Lightweight drought recognition model based on feature extraction of soybean multispectral images

Drought is an important stress factor restricting soybean's high yield and high quality. Rapid detection of soybean drought conditions is of great significance for scientific cultivation management and drought-resistant variety breeding. In view of the complex and diverse phenotypes of soybean canopy, the existing recognition algorithms have high feature dimensions and large amount of calculation, which are difficult to meet the requirements of lightweight models for portable devices to identify soybean drought. Thus, a lightweight soybean drought recognition model based on feature extraction and one-dimensional convolutional neural network is proposed in this paper. Firstly, the multispectral image of soybean canopy was taken as the research object, and ReliefF feature selection method was applied to extract 14 feature vectors from the original 37 phenotypic indicators calculated from soybean canopy image, and the correlation coefficient R² reached 0.886. Finally, based on the selected dataset of soybean canopy phenotypic features, a seven-layer one-dimensional convolutional neural network was constructed to achieve a lightweight recognition model for soybean canopy drought (ReliefF_Conv), with an accuracy of 95.67 % and a inference time of only 0.000009 s. Compared with Back Propagation(BP), Radial Basis Function Network(RBF), Random Forest(RF), Support Vector Machine(SVM), Long Short-Term Memory(LSTM) and MobileNet models, the accuracy of the proposed model is increased by 14.42 %, 8.17 %, 5.05 %, 1.92 %, 14.42 % and 14.42 %, respectively. Compared with the full-variable model (OD_Conv), the accuracy of the proposed model is increased by 9.16 %, the training parameters were reduced by 64.2 %, and the inference efficiency has also increased by 70 %. The results achieved rapid detection of drought traits of soybean, and could provide basis and reference for water-saving irrigation and precise decision-making in drought-resistant varieties breeding, environmental regulation and scientific management.