[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2058":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":26,"view_count":32,"doi":33,"paper":34,"created_at":55},2058,"Machine Learning-Based Prediction of Fall Armyworm (Spodoptera frugiperda) Outbreaks in Maize Production Systems of Northern Nigeria Using Climate and Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.58578\u002Fkijst.v3i3.12026","Fall Armyworm (Spodoptera frugiperda) poses a substantial threat to maize production across sub-Saharan Africa, particularly in Nigeria, where climatic variability intensifies the risk of pest outbreaks. This study developed and evaluated machine learning models for predicting Fall Armyworm outbreaks in northern Nigeria using integrated climate and remote sensing variables. A retrospective modeling framework was applied to simulated but biologically constrained datasets covering a six-year period (2019–2024) and comprising temperature, rainfall, relative humidity, Normalized Difference Vegetation Index (NDVI), and vegetation condition indices. Three supervised learning algorithms—Random Forest, Support Vector Machine, and Gradient Boosting—were trained and validated using five-fold cross-validation. Gradient Boosting achieved the highest predictive accuracy at 92.8%, followed by Random Forest at 89.2% and Support Vector Machine at 84.6%. Temperature, relative humidity, and NDVI emerged as the most influential predictors of outbreak occurrence, while the integration of satellite-derived vegetation indices improved overall model performance. These findings demonstrate the potential of combining machine learning with remote sensing data to develop scalable and cost-effective early warning systems for agricultural pest management. However, because the models were developed using simulated data, their predictive validity requires confirmation using field-collected observational data. The proposed framework contributes to data-driven pest surveillance by providing a basis for anticipating outbreaks and supporting timely decision-making in maize production systems.","草地贪夜蛾（Spodoptera frugiperda）对撒哈拉以南非洲的玉米生产构成重大威胁，在尼日利亚尤为突出，因为气候变率加剧了害虫暴发的风险。本研究开发并评估了机器学习模型，利用气候与遥感综合变量预测尼日利亚北部草地贪夜蛾的暴发。研究采用回顾性建模框架，基于模拟但受生物学约束的数据集，覆盖六年时间（2019—2024年），包括温度、降雨量、相对湿度、归一化植被指数（NDVI）及植被状况指数。三种监督学习算法——随机森林、支持向量机和梯度提升——通过五折交叉验证进行训练与验证。梯度提升取得了最高的预测准确率，达92.8%，其次为随机森林（89.2%）和支持向量机（84.6%）。温度、相对湿度和NDVI是暴发发生最具影响力的预测因子，而卫星衍生植被指数的整合提升了模型的整体性能。这些发现表明，将机器学习与遥感数据相结合，有望开发可扩展且成本效益高的农业害虫管理预警系统。然而，由于模型基于模拟数据开发，其预测效度仍需利用实地观测数据加以验证。所提出的框架为数据驱动的害虫监测提供了依据，有助于预判暴发并支持玉米生产系统中的及时决策。",null,"Kwaghe International Journal of Sciences and Technology","2026-09-09T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,16,12,9,1,"将机器学习与气候及遥感数据结合用于尼日利亚北部玉米草地贪夜蛾暴发预测，方法框架有参考价值，但基于模拟数据、结论可靠性待田间验证，属细分领域研究进展。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","玉米","遥感","病虫害预警",0,"10.58578\u002Fkijst.v3i3.12026",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":9,"card":47,"direction":53,"ingested_from":54},"W7212022953",[37,39,41,43,45],{"name":38,"orcid":9},"Okwor Jude I.",{"name":40,"orcid":9},"Attamah Chinyere G.",{"name":42,"orcid":9},"Uchendu Christian N.",{"name":44,"orcid":9},"Onyima John O.",{"name":46,"orcid":9},"Idoko Bartholomew",{"tldr":48,"method":49,"finding":50,"direction":51,"opportunity":52},"用气候与遥感数据构建机器学习模型预测尼日利亚北部玉米草地贪夜蛾暴发。","随机森林、SVM、梯度提升，结合温度、降雨、湿度、NDVI等模拟数据，五折交叉验","梯度提升准确率最高达92.8%，温度、相对湿度和NDVI是最关键预测因子。","农业人工智能与决策模型","模型基于模拟数据，亟需用田间实测数据验证，并开发可扩展的实时早期预警系统。","农业遥感与作物表型","openalex","2026-09-10T23:30:27.820508Z"]