Estimation Model of Corn Leaf Area Index Based on Improved CNN

In response to the issues of high complexity and low efficiency associated with the current reliance on manual sampling and instrumental measurement for obtaining maize leaf area index (LAI), this study constructed a maize image dataset comprising 624 images from three growth stages of summer maize in the Henan region, namely the jointing stage, small trumpet stage, and large trumpet stage. Furthermore, a maize LAI estimation model named LAINet, based on an improved convolutional neural network (CNN), was proposed. LAI estimation was carried out at these three key growth stages. In this study, the output structure was improved based on the ResNet architecture to adapt to regression tasks. The Triplet module was introduced to achieve feature fusion and self-attention mechanisms, thereby enhancing the accuracy of maize LAI estimation. The model structure was adjusted to enable the integration of growth-stage information, and the loss function was improved to accelerate the convergence speed of the network model. The model was validated on the self-constructed dataset. The results showed that the incorporation of attention mechanisms, integration of growth-stage information, and improvement of the loss function increased the model’s R 2 by 0.04, 0.15, and 0.05, respectively. Among these, the integration of growth-stage information led to the greatest improvement, with the R 2 increasing directly from 0.54 to 0.69. The improved model, LAINet, achieved an R 2 of 0.81, which indicates that it can effectively estimate the LAI of maize. This model can provide information technology support for the phenotypic monitoring of field crops.