A hybrid CNN-LSTM model for diagnosing rice nutrient levels at the rice panicle initiation stage

Abstract

Nitrogen (N) and potassium (K) are two key mineral nutrient elements involved in rice growth. Accurate diagnosis of N and K status is very important for the rational application of fertilizers at a specific rice growth stage. Therefore, we propose a hybrid model for diagnosing rice nutrient levels at the early panicle initiation stage (EPIS), which combines a convolutional neural network (CNN) with an attention mechanism and a long short-term memory network (LSTM). The model was validated on a large set of sequential images collected by an unmanned aerial vehicle (UAV) from rice canopies at different growth stages during a two-year experiment. Compared with VGG16, AlexNet, GoogleNet, DenseNet, and inceptionV3, ResNet101 combined with LSTM obtained the highest average accuracy of 83.81% on the dataset of Huanghuazhan (HHZ, an indica cultivar). When tested on the datasets of HHZ and Xiushui 134 (XS134, a japonica rice variety) in 2021, the ResNet101-LSTM model enhanced with the squeeze-and-excitation (SE) block achieved the highest accuracies of 85.38 and 88.38%, respectively. Through the cross-dataset method, the average accuracies on the HHZ and XS134 datasets tested in 2022 were 81.25 and 82.50%, respectively, showing a good generalization. Our proposed model works with the dynamic information of different rice growth stages and can efficiently diagnose different rice nutrient status levels at EPIS, which are helpful for making practical decisions regarding rational fertilization treatments at the panicle initiation stage.

Keywords

• dynamic model of deep learning
• UAV
• rice panicle initiation
• nutrient level diagnosis
• image classification