[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2951":3,"related-2951":106},{"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,"primary_topic":32,"secondary_topics":37,"classification":38,"search_phrases":81,"slug":84,"view_count":22,"doi":85,"paper":86,"created_at":105},2951,"Comparative Analysis of Geographical Factors Affecting Paddy (Oryza sativa L.) Yields in Türkiye Using Random Forest and ANOVA: The Case of Kırıkkale, Balıkesir, Diyarbakır and Şanlıurfa","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i9.2678-2694.8977","Rice (Oryza sativa L.) is a staple food for nearly half of the global population and a strategic crop for Turkey, where inter-provincial yield disparities remain pronounced. This study aims to classify provincial rice yield levels in Turkey for the 2004–2024 period using TurkStat data and to quantify the relative contribution of 14 environmental, edaphic and agronomic parameters driving these differences. Preliminary analyses identified Kırıkkale (21-year mean 908.3 kg\u002Fda) as the high-yield province, Balıkesir (747.7 kg\u002Fda) as the medium-yield province, and Diyarbakır (448.1 kg\u002Fda) and Şanlıurfa (440.5 kg\u002Fda) as the low- and lowest-yield provinces, respectively. A 14-parameter dataset compiled from field measurements and published province-level studies was analysed using Principal Component Analysis (PCA), Random Forest (RF) classification and one-way Analysis of Variance (ANOVA).With a 70\u002F30 train\u002Ftest split, the RF model achieved 93.47% accuracy, 0.9764 ROC-AUC and a mean variance of 0.0145. Gini-based variable importance ranked soil moisture, organic matter, soil pH, rainfall and temperature as the most influential drivers of yield, and ANOVA confirmed statistically significant differences across yield classes for these variables (all p \u003C 0.001). Findings indicate that low yields in south-eastern Anatolia are largely driven by inadequate soil moisture management, low organic matter content, elevated soil pH and summer heat stress, whereas Kırıkkale’s high yields are associated with more balanced soil–water relations. Results provide evidence-based guidance for region-specific rice production policies and data-driven decision support in Türkiye.","水稻（Oryza sativa L.）是全球近半数人口的主粮，也是土耳其的战略性作物，但该国各省之间的产量差异依然显著。本研究旨在利用土耳其统计局（TurkStat）数据，对2004—2024年期间土耳其各省水稻产量水平进行分类，并量化14项环境、土壤和农艺参数对上述差异的相对贡献。初步分析确定，Kırıkkale省（21年均值908.3 kg\u002Fda）为高产区，Balıkesir省（747.7 kg\u002Fda）为中产区，Diyarbakır省（448.1 kg\u002Fda）和Şanlıurfa省（440.5 kg\u002Fda）分别为低产区和最低产区。基于田间实测数据和已发表的省级研究，构建了包含14项参数的数据集，并采用主成分分析（PCA）、随机森林（RF）分类和单因素方差分析（ANOVA）进行分析。在70\u002F30的训练\u002F测试集划分下，RF模型达到了93.47%的准确率、0.9764的ROC-AUC值以及0.0145的平均方差。基于基尼系数的变量重要性排序显示，土壤水分、有机质、土壤pH、降雨量和温度是影响产量最重要的驱动因素，ANOVA证实这些变量在不同产量类别间均存在统计学显著差异（均p \u003C 0.001）。研究结果表明，安纳托利亚东南部地区的低产主要归因于土壤水分管理不足、有机质含量低、土壤pH偏高以及夏季高温胁迫，而Kırıkkale省的高产则与更为均衡的土壤—水分关系有关。研究结果为土耳其制定区域特异性水稻生产政策和数据驱动的决策支持提供了循证依据。",null,"Turkish Journal of Agriculture - Food Science and Technology","2026-09-17T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,13,9,1,"基于21年省级数据与随机森林、ANOVA量化水稻产量驱动因子，方法规范、结论可靠，但属土耳其区域研究，对国内三农实践参考价值有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"水稻","产量预测","农业大数据","精准农业","土壤墒情",{"code":33,"slug":34,"name":27,"path":35,"parent_code":36},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,59,66,71,76],{"code":33,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},15,true,[48],"paddy",[27,34],[27],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":58},"H2",7,[],[56,57],"土壤","soil",[31],{"code":60,"score":61,"supported":46,"title":62,"abstract":63,"tags":65},"I2",3,[],[64],"decision