Search

In high-throughput phenotyping, recognizing individual plant categories is a vital support process for plant breeding. However, different plant categories have different fine-grained characteristics, i.e., intra-class variation and inter-class similarity, making the process challenging. Existing deep learning-based recognition methods fail to effectively address this recognition task under challenging requirements, leading to technical difficulties such as low accuracy and lack of generalization robustness. To address these requirements, this paper proposes PlantNet, a fine-grained network for plant recognition based on transfer learning and a bilinear convolutional neural network, which achieves high recognition accuracy in high-throughput phenotyping requirements. The network operates as follows. First, two deep feature extractors are constructed using transfer learning. The outer product of the different spatial locations corresponding to the two features is then calculated, and the bilinear convergence is computed for the different spatial locations. Finally, the fused bilinear vectors are normalized via maximum expectation to generate the network output. Experiments on a publicly available Arabidopsis dataset show that the proposed bilinear model performed better than related state-of-the-art methods. The interclass recognition accuracy of the four different species of Arabidopsis Sf-2, Cvi, Landsberg and Columbia are found to be 98.48%, 96.53%, 96.79% and 97.33%, respectively, with an average accuracy of 97.25%. Thus, the network has good generalization ability and robust performance, satisfying the needs of fine-grained plant recognition in agricultural production. [ABSTRACT FROM AUTHOR]