DualCMNet: a lightweight dual-branch network for maize variety identification based on multi-modal feature fusion.

INTRODUCTION

The accurate identification of maize varieties is of great significance to modern agricultural management and breeding programs. However, traditional maize seed classification methods mainly rely on single modal data, which limits the accuracy and robustness of classification. Additionally, existing multimodal methods face high computational complexity, making it difficult to balance accuracy and efficiency.

METHODS

Based on multi-modal data from 11 maize varieties, this paper presents DualCMNet, a novel dual-branch deep learning framework that utilizes a one-dimensional convolutional neural network (1D-CNN) for hyperspectral data processing and a MobileNetV3 network for spatial feature extraction from images. The framework introduces three key improvements: the HShuffleBlock feature transformation module for feature dimension alignment and information interaction; the Channel and Spatial Attention Mechanism (CBAM) to enhance the expression of key features; and a lightweight gated fusion module that dynamically adjusts feature weights through a single gate value. During training, pre-trained 1D-CNN and MobileNetV3 models were used for network initialization with a staged training strategy, first optimizing non-pre-trained layers, then unfreezing pre-trained layers with differentiated learning rates for fine-tuning.

RESULTS

Through 5-fold cross-validation evaluation, the method achieved a classification accuracy of 98.75% on the validation set, significantly outperforming single-modal methods. The total model parameters are only 2.53M, achieving low computational overhead while ensuring high accuracy.

DISCUSSION

This lightweight design enables the model to be deployed in edge computing devices, allowing for real-time identification in the field, thus meeting the practical application requirements in agricultural Internet of Things and smart agriculture scenarios. This study not only provides an accurate and efficient solution for maize seed variety identification but also establishes a universal framework that can be extended to variety classification tasks of other crops.