[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2337":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":28,"view_count":21,"doi":34,"paper":35,"created_at":47},2337,"High-Precision Crop Yield Prediction Model Combining GANs and Random Forest","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22723504","Abstract Crop yield prediction algorithms are now much more accurate and useful thanks to recent developments in deep learning. In order to analyse crop yield, this study explores the combination of Random Forest methods with Generative Adversarial Networks (GANs). In order to overcome data scarcity and class imbalance, GANs are used for data augmentation, producing realistic synthetic samples that strengthen deep learning models. Various agro-climatic and soil datasets are used to forecast yield using Random Forest, an ensemble machine learning technique. According to comparative analyses, Random Forest outperforms conventional regression models and has great generalization across areas and crops4568, regularly achieving high predictive accuracy (R2 > 0.95). A potent foundation for precision agriculture is provided by the complementary application of Random Forest for prediction and GANs for data enrichment, allowing better precision in crop yield analysis. With proper hyperparameter tuning, RF can achieve very high accuracy (R² up to 0.99 in some studies), making it a preferred choice for practical yield forecasting.","摘要 得益于深度学习的最新发展，作物产量预测算法如今已更加准确和实用。为了分析作物产量，本研究探索了随机森林方法与生成对抗网络（Generative Adversarial Networks，GANs）的结合。为了克服数据稀缺和类别不平衡问题，研究使用GANs进行数据增强，生成逼真的合成样本以强化深度学习模型。研究采用多种农业气候和土壤数据集，利用随机森林这一集成机器学习技术来预测产量。比较分析表明，随机森林优于传统回归模型，并在不同地区和作物间展现出强大的泛化能力，通常能够实现较高的预测精度（R2 > 0.95）。随机森林用于预测与GANs用于数据增强的互补应用，为精准农业提供了有力的基础，使作物产量分析能够实现更高的精度。通过适当的超参数调优，随机森林可以达到非常高的精度（在某些研究中R²高达0.99），使其成为实际产量预测的首选方法。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z","论文",25,false,61,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,18,15,12,0,1,"将GAN数据增强与随机森林结合用于作物产量预测，方法组合有新意且精度结论明确，但属预印本平台论文、发布日期异常且时效性差，暂不宜进入每日精选。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22723505",[29,30,31,32,33],"智慧农业","农业人工智能","产量预测","精准农业","数据增强","10.5281\u002Fzenodo.22723504",{"doi":34,"openalex_id":36,"authors":37,"venue":10,"cited_by_count":21,"oa_url":6,"card":40,"direction":44,"ingested_from":46},"W7212353714",[38],{"name":39,"orcid":9},"S. Kavitha",{"tldr":41,"method":42,"finding":43,"direction":44,"opportunity":45},"结合GAN数据增强与随机森林，构建高精度作物产量预测模型。","GAN生成合成样本缓解数据稀缺，随机森林基于农业气候与土壤数据预测。","随机森林预测精度高（R²>0.95，部分达0.99），优于传统回归模型。","农业人工智能与决策模型","可探索GAN生成样本的农学合理性验证及跨区域迁移学习以提升泛化能力。","openalex","2026-09-13T23:30:43.223973Z"]