Rice Nitrogen Nutrition Diagnosis Based on Convolutional Neural Network.

In order to rapidly and accurately diagnose and identify nitrogen stress in rice, a field experiment was conducted. Taking the super rice variety 'Liangyoupei 9' as material, 4 treatments of nitrogen application (0, 210, 300, and 390 kgµhm^-2) were set, and images of the first, second, and third leaves at the top of the rice plant were scanned and collected during the spikelet differentiation and the full heading stage. The SE block (squeeze-and- excitation block) module was added to each residual block of the ResNet34 in convolutional neural network (CNN), and the weight parameters trained on the ImageNet (ImageNet large scale visual recognition challenge) dataset were transferred to the nitrogen nutrition diagnosis model of rice. The feature extraction layer of ResNet34 was kept unchanged, and the pooling layer at the end of the model was replaced with a global average pooling layer. The improved network was used to extract features from rice images and train the optimal weight file. The results showed that the improved network achieved a testing accuracy of 98.13% during the spikelet differentiation stage and 99.46% during the full heading stage of rice. The convergence speed of the model was faster, and the accuracy was improved by more than 7% compared to the original network. Above results showed that it was feasible to add the SE block to the residual block of ResNet34 and use transfer learning to diagnose nitrogen nutrition in rice, which could effectively diagnose and identify the nitrogen nutrition of rice during the spikelet differentiation stage and the full heading stage. Above results provided reference for the diagnosis and identification of nutrient status in crops. [ABSTRACT FROM AUTHOR]