support",[],{"code":67,"score":22,"supported":14,"title":68,"abstract":69,"tags":70},"I1",[],[],[30],{"code":72,"score":22,"supported":14,"title":73,"abstract":74,"tags":75},"I3",[],[],[28],{"code":77,"score":22,"supported":14,"title":78,"abstract":79,"tags":80},"I6",[],[],[29],[82,83],"土耳其 水稻 产量 随机森林","Kırıkkale Balıkesir 水稻 产量","土耳其水稻产量随机森林-2951","10.24925\u002Fturjaf.v14i9.2678-2694.8977",{"doi":85,"openalex_id":87,"authors":88,"venue":10,"cited_by_count":95,"oa_url":96,"card":97,"direction":103,"ingested_from":104},"W7213465585",[89,92],{"name":90,"orcid":91},"Mehmet ÖZCANLI","https:\u002F\u002Forcid.org\u002F0000-0003-2228-8298",{"name":93,"orcid":94},"Kerim Karadağ","https:\u002F\u002Forcid.org\u002F0000-0001-5167-4054",0,"https:\u002F\u002Fwww.agrifoodscience.com\u002Findex.php\u002FTURJAF\u002Farticle\u002Fdownload\u002F8977\u002F4317",{"tldr":98,"method":99,"finding":100,"direction":101,"opportunity":102},"用随机森林和方差分析比较土耳其四省水稻产量差异，识别关键地理驱动因子。","基于2004–2024年TurkStat数据，用PCA、随机森林分类和单因素AN","土壤水分、有机质、pH、降雨和温度是产量主因；东南部低产源于土壤水分不足、有机质低、pH高和夏季热胁","农业人工智能与决策模型","可引入时序遥感与土壤传感器数据，构建跨区域可迁移的产量预测与精准水肥管理模型。","农业遥感与作物表型","openalex","2026-09-19T23:30:34.632573Z",{"total":107,"page":22,"page_size":107,"items":108},6,[109,186,272,356,409,488],{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":9,"source_name":115,"source_url":112,"published_at":116,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":117,"score_detail":118,"sources":121,"tags":123,"primary_topic":127,"secondary_topics":128,"classification":134,"search_phrases":167,"slug":170,"view_count":95,"doi":171,"paper":172,"created_at":185},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering","2026-10-18T00:00:00Z",78,{"impact":19,"substance":18,"depth":19,"authority":17,"freshness":119,"relevant":22,"comment":120},8,"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[122],{"name":115,"url":112},[124,125,27,28,126],"智慧农业","农业人工智能","遥感",{"code":33,"slug":34,"name":27,"path":35,"parent_code":36},[129],{"code":72,"slug":130,"name":131,"path":132,"parent_code":133},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":135},{"candidates":136,"agricultural_evidence":46},[137,141,146,153,159,163],{"code":33,"score":45,"supported":46,"title":138,"abstract":139,"tags":140},[34],[27,34],[27],{"code":72,"score":119,"supported":46,"title":142,"abstract":143,"tags":145},[],[126,144],"remote sensing",[28,126],{"code":147,"score":107,"supported":46,"title":148,"abstract":149,"tags":152},"A8",[],[150,151],"粮食安全","food security",[],{"code":154,"score":61,"supported":46,"title":155,"abstract":156,"tags":158},"J1",[],[157],"农业规划",[],{"code":67,"score":22,"supported":14,"title":160,"abstract":161,"tags":162},[],[],[124],{"code":60,"score":22,"supported":14,"title":164,"abstract":165,"tags":166},[],[],[125],[168,169],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394","10.3390\u002Feng7100572",{"doi":171,"openalex_id":173,"authors":174,"venue":115,"cited_by_count":95,"oa_url":112,"card":180,"direction":103,"ingested_from":104},"W7222942064",[175,177],{"name":176,"orcid":9},"Nayana Venkatesh",{"name":178,"orcid":179},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":181,"method":182,"finding":183,"direction":103,"opportunity":184},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","2026-10-20T23:30:19.292634Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":9,"source_name":192,"source_url":189,"published_at":193,"category":12,"cover_url":9,"hotness":194,"is_selected":14,"score":195,"score_detail":196,"sources":200,"tags":204,"primary_topic":205,"secondary_topics":206,"classification":212,"search_phrases":256,"slug":259,"view_count":95,"doi":260,"paper":261,"created_at":271},4310,"Comprehensive