Proceedings
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| Filter results3 paper(s) found. |
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1. Inversion of Potato Chlorophyll Content Based on Radiation Transfer Model and Machine Learning AlgorithmLeaf chlorophyll content (LCC) significantly correlates with crop growth conditions, nitrogen content, yield, etc. It is a crucial indicator for elucidating the senescence process of plants and can reflect their growth and nutrition status. However, the performance of traditional LCC inversion models is limited by the quality and scale of training data. It is difficult to satisfy the needs of precision agriculture. 【Objective】Therefore, this study proposes a hybrid modeling framework based... Y. Ma, J. Zhang, D. Pan, Q. Wu, S. Xiaoyu, X. Xu |
2. Study on the Phenological Zoning Method for Winter Wheat in the Huang-Huai-Hai Region of ChinaThe impact of global climate change on agricultural phenology is becoming increasingly significant. As a major producer of winter wheat, China's cultivation areas span multiple climate zones. Against the backdrop of climate change, the spatiotemporal differentiation of crop phenology has raised new scientific demands for agricultural zoning. Phenological zoning has guiding significance for variety selection, irrigation management, and pest prediction. However, existing research often relies... S. Xiaoyu, Q. Wu, Y. Ma, J. Zhang, P. Dong, X. Xu |
3. Early Detection of Soybean Pest Infestations Using Leaf-Level Reflectance Spectroradiometry and Machine LearningThe agricultural sector plays a central role in sustaining global food production, energy supply, and economic development. However, population growth, climate change, resource scarcity, and increasing sustainability demands have intensified production challenges. Pest and disease outbreaks are major contributors to crop losses worldwide, underscoring the urgent need for reliable methods capable of enabling early detection and timely intervention. In this context, leaf-level spectroradiometry... M. Lima, J.C. Felipe, E.J. Ferreira, L.A. Jorge, L. Zhao |