{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T06:19:21Z","timestamp":1787552361230,"version":"build-2736575974"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Accurate crop classification from unmanned aerial vehicle (UAV) imagery is essential for precision agriculture and food security monitoring. Existing approaches face challenges in balancing computational efficiency with classification accuracy, particularly when dealing with fine-grained crop categories and complex aerial backgrounds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This paper proposes MFK-Net, a computationally efficient hybrid architecture that integrates state-space models (Mamba), Fourier frequency-domain enhancement, and Kolmogorov-Arnold Networks (KAN) for efficient UAV crop classification. The proposed framework employs a streamlined Mamba backbone to capture long-range spatial dependencies with linear complexity, a learnable Fourier filtering module to enhance textural and boundary features in the frequency domain, and a KAN-based classification head to improve nonlinear decision boundaries.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Evaluated on the publicly available UAV-CM benchmark dataset comprising 7,666 images across 9 crop and 2 background categories, MFKNet achieves 93.67% accuracy, outperforming ResNet50 (87.34%), ViT-Small (89.42%), and SwinTiny (91.56%) while maintaining competitive efficiency with 31.33M parameters and 3.78G FLOPs. Extensive ablation studies demonstrate the complementary contributions of each component. Cross-dataset evaluation on the PlantDoc plant disease detection dataset demonstrates transfer learning capability with 95.09% macro-averaged accuracy. Furthermore, cross-dataset evaluation on the Martell Forest UAV tree species dataset confirms robust generalization to temperate forest vegetation with 89.45% macro-averaged accuracy, outperforming ResNet50 (83.15%) and SwinTiny (87.68%) on this distinct geographic domain and vegetation type.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Five-fold cross-validation on UAV-CM yields 93.41% \u00b1 0.31% mean accuracy, confirming result stability. Comprehensive sensitivity analyses of KAN hyperparameters and Fourier filter configurations provide practical guidance for deployment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpls.2026.1889120","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T05:28:15Z","timestamp":1787549295000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["MFK-Net: a computationally efficient Mamba-Fourier-KAN hybrid architecture for UAV-based crop classification"],"prefix":"10.3389","volume":"17","author":[{"given":"Yanze","family":"Mu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Jilin University","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/rs15071860","article-title":"Transformers in remote sensing: A survey","volume":"15","author":"Aleissaee","year":"2023","journal-title":"Remote Sens."},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2505.00630","article-title":"Vision mamba in remote sensing: A comprehensive survey of techniques, applications and outlook","author":"Bao","year":"2025"},{"key":"B3","unstructured":"UAV-CM dataset: UAV crop monitoring dataset for agricultural object recognition\n          \n          2024"},{"key":"B4","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2021"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e29802","article-title":"Investigating attention mechanisms for plant disease identification in challenging environments","volume":"10","author":"Duhan","year":"2024","journal-title":"Heliyon"},{"key":"B6","article-title":"Scientific machine learning with kolmogorov-arnold networks","author":"Faroughi","year":"2025","journal-title":"arXiv:2507.22959v1"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1711.05458","article-title":"A public image database for benchmark of plant seedling classification algorithms","author":"Giselsson","year":"2017"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/ARXIV.2312.00752","article-title":"Mamba: Linear-time sequence modeling with selective state spaces","author":"Gu","year":"2023","journal-title":"CoRR"},{"key":"B9","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"B10","first-page":"1314","article-title":"Searching for mobilenetv3","author":"Howard","year":"2019"},{"key":"B11","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2506.05972","article-title":"Domain adaptation in agricultural image analysis: A comprehensive review from shallow models to deep learning","author":"Hu","year":"2025","journal-title":"arXiv:2506.05972v1"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/rs16203836","article-title":"Tree species classification from uav canopy images with deep learning models","volume":"16","author":"Huang","year":"2024","journal-title":"Remote Sens."},{"key":"B14","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1090\/trans2\/028\/04","article-title":"On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition","volume":"114","author":"Kolmogorov","year":"1957","journal-title":"Doklady Akademii Nauk SSSR"},{"key":"B15","first-page":"4296","article-title":"Fnet: Mixing tokens with fourier transforms","author":"Lee-Thorp","year":"2022"},{"key":"B16","first-page":"9992","article-title":"Swin transformer: Hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"B17","first-page":"11966","article-title":"A convnet for the 2020s","author":"Liu","year":"2022"},{"key":"B18","doi-asserted-by":"crossref","DOI":"10.52202\/079017-3273","article-title":"Vmamba: Visual state space model","author":"Liu","year":"2024"},{"key":"B19","article-title":"KAN: kolmogorovarnold networks","author":"Liu","year":"2025"},{"key":"B20","first-page":"9630","article-title":"Agriclip: Adapting clip for agriculture and livestock via domain-specialized cross-model alignment","volume":"644","author":"Nawaz","year":"2024"},{"key":"B21","doi-asserted-by":"publisher","first-page":"11436","DOI":"10.1038\/s41598-026-42151-5","article-title":"Design and implementation of a deep learning framework for automated crop classification and health diagnosis in precision agriculture","volume":"16","author":"Pal","year":"2026","journal-title":"Sci. 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