An automatic classification grading of spinach seedlings water stress based on N-MobileNetXt

Abstract

This study proposed an automatic classification method for water stress level of spinach seedlings based on N-MobileNetXt network, solving inefficient water stress classification of spinach seedlings under complex background. Firstly, this study reconstructed the Sandglass Block in MobileNetXt model to effectively increase the model accuracy by adopting up-dimension and down-dimension, and introduced NAM and two-dimensional adaptive average pooling; 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 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. While, the model size is only 3.89 MB. Comparing the N-MobileNetXt model with the deep network models proposed in the past five years, the N-MobileNetXt model was superior to other network models in terms of parameters and accuracy of identification. The N-MobileNetXt exhibited a considerable advantage in the rapid and accurate classification of spinach seedlings, and can provide theoretical basis and technical support for automatic irrigation.