数字农科院2.0

Estimation of leaf area index for winter wheat at early stages based on convolutional neural networks

文献类型： 外文期刊

作者： Yunxia Li;Hongjie Liu;Juncheng Ma;Lingxian Zhang

作者机构：

关键词： Convolutional neural network;Early stages;Leaf area index;RGB images;Winter wheat

期刊名称： Computers and Electronics in Agriculture

ISSN： 0168-1699

年卷期： 2021 年 190.0 卷

页码：

收录情况： JCR（2021版） ; EI（2021版）

摘要： Leaf area index (LAI) is a key growth trait to characterize the winter wheat growth at early stages. However, there needs more study on the low cost and fine-scale estimation of LAI of winter wheat. In this study, a LAI estimation method of winter wheat at the early stages was proposed based on low-cost RGB images and deep learning. The time-series canopy images of winter wheat at early stages were collected for two consecutive growth seasons (growth season 2018 and 2019), based on which the proposed model, as well as the compared models, were built. In the following step, the performances of these models were compared and analyzed. The influences of the input image with different pixel resolutions and the network depth on the model performance were discussed. Moreover, transfer learning was used to test the generalization ability of the proposed model. The results showed that the proposed estimation model could reflect the time-series variation of LAI of winter wheat at early stages. The proposed model with the input image of 128 × 128 pixel resolution achieved the best performance (R2 = 0.82, NRMSE = 24.89%), outperforming the compared models. The generalization test showed that the proposed model had a good generalization ability, achieving accurate LAI estimations for growing season 2019. However, deepen the network by adding extra SAME convolutional layers could not improve the model performance. In conclusion, based on the convolutional neural network (CNN) and low-cost RGB images, the proposed model is fast and accurate in estimating the LAI of winter wheat at early stages. This method can meet the need for LAI estimation of winter wheat at early stages and provide support for growth monitoring and agronomic management of winter wheat at early stages.

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