remote sensing and machine learning framework for multi-regional rice yield forecasting under hydroclimatic variability in Sahelian Niger","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23149579","It is crucial to forecast rice yields effectively in order to improve agricultural planning and resilience to climate change in the Sahel, where production is greatly affected by hydroclimatic variability. This research has developed an integrated framework combining remote sensing analysis and machine learning to assess the variability in rice production in Niamey, Dosso, Tillaberi and Diffa, in Niger, over the period 2010–2025. They combined Landsat-derived NDVI and EVI with parameters such as evapotranspiration, precipitation, temperature, humidity, solar radiation, soil moisture, cultivated area, year and season. We processed and harmonised the datasets using Google Earth Engine and Python. We then evaluated four regression algorithms separately, Random Forest (RF), k-nearest neighbours (KNN), support vector regression (SVR) and XGBoost (XGB), for each region. The correlation analysis revealed modest positive links between rice yield and the NDVI (r = 0.27) and the EVI (r = 0.14), while a strong correlation was observed between the NDVI and the EVI (r = 0.80). The vegetation indices showed marked seasonal and interannual variability, generally exhibiting higher values during the wet season. Model performance varied considerably from one region to another","在萨赫勒地区，有效预测水稻产量对于改善农业规划和增强气候变化适应能力至关重要，因为该地区的生产在很大程度上受到水文气候变率的制约。本研究开发了一个集成框架，将遥感分析与机器学习相结合，以评估尼日尔尼亚美、多索、蒂拉贝里和迪法在2010—2025年期间水稻产量的变异性。研究将基于Landsat的归一化植被指数（NDVI）和增强型植被指数（EVI）与蒸散发、降水、温度、湿度、太阳辐射、土壤水分、耕地面积、年份和季节等参数相结合。利用Google Earth Engine和Python对数据集进行了处理和协调。随后，针对每个地区分别评估了四种回归算法：随机森林（RF）、k近邻（KNN）、支持向量回归（SVR）和XGBoost（XGB）。相关分析显示，水稻产量与NDVI（r = 0.27）和EVI（r = 0.14）之间存在弱正相关，而NDVI与EVI之间则观察到强相关（r = 0.80）。植被指数表现出明显的季节性和年际变异性，通常在湿季呈现较高值。模型性能在不同地区之间差异显著。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-05T00:00:00Z",25,72,{"impact":17,"substance":197,"depth":198,"authority":20,"freshness":21,"relevant":22,"comment":199},21,17,"基于Landsat植被指数与四种机器学习模型的多区域水稻产量预测研究，方法扎实、数据跨度长，但属区域性学术成果，对国内农业信息化有方法借鉴价值。",[201,202],{"name":192,"url":189},{"name":192,"url":203},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23149580",[124,125,27,28,126],{"code":33,"slug":34,"name":27,"path":35,"parent_code":36},[207,208],{"code":72,"slug":130,"name":131,"path":132,"parent_code":133},{"code":60,"slug":209,"name":210,"path":211,"parent_code":133},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":213},{"candidates":214,"agricultural_evidence":46},[215,220,224,230,234,241,248,252],{"code":72,"score":216,"supported":46,"title":217,"abstract":218,"tags":219},16,[144],[126,144],[28,126],{"code":33,"score":45,"supported":46,"title":221,"abstract":222,"tags":223},[34],[27,34],[27],{"code":60,"score":45,"supported":46,"title":225,"abstract":227,"tags":229},[226],"machine learning",[228,226],"机器学习",[125],{"code":52,"score":107,"supported":46,"title":231,"abstract":232,"tags":233},[],[56,57],[],{"code":235,"score":107,"supported":46,"title":236,"abstract":237,"tags":240},"H3",[],[238,239],"蒸散发","evapotranspiration",[],{"code":242,"score":107,"supported":46,"title":243,"abstract":244,"tags":247},"H7",[],[245,246],"气候变化","climate change",[],{"code":154,"score":61,"supported":46,"title":249,"abstract":250,"tags":251},[],[157],[],{"code":67,"score":22,"supported":14,"title":253,"abstract":254,"tags":255},[],[],[124],[257,258],"尼日尔 