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Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms. AgriEngineering, 6(1), 786\u2013802, https:\/\/doi.org\/10.3390\/agriengineering6010045","DOI":"10.3390\/agriengineering6010045"},{"key":"ref9","doi-asserted-by":"publisher","unstructured":"Qi, H., Wang, L., Zhu, H., Gani, A., & Gong, C. (2023). The barren plateaus of quantum neural networks: review, taxonomy and trends. Quantum Information Processing, 22(12), 435. https:\/\/doi.org\/10.1007\/s11128-023-04188-7","DOI":"10.1007\/s11128-023-04188-7"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Farmonov, N., Esmaeili, M., Abbasi-Moghadam, D., Sharifi, A., Amankulova, K., & Mucsi, L. (2024). HypsLiDNet: 3D-2D CNN model and spatial-spectral morphological attention for crop classification with DESIS and LiDAR data. 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In Proceedings of the 7th Asian-Australasian Conference on Precision Agriculture (pp. 16\u201318). Hamilton, New Zealand. Zenodo. https:\/\/doi.org\/10.5281\/zenodo.893668","DOI":"10.5281\/zenodo.893668"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"M. Hosseini, I. Becker-Reshef, R. Sahajpal, L. Fontana, P. Lafluf, G. Leale, and C. Justice, \"Crop Yield Prediction Using Integration of Polarimetric Synthetic Aperture Radar and Optical Data,\" in Proceedings of the 2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), 2020, pp. 17\u201320. DOI: 10.1109\/InGARSS48198.2020.9358978","DOI":"10.1109\/InGARSS48198.2020.9358978"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Wang, S., Hu, T., Huang, X., Li, Y., Zhang, C., Ning, H., Zhu, R., Li, Z., & Ye, X. (2024). GPT, large language models (LLMs) and generative artificial intelligence (GAI) models in geospatial science: a systematic review. International Journal of Digital Earth, 17(1), Article 2353122.LinkedIn+2University of Bristol+2University of Bristol+2, DOI: 10.1080\/17538947.2024.2353122","DOI":"10.1080\/17538947.2024.2353122"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Sapkota, R., Qureshi, R., Hassan, S. Z., Shutske, J., Shoman, M., & Sajjad, M. (2024). Multi-Modal LLMs in Agriculture: A Comprehensive Review. TechRxiv Preprint. DOI: 10.36227\/techrxiv.172651082.24507804\/v1","DOI":"10.36227\/techrxiv.172651082.24507804\/v1"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Mansourian, A., & Oucheikh, R. (2024). ChatGeoAI: Enabling Geospatial Analysis for Public through Natural Language, with Large Language Models. ISPRS International Journal of Geo-Information, 13(10), 348.Lund University, DOI: 10.3390\/ijgi13100348","DOI":"10.3390\/ijgi13100348"},{"key":"ref24","unstructured":"L. Yuan, F. Mo, K. Huang, W. Wang, W. Zhai, X. Zhu, Y. Li, J. Xu, and J.-Y. Nie, \"OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence,\" arXiv preprint https:\/\/arxiv.org\/abs\/2503.16326, Mar. 2025. https:\/\/arxiv.org\/html\/2503.16326v1"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"L. Wang, J. Chou, A. Tien, X. Zhou, and D. M. Baumgartner, \"AviationGPT: A Large Language Model for the Aviation Domain,\" in Proceedings of the AIAA AVIATION Forum and ASCEND 2024, 2024, p. 4250. DOI: 10.2514\/6.2024-4250","DOI":"10.2514\/6.2024-4250"},{"key":"ref26","unstructured":"Ali. S. H, Shahid. M. F, Tanveer. M. H, Rauf. A, \u201cIntegrating LLM for Cotton SoilAnalysis in Smart Agriculture System\u201d, IJIST, Special Issue pp 283-294, (2024) \n(PDF) Integrating LLM for Cotton Soil Analysis in Smart Agriculture System. Available from: https:\/\/www.researchgate.net\/publication\/389436000_Integrating_LLM_for_Cotton_Soil_Analysis_in_Smart_Agriculture_System [accessed May 23 2025]"},{"key":"ref27","doi-asserted-by":"publisher","unstructured":"Peng, B., Li, C., He, P., Galley, M., & Gao, J. (2023). Instruction tuning with GPT-4. arXiv preprint https:\/\/arxiv.org\/abs\/2304.03277. https:\/\/doi.org\/10.48550\/arXiv.2304.03277","DOI":"10.48550\/arXiv.2304.03277"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Q. Huang, Y. Tao, S. Ding, Y. Liu, and F. Marinello, \u201cAutomatic Construction of Knowledge Graph of Tea Diseases and Pests,\u201d Communication Papers of the 18th Conf. Computer Science and Intelligence Systems, ACSIS, vol. 37, pp. 141\u2013146, 2023, DOI: 10.15439\/2023F6100","DOI":"10.15439\/2023F6100"},{"key":"ref29","doi-asserted-by":"publisher","unstructured":"Auer, C., Lysak, M., Nassar, A., Dolfi, M., Livathinos, N., Vagenas, P., Berrospi Ramis, C., Omenetti, M., Lindlbauer, F., Dinkla, K., Mishra, L., Kim, Y., Gupta, S., Teixeira de Lima, R., Weber, V., Morin, L., Meijer, I., Kuropiatnyk, V., & Staar, P. W. J. (2024). Docling Technical Report (Version 1.0.0). arXiv preprint https:\/\/arxiv.org\/abs\/2408.09869. https:\/\/doi.org\/10.48550\/arXiv.2408.09869","DOI":"10.48550\/arXiv.2408.09869"},{"key":"ref30","doi-asserted-by":"publisher","unstructured":"Li, Z., Zhang, X., Zhang, Y., Long, D., Xie, P., & Zhang, M. (2023). 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