Rice nitrogen nutrition diagnosis based on stacking integrated convolutional neural network

Objectives To achieve rapid and accurate diagnosis of rice nitrogen nutrition status, we established a rice nitrogen nutrition diagnosis model involving stacking integrated convolution neural networks.

Methods In a rice field experiment, a super hybrid rice cultivar “Liangyoupeijiu” was used as the test material, and four N application levels (0, 210, 300, 390 kg/hm²) were the treatments. At the young panicle differentiation stage of rice, the images of the top three fully unfolded leaves were taken by scanning, and the images were cut into images containing leaf tip parts, and the rice leaf image dataset was established after preprocessing. A stacking integrated convolutional neural network model with different four combinations of three base learners (i.e., DenseNet121, ResNet50, InceptionResNet V2) and MLP as the meta learner was constructed. The results of the integrated models on nitrogen nutrition diagnosis task were compared with that of the single convolutional neural network model of different single base learners (i.e., DenseNet121, ResNet50, InceptionResNet V2, and VGG16).

Results Among the four single models, DenseNet121 had the highest accuracy of 96.41%. The accuracy rate of the binary integration model and the ternary integration model were higher than that of the single model. The accuracy rate of the stacking integration model was the highest, reaching 98.10%, with an increase of 1.69 percentage points compared with the single model which had the highest accuracy.

Conclusions The nitrogen nutrition diagnosis model established by stacking integrated convolution neural network has strong generalization ability and learning ability, and can accurately identify nitrogen nutrition status.