Multi-stream hybrid architecture based on cross-level fusion strategy for fine-grained crop species recognition in precision agriculture

•Crops species recognition in agricultural scenes belongs to fine-grained visual classification lacking of effective solutions.•Large-scale dataset collected by various IoT sensors provides better benchmark for agricultural applications.•Multi-granulometric information are preferable to distinguish inter-class discrepancy and intra-class variances.•Cross-level fusion strategy improve the fine-grained recognition performance of proposedmulti-steam hybrid architecture. Precision farming aims to optimizing the crop production process and managing sustainable supply chain practices as more efficient and reasonable as possible. Recently, various advanced technologies, such as deep-learning and internet of things (IoT), have achieved remarkable intelligence progress in realistic agricultural conditions. However, crops species recognition can be considered as fine-grained visual classification (FGVC) problem, suffering the low inter-class discrepancy and high intra-class variances from the subordinate categories, which is more challenging than common basic-level category classification depended on traditional deep neural networks (DNNs). This paper presents a fine-grained visual recognition model named as MCF-Net to classifying different crop species in practical farmland scenes. Proposed MCF-Net is consisted of cross stage partial network (CSPNet) as backbone module, three parallel sub-networks, and cross-level fusion module. With multi-stream hybrid architecture utilizing massive fine-granulometric information, MCF-Net obtains preferable representation ability for distinguishing interclass discrepancy and tolerating intra-class variances. In addition, the end-to-end implementation of MCF-Net is optimized by cross-level fusion strategy to accurately identify different crop categories. Several experiments on in-field CropDeepv2 datasets demonstrate that our method favorably against the state-of-the-art methods. The recognition accuracy and F1-score of MCF-Net achieves very competitive results up to 90.6% and 0.962 separately, both of which outperforming contrasted models indicate better recognition accuracy and model stability of our method. Moreover, the overall parameters of MCF-Net are only 807 MByte with achieving a good balance between model’s performance and complexity. It is acceptable and suitable to the implementation of IoT platforms in precision agricultural practices.