TY  - EJOU
AU  - Xu, Yanlei 
AU  - Cong, Xue 
AU  - Zhai, Yuting 
AU  - Gao, Zhiyuan 
AU  - Yu, Helong 

TI  - An Automatic Classification Grading of Spinach Seedlings Water Stress Based on N-MobileNetXt
T2  - Intelligent Automation \& Soft Computing

PY  - 2023
VL  - 37
IS  - 3
SN  - 2326-005X

AB  - To solve inefficient water stress classification of spinach seedlings under complex background, this study proposed an automatic classification method for the water stress level of spinach seedlings based on the N-MobileNetXt (NCAM+MobileNetXt) network. Firstly, this study reconstructed the Sandglass Block to effectively increase the model accuracy; secondly, this study introduced the group convolution module and a two-dimensional adaptive average pool, which can significantly compress the model parameters and enhance the model robustness separately; finally, this study innovatively proposed the Normalization-based Channel Attention Module (NCAM) to enhance the image features obviously. The experimental results showed that the classification accuracy of N-MobileNetXt model for spinach seedlings under the natural environment reached 90.35%, and the number of parameters was decreased by 66% compared with the original MobileNetXt model. The N-MobileNetXt model was superior to other network models such as ShuffleNet and GhostNet in terms of parameters and accuracy of identification. It can provide a theoretical basis and technical support for automatic irrigation.
KW  - Deep learning; water stress; grade classification; image processing; complex background

DO  - 10.32604/iasc.2023.040330