Open Access

ARTICLE

An Automatic Classification Grading of Spinach Seedlings Water Stress Based on N-MobileNetXt

Yanlei Xu, Xue Cong, Yuting Zhai, Zhiyuan Gao, Helong Yu^*

School of Information Technology, Jilin Agricultural University, Changchun, 130118, China

* Corresponding Author: Helong Yu. Email:

Intelligent Automation & Soft Computing 2023, 37(3), 3019-3037. https://doi.org/10.32604/iasc.2023.040330

Received 14 March 2023; Accepted 24 May 2023; Issue published 11 September 2023

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.

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Keywords

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