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玉米产量预测研究综述
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Abstract:
玉米作为全球最重要的粮食作物之一，其产量预测在确保粮食安全、优化农业管理以及支持政策制定方面具有重要意义。本文系统综述了当前玉米产量预测领域的主要方法，包括传统的作物预测系统模型、数据驱动的机器学习模型以及近年来迅速发展的深度学习模型，深入分析了气候、土壤、遥感和社会经济等多种预测变量对产量预测精度的影响。通过对现有研究的成果进行总结，本文揭示了不同模型的优劣势，并指出了现阶段研究中存在的不足，如模型泛化能力有限、多模态数据融合的挑战等。最后，本文对未来研究提出了展望，建议进一步探索多源数据的整合、优化模型的适应性以及提升预测的解释性，以推动玉米产量预测技术向更精准、智能化方向发展。本文为未来的玉米产量预测研究提供了有价值的理论基础和实践指导。
Maize is one of the most important crops globally, and accurate yield prediction is crucial for food security, agricultural management, and policy decisions. This paper reviews key methods for maize yield prediction, including crop prediction models, machine learning, and deep learning. It analyzes the impact of variables like climate, soil, remote sensing, and socioeconomic factors on prediction accuracy. By summarizing current research, this paper highlights the strengths and limitations of these models and addresses challenges such as limited generalization and multimodal data integration. Finally, it proposes future directions for improving model adaptability and prediction precision, offering valuable insights for further research.
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