2023
DOI: 10.34133/plantphenomics.0109
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Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet

Jianqing Zhao,
Yucheng Cai,
Suwan Wang
et al.

Abstract: Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background. Existing models for wheat spike detection are typically designed for a specific growth stage. Their adaptability to other growth stages or field scenes is limited. Suc… Show more

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Cited by 11 publications
(1 citation statement)
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“…Yan et al [18] developed a method for refining the scale of detection layers in a wheat spike detection network using the deep learning interpretation method GradCAM. Zhao et al [19] introduced WheatNet for detecting wheat spikes from the filling to maturity stages. CNNs have a strong local perception ability, enabled by stacking multiple convolutional layers to expand the field of view and effectively capture local features in images.…”
Section: Introductionmentioning
confidence: 99%
“…Yan et al [18] developed a method for refining the scale of detection layers in a wheat spike detection network using the deep learning interpretation method GradCAM. Zhao et al [19] introduced WheatNet for detecting wheat spikes from the filling to maturity stages. CNNs have a strong local perception ability, enabled by stacking multiple convolutional layers to expand the field of view and effectively capture local features in images.…”
Section: Introductionmentioning
confidence: 99%