WheatNet: A lightweight convolutional neural network for high-throughput image-based wheat head detection and counting

Khaki, Saeed; Safaei, Nima; Pham, Hieu; Wang, Lizhi

doi:10.1016/j.neucom.2022.03.017

Cited by 63 publications

(26 citation statements)

References 32 publications

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“…Therefore, deep-learning methods have gained wide popularity in recent years ( Xiao et al., 2022 ). Image-based deep-learning methods have been well investigated for phenotypic analyses such as wheat head counting ( Khaki et al., 2022 ) and stress detection ( Wang et al., 2022 ). More deep-learning-based phenotypic applications have been reviewed ( Singh et al., 2018 ; Guo et al., 2020a ; Arya et al., 2022 ).…”

Section: Challenges and Future Perspectivesmentioning

confidence: 99%

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Tao

Tian

et al. 2022

Plant Communications

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Section: Challenges and Future Perspectivesmentioning

confidence: 99%

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Tao

Tian

et al. 2022

Plant Communications

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“…Compared with other deep convolution neural networks, TasselNetV2 + reduced the use of the video memory and would be able to analyze large size images efficiently. Similarly, Khaki et al (2022) proposed WheatNet for wheat head counting and its overall prediction error was 8.7%. One disadvantage of the counting directly through regressing network method was that this method can only obtain the panicle number.…”

Section: Introductionmentioning

confidence: 99%

Field rice panicle detection and counting based on deep learning

Wang

Yang

et al. 2022

Front. Plant Sci.

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Panicle number is directly related to rice yield, so panicle detection and counting has always been one of the most important scientific research topics. Panicle counting is a challenging task due to many factors such as high density, high occlusion, and large variation in size, shape, posture et.al. Deep learning provides state-of-the-art performance in object detection and counting. Generally, the large images need to be resized to fit for the video memory. However, small panicles would be missed if the image size of the original field rice image is extremely large. In this paper, we proposed a rice panicle detection and counting method based on deep learning which was especially designed for detecting rice panicles in rice field images with large image size. Different object detectors were compared and YOLOv5 was selected with MAPE of 3.44% and accuracy of 92.77%. Specifically, we proposed a new method for removing repeated detections and proved that the method outperformed the existing NMS methods. The proposed method was proved to be robust and accurate for counting panicles in field rice images of different illumination, rice accessions, and image input size. Also, the proposed method performed well on UAV images. In addition, an open-access and user-friendly web portal was developed for rice researchers to use the proposed method conveniently.

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“… Sadeghi-Tehran et al (2019) constructed wheat feature models and fed the models into convolutional neural networks to achieve semantic segmentation and automatic counting of wheat. In addition, TasselNetv2 ( Xiong et al, 2019 ), mobileNetV2 ( Khaki et al, 2021 ), YOLOV4 ( Yang et al, 2021 ), EfficientDet ( Wang et al, 2021 ), LPNet ( Misra et al, 2020 ), and other deep-learning networks have shown advantages in wheat counting.…”

Section: Introductionmentioning

confidence: 99%

Rapid Detection of Wheat Ears in Orthophotos From Unmanned Aerial Vehicles in Fields Based on YOLOX

et al. 2022

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Wheat ears in unmanned aerial vehicles (UAV) orthophotos are characterized by occlusion, small targets, dense distribution, and complex backgrounds. Rapid identification of wheat ears in UAV orthophotos in a field environment is critical for wheat yield prediction. Three improvements were achieved based on YOLOX-m: mosaic optimized, using BiFPN structure, and attention mechanism, then ablation experiments were performed to verify the effect of each improvement. Three scene datasets were established: images were acquired during three different growing periods, at three planting densities, and under three scenarios of UAV flight heights. In ablation experiments, three improvements had increased recognition accuracies on the experimental dataset. Compared the accuracy of the standard model with our improved model on three scene datasets. Our improved model during three different periods, at three planting densities, and under three scenarios of the UAV flight height, obtaining 88.03%, 87.59%, and 87.93% accuracies, which were, respectively, 2.54%, 1.89%, and 2.15% better than the original model. The results of this study showed that the improved YOLOX-m model can achieve UAV orthophoto wheat recognition under different practical scenarios in large fields, and that the best combination were obtained images from the wheat milk stage, low planting density, and low flight altitude.

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WheatNet: A lightweight convolutional neural network for high-throughput image-based wheat head detection and counting

Cited by 63 publications

References 32 publications

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Field rice panicle detection and counting based on deep learning

Rapid Detection of Wheat Ears in Orthophotos From Unmanned Aerial Vehicles in Fields Based on YOLOX

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