ndows 11 (64 bit), and an Nvidia GeForce RTX 3090 24 GB graphics card with Nvidia Ampere architecture. All of the models used the processed grayscale images of 224 × 224 pixels as the input and the estimated buckwheat height as the output. The codes of the models with training results are available at the following GitHub link: https://github.com/18801389568/Buckwheat-height-estimation (accessed on 26 July 2023). Figure 5. Construction method of the buckwheat crop height estimation models. Training the models was essentially a process of continually updating the trainable parameters of each model in order to make the crop height estimation results increasingly accurate. Considering the quant
Open resource ↗18801389568/Buckwheat-height-estimation · pdf-raw-page:6 lines:1-60Unverified paper record
Buckwheat Plant Height Estimation Based on Stereo Vision and a Regression Convolutional Neural Network under Field Conditions
Agronomy · 1 Sept 2023 · 10.3390/agronomy13092312
Abstract
Buckwheat plant height is an important indicator for producers. Due to the decline in agricultural labor, the automatic and real-time acquisition of crop growth information will become a prominent issue for farms in the future. To address this problem, we focused on stereo vision and a regression convolutional neural network (CNN) in order to estimate buckwheat plant height. MobileNet V3 Small, NasNet Mobile, RegNet Y002, EfficientNet V2 B0, MobileNet V3 Large, NasNet Large, RegNet Y008, and EfficientNet V2 L were modified into regression CNNs. Through a five-fold cross-validation of the modeling data, the modified RegNet Y008 was selected as the optimal estimation model. Based on the depth and contour information of buckwheat depth image, the mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and mean relative error (MRE) when estimating plant height were 0.56 cm, 0.73 cm, 0.54 cm, and 1.7%, respectively. The coefficient of determination (R2) value between the estimated and measured results was 0.9994. Combined with the LabVIEW software development platform, this method can estimate buckwheat accurately, quickly, and automatically. This work contributes to the automatic management of farms.
Plant phenotyping relevance
ステレオビジョンと回帰CNNにより、圃場でのソバ草丈を自動推定する手法を開発・検証しており、植物表現型の取得方法が研究の中心です。
abstractwe focused on stereo vision and a regression convolutional neural network (CNN) in order to estimate buckwheat plant height.
abstractThrough a five-fold cross-validation of the modeling data, the modified RegNet Y008 was selected as the optimal estimation model.
abstractthis method can estimate buckwheat accurately, quickly, and automatically.
Code and data availability
The paper's buckwheat height estimation model code (modified regression CNNs with training results) is publicly available via an authors' GitHub repository explicitly stated in the text. The phenotype dataset (depth images with height labels) is only available by contacting the authors, so it is not a public asset.
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