@Article{iasc.2023.040330,
AUTHOR = {Yanlei Xu, Xue Cong, Yuting Zhai, Zhiyuan Gao, Helong Yu},
TITLE = {An Automatic Classification Grading of Spinach Seedlings Water Stress Based on N-MobileNetXt},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {37},
YEAR = {2023},
NUMBER = {3},
PAGES = {3019--3037},
URL = {http://www.techscience.com/iasc/v37n3/54139},
ISSN = {2326-005X},
ABSTRACT = {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.},
DOI = {10.32604/iasc.2023.040330}
}