3-band near-infrared images can improve to 0.95. It is also found that the proposed LtCNN can obtain a satisfactory performance of plant classification (kappa = 0.95) using critical spectral features of the green edge (591 nm), red-edge (682 nm), and near-infrared (762 nm) bands. This study also demonstrates the excellent adaptability of the LtCNN model in recognizing leaf features of plant live-crown images while using a relatively smaller number of training samples than complex CNN models such as AlexNet, GoogLeNet, and VGGNet.

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Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning

Cited by 38 publications

References 38 publications

Wheat Spike Detection and Counting in the Field Based on SpikeRetinaNet

Wheat Spike Detection and Counting in the Field Based on SpikeRetinaNet

Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model

Plant Species Classification Based on Hyperspectral Imaging via a Lightweight Convolutional Neural Network Model

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