CO-ResNetRS50-SSL: Enhanced convolution and semi-supervised learning for accurate rice growth stage recognition in complex field conditions

Abstract

Effective crop management decisions, such as fertilization, irrigation, and crop protection, are closely tied to the crop growth stages. Precise identification of development stages is essential to optimize management practices in line with crop needs. While deep learning has shown promise in identifying growth stages, existing models often face challenges due to limited data availability and reduced accuracy in complex field conditions. To overcome these limitations, this study proposes a semi-supervised image classification method built on an enhanced ResNetRS50 architecture, named CO-ResNetRS50-SSL. This model leverages ResNetRS50 as its backbone, integrating Coordinate Attention (CA) for improved positional feature extraction and Omni-Dimensional Dynamic Convolution (ODConv) to enhance the adaptability of convolutional kernels to varying targets. Additionally, a semi-supervised learning framework is employed to boost generalization while minimizing dependence on labeled data. Ablation experiments show that semi-supervised learning boosted ResNetRS50’s accuracy from 88.58 % to 89.36 %. Adding Coordinate Attention further increased accuracy to 89.89 %, while incorporating ODConv in the final CO-ResNetRS50-SSL model achieved 90.38 % accuracy, 90.59 % precision, and 90.19 % F1 score (with 65.38 M parameters). Comparisons reveal that CO-ResNetRS50-SSL outperforms state-of-the-art models (FasterNet-T1, ShuffleNetV2, Swin Transformer, Vision Transformer, ConvNeXt-base) with highly significant differences (p < 0.001) and delivers robust performance across rice growth stages, with an optimal trade-off at 224 × 224 resolution. CO-ResNetRS50-SSL can accurately detect rice growth stages with limited labeled data, and its improvements in accuracy and generalization are expected to enhance decision-making in precision agriculture, optimizing resource allocation, reducing inputs, and advancing progress in the field of digital agriculture. Future work will focus on improving efficiency in utilizing unlabeled data, ensuring more balanced performance across different growth stages, and enhancing the model’s adaptability to other crops and more complex agricultural scenarios.