水稻 遥感 产量预测","Landsat NDVI 水稻 机器学习","尼日尔水稻遥感产量预测-4310","10.5281\u002Fzenodo.23149579",{"doi":260,"openalex_id":262,"authors":263,"venue":192,"cited_by_count":95,"oa_url":189,"card":266,"direction":103,"ingested_from":104},"W7220391503",[264],{"name":265,"orcid":9},"Battula Vijaya Saradhi Halarou Saidou Souley",{"tldr":267,"method":268,"finding":269,"direction":103,"opportunity":270},"结合遥感植被指数与机器学习，预测尼日尔萨赫勒四地区2010–2025年水稻产量。","Landsat NDVI\u002FEVI 加气象土壤变量，GEE与Python处理，比较","NDVI\u002FEVI与产量仅弱相关，模型精度区域差异大，植被指数呈明显季节与年际波动。","可引入时序深度学习与迁移学习，解决跨区域泛化差及植被指数解释力弱的问题。","2026-10-06T23:30:20.077977Z",{"id":273,"title":274,"url":275,"summary":276,"summary_zh":277,"content":9,"source_name":278,"source_url":275,"published_at":279,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":280,"sources":283,"tags":285,"primary_topic":288,"secondary_topics":289,"classification":296,"search_phrases":332,"slug":335,"view_count":95,"doi":336,"paper":337,"created_at":355},3168,"Spatial and Temporal Analysis of Rice Yield Using the DSSAT Model in the Kommamuru Canal Command Area, Guntur (Dist), Andhra Pradesh, India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjeai\u002F2026\u002Fv48i104524","Rice is the predominant crop cultivated in the Kommamuru Canal Command Area of Guntur district, Andhra Pradesh. Rice productivity is strongly influenced by irrigation water availability and climatic conditions. The present study aimed to analyse the spatial and temporal variability of rice productivity using the DSSAT-CERES Rice model integrated with Sentinel-2 remote sensing data during the kharif seasons of 2022, 2023 and 2024. Rice-cultivated areas were mapped using multi-temporal Sentinel-2 imagery, while weather, soil, crop management and cultivar data were used as inputs for DSSAT simulations. The model was calibrated and validated using field observations and yield data collected from representative locations within the command area. Simulated rice yields showed considerable variation among mandals and across years, ranging from 1,560.58 to 4,111.06 kg ha⁻¹. The highest productivity was observed in 2024 due to favourable rainfall distribution, adequate canal water supply and improved crop growth conditions, whereas relatively lower productivity was recorded in 2023 owing to moisture stress and irregular irrigation availability. Spatial yield maps generated through the integration of DSSAT outputs and GIS-based rice maps identified high- and low-productivity zones across the command area. The study demonstrated the usefulness of combining crop simulation modelling and remote sensing techniques for rice yield assessment, irrigation planning, yield forecasting and sustainable water resource management in canal command areas. The study confirmed that the DSSAT-CERES Rice model effectively simulated rice productivity in the Kommamuru Canal Command Area during 2022-2024. Overall, the study provides a reliable framework for sustainable rice production and efficient water management in canal command areas and can be applied for future agricultural planning and decision-making. The validation results showed low percentage deviation values ranging from 0.94% to 4.39%, indicating good agreement between simulated and actual farmer yields. Low percentage deviation values between simulated and actual yields indicated good model accuracy and reliability for yield prediction. The integration of DSSAT, GIS and remote sensing techniques was useful for sustainable rice production planning and efficient irrigation management.","水稻是安得拉邦贡土尔县科马穆鲁灌区种植的主要作物。