Betty Dewi Puspasari, I-Cheng Chang, Andy Pramono, Titiek Yulianti
Accurate identification of plant leaf diseases is essential for modern agriculture. This paper presents SynerFANet, a hybrid deep learning framework designed to improve disease detection in sugarcane leaves. SynerFANet integrates two core modules: AdaptiveMBNet and WiseAttentionNet for comprehensive feature extraction and processing. AdaptiveMBNet combines MBConv layers with attention mechanisms to improve feature quality and reduce computation, enabling more accurate disease detection. WiseAttentionNet incorporates attention mechanisms into depthwise and expansion layers to enhance feature recalibration. The combination of the two cores inside SynerFANet can improve the representation capacity and robustness of the overall model. We evaluate the model using two datasets: a new proposed field-collected SugarLeaf-IDN dataset and the publicly available PlantVillage dataset. SynerFANet achieves superior accuracy with a moderate parameter size and GFLOPs, providing a balanced trade-off between predictive performance and computational cost, and exhibiting stable convergence during training. SynerFANet achieves 95.81% validation accuracy on our challenging real-world SugarLeaf-IDN dataset and 99.85% (SOTA) on the controlled PlantVillage benchmark. © 2026 Informa UK Limited, trading as Taylor & Francis Group.
Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien County, Taiwan; Department of Informatics Engineering, Industrial Engineering Faculty, Institut Teknologi Nasional Malang, East Java, Malang, Indonesia; Department of Animation, State University of Malang, Malang, Indonesia; Department is Horticultural and Estate Crops Research Centre, Research Organization of Agriculture and Food National Research and Innovation Agency, Jakarta, Indonesia