水稻生产力受灌溉可用水量和气候条件的强烈影响。本研究旨在利用DSSAT-CERES水稻模型结合Sentinel-2遥感数据，分析2022年、2023年和2024年kharif季水稻生产力的时空变异性。利用多时相Sentinel-2影像绘制水稻种植区，同时将气象、土壤、作物管理和品种数据作为DSSAT模拟的输入。利用从灌区内代表性地点收集的田间观测数据和产量数据对模型进行校准和验证。模拟水稻产量在不同mandal和年份间表现出相当大的变异，范围为1,560.58至4,111.06 kg ha⁻¹。2024年由于有利的降雨分布、充足的渠水供应和改善的作物生长条件，生产力最高；而2023年由于水分胁迫和不规律的灌溉可用性，生产力相对较低。通过整合DSSAT输出和基于GIS的水稻分布图生成的空间产量图，识别出灌区内的高产区和低产区。研究表明，将作物模拟模型与遥感技术相结合，对于灌区水稻产量评估、灌溉规划、产量预测和可持续水资源管理具有实用价值。研究证实，DSSAT-CERES水稻模型有效模拟了2022-2024年科马穆鲁灌区的水稻生产力。总体而言，本研究为灌区可持续水稻生产和高效水资源管理提供了可靠的框架，可应用于未来的农业规划和决策。验证结果显示，百分比偏差值较低，范围为0.94%至4.39%，表明模拟产量与实际农民产量之间具有良好的一致性。模拟产量与实际产量之间较低的百分比偏差值表明模型在产量预测方面具有良好的准确性和可靠性。DSSAT、GIS和遥感技术的整合有助于可持续水稻生产规划和高效灌溉管理。","Journal of Experimental Agriculture International","2026-09-22T00:00:00Z",{"impact":17,"substance":281,"depth":198,"authority":20,"freshness":13,"relevant":22,"comment":282},20,"印度区域尺度的DSSAT与Sentinel-2融合估产研究，方法成熟、验证可靠，对遥感估产与灌溉管理有参考价值，但属区域性案例，公共影响有限。",[284],{"name":278,"url":275},[27,28,126,286,287],"作物模型","灌溉管理",{"code":33,"slug":34,"name":27,"path":35,"parent_code":36},[290,295],{"code":235,"slug":291,"name":292,"path":293,"parent_code":294},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"code":72,"slug":130,"name":131,"path":132,"parent_code":133},{"status":39,"locked":14,"version":40,"method":41,"evidence":297},{"candidates":298,"agricultural_evidence":46},[299,303,310,314,318,324,328],{"code":33,"score":45,"supported":46,"title":300,"abstract":301,"tags":302},[34],[27,34],[27],{"code":235,"score":13,"supported":46,"title":304,"abstract":305,"tags":309},[],[306,307,291,308],"灌溉","水资源","water resource",[287],{"code":72,"score":119,"supported":46,"title":311,"abstract":312,"tags":313},[],[126,144],[28,126],{"code":52,"score":107,"supported":46,"title":315,"abstract":316,"tags":317},[],[56,57],[],{"code":319,"score":61,"supported":46,"title":320,"abstract":321,"tags":323},"I4",[],[322],"GIS",[],{"code":154,"score":61,"supported":46,"title":325,"abstract":326,"tags":327},[],[157],[],{"code":60,"score":22,"supported":14,"title":329,"abstract":330,"tags":331},[],[],[286],[333,334],"DSSAT CERES Rice 水稻","Sentinel-2 水稻 遥感估产","DSSATCERESRice水稻-3168","10.9734\u002Fjeai\u002F2026\u002Fv48i104524",{"doi":336,"openalex_id":338,"authors":339,"venue":278,"cited_by_count":95,"oa_url":275,"card":350,"direction":103,"ingested_from":104},"W7213966772",[340,342,344,346,348],{"name":341,"orcid":9},"Rana Prathap",{"name":343,"orcid":9},"G. Ravi Babu",{"name":345,"orcid":9},"V. Muthayya Chowdary",{"name":347,"orcid":9},"K.Krupavathi",{"name":349,"orcid":9},"K. Chandrasekhar",{"tldr":351,"method":352,"finding":353,"direction":103,"opportunity":354},"用DSSAT-CERES水稻模型结合Sentinel-2遥感，分析印度Guntur灌区2022-20","DSSAT-CERES水稻模型、Sentinel-2多时相影像、GIS空间制图与","模拟产量1560-4111 kg\u002Fha，2024年最高、2023年因水分胁迫最低，验证偏差仅0.94","可引入机器学习同化遥感与作物模型，提升灌区尺度产量预报精度并支撑灌溉决策。","2026-09-22T23:30:22.711640Z",{"id":357,"title":358,"url":359,"summary":360,"summary_zh":9,"content":361,"source_name":362,"source_url":9,"published_at":363,"category":364,"cover_url":9,"hotness":13,"is_selected":14,"score":365,"score_detail":366,"sources":368,"tags":370,"primary_topic":372,"secondary_topics":373,"classification":374,"search_phrases":404,"slug":407,"view_count":22,"doi":9,"paper":9,"created_at":408},3101,"AI 接管稻田：四川眉山永丰村 300 亩 AI 试点水稻亩产 826.8-863.6 公斤","https:\u002F\u002Fai-damn.com\u002Fai-takes-over-the-rice-fields-863-6-kg-per-mu-in-sichuan-pilot-1789513373662","9-14 四川省眉山市东坡区太和镇永丰村千亩高标准农田 300 亩 AI 试点田通过专家组测产验收：\"华浙优 210\"（高产优质杂交稻）亩产 826.8 公斤、\"胜两优 222\"（超高产籼粳杂交稻）亩产 863.6 公斤、\"全优 169\"（超高产杂交籼稻）亩产 858.8 公斤。AI 系统通过无人机巡检采集数据，对种植、水肥调控和病虫害早期预警提供精准建议。四川农业大学水稻栽培专家马均教授表示，结合良种、良法与 AI 精准管理可有效释放水稻增产潜力，为大规模单产提升提供可复制技术路径；今年永丰村共有 240 余个新品种在产量\u002F株型\u002F米质上表现良好，智能精准播种技术与 AI 应用已初步见效。","## AI Takes Over the Rice Fields: 863.6 kg per Mu in Sichuan Pilot\n\nIn the rolling fields of Yongfeng Village, Tahe Town, Dongpo District, Meishan City, Sichuan Province, something unusual happened this harvest season. On September 14, as combines rolled through the thousand-mu high-standard farmland, 300 mu of it had been managed not by traditional farming wisdom alone, but by an **AI model** specifically designed for rice cultivation.\n\nGone are the days of \"judging fields by experience.\" Now, it's all about **making decisions based on data**.\n\n### From Experience to Data\n\nThe embankments were crowded with agricultural experts and curious farmers, all gathered to witness a field test. An expert group organized by the Sichuan Provincial Science and Technology Department was evaluating a project led by Sichuan Agricultural University: the \"Integrated Demonstration and Application of High-quality, High-yield, and Efficient Production Technologies for Rice-Vegetable (Medicinal) Crops in the Chengdu Plain.\"\n\nSo how does it work? The AI system collects data through **drone inspections**, then provides precise recommendations on planting, water and fertilizer regulation, and pest and disease early warning.\n\nLocal large-scale grain farmer Zhao Youyong put it simply: \"Before, farming relied on experience for field inspections. Now, using drones and the AI system, we get timely information about pests and diseases, so we can handle them directly. Farming has become more convenient.\"\n\n### The Numbers That Matter\n\nThe expert group's standardized yield test delivered solid results. All three core varieties in the 300-mu AI pilot fields performed impressively:\n\n*   **\"Huazheyous 210\"** (high-yield, high-quality hybrid rice): 826.8 kg per mu\n*   **\"Shengliangyou 222\"** (super-high-yield indica-japonica hybrid rice): **863.6 kg per mu**\n*   **\"Quanyou 169\"** (super-high-yield hybrid indica rice): 858.8 kg per mu\n\nMa Jun, a rice cultivation expert at Sichuan Agricultural University, explained that these yields prove that combining quality seeds with appropriate methods and AI precision management can **effectively release the potential for rice yield increase**. It offers a replicable technical path for large-scale yield improvement.\n\nHe also noted that more than 240 new varieties demonstrated good performance in yield, plant shape, and rice quality in Yongfeng Village this year. The application of intelligent precision sowing technology and AI has already shown initial results.\n\n### What This Means for the Future\n\nThis pilot isn't just about one good harvest. It's a glimpse into how **AI can transform traditional agriculture**. By moving from experience-based to data-driven farming, growers can make more informed decisions, reduce risks, and potentially achieve higher yields sustainably.\n\nAs Ma Jun pointed out, the combination of quality seeds, appropriate methods, and AI precision management provides a technical path that can be replicated on a larger scale. For a country that feeds 20% of the world's population with less than 10% of its arable land, such innovations are more than welcome—they're essential.\n\n### Key Points\n\n*   **AI-managed pilot field** in Sichuan achieved rice yields up to **863.6 kg per mu**.\n*   **Drones and data** replaced traditional experience-based farming for planting, fertilization, and pest control.\n*   **Three rice varieties** all exceeded 826 kg per mu, proving the effectiveness of AI precision management.\n*   **Experts say** this approach offers a replicable path for large-scale yield improvement.\n*   **The future of farming** is shifting from \"judging fields by experience\" to \"making decisions based on data.\"","AI DAMN","2026-09-14T10:00:00Z","报道",76,{"impact":18,"substance":18,"depth":198,"authority":21,"freshness":107,"relevant":22,"comment":367},"AI精准管理水稻试点实测亩产数据具体、多方信源，具备可复制的智慧农业示范价值，值得入选每日精选。",[369],{"name":362,"url":359},[124,125,27,30,371],"无人機巡田",{"code":33,"slug":34,"name":27,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":375},{"candidates":376,"agricultural_evidence":46},[377,384,389,395,400],{"code":33,"score":378,"supported":46,"title":379,"abstract":381,"tags":383},23,[27,380],"稻田",[27,382],"杂交稻",[27],{"code":147,"score":61,"supported":46,"title":385,"abstract":386,"tags":388},[],[387],"单产提升",[],{"code":390,"score":61,"supported":46,"title":391,"abstract":392,"tags":394},"H1",[],[393],"高标准农田",[],{"code":67,"score":396,"supported":14,"title":397,"abstract":398,"tags":399},2,[],[],[124,30],{"code":60,"score":22,"supported":14,"title":401,"abstract":402,"tags":403},[],[],[125],[405,406],"四川眉山 永丰村 AI水稻","四川农业大学 水稻 AI试点","四川眉山永丰村AI水稻-3101","2026-09-22T00:05:33.996455Z",{"id":410,"title":411,"url":412,"summary":413,"summary_zh":414,"content":9,"source_name":415,"source_url":412,"published_at":416,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":365,"score_detail":417,"sources":419,"tags":421,"primary_topic":422,"secondary_topics":423,"classification":425,"search_phrases":447,"slug":450,"view_count":22,"doi":451,"paper":452,"created_at":487},2512,"Deep learning-based classification of wet direct seeded rice, broadcasted direct seeded rice and transplanted rice using drone imagery for precision agriculture","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1698781","Accurate estimation of crop area using classification techniques applied to drone imagery plays an important role in precision agriculture. Traditional machine learning (ML) approaches have been widely used for agricultural image classification; however, advanced deep learning (DL) models generally provide superior feature extraction and classification capability for complex crop patterns. Differentiating various rice establishment methods is essential for precise area estimation. In this study, advanced deep learning models were employed to classify three types of rice cultivation: (i) Broadcasted Direct Seeded Rice (DSR), (ii) Wet Direct Seeded Rice (Wet DSR), and (iii) Transplanted Rice (TR) using drone imagery. The drone imagery was collected from an experimental field at Praanadhaara Organised Agro Forestry Private Limited, Bapatla District, Andhra Pradesh, India. The images were captured in the visible spectrum (Red, Green, and Blue bands) on three dates, viz., 15 October 2023, 27 October 2023, and 01 December 2023, from an altitude of 40 m and were used to train and test classification models. Classification was performed using six ResNet-50 based hybrid models, namely, ResNet-50+K-Nearest Neighbor (ResNet-50+KNN), ResNet-50+Support Vector Machine (ResNet-50+SVM), ResNet-50+Decision Trees (ResNet-50+DT), ResNet-50+Random Forest (ResNet-50+RF), ResNet-50+Naïve Bayes (ResNet-50+NB), and ResNet-50+Neural Network (ResNet-50+NN), along with two additional DL architectures, namely, You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8). Model performance was evaluated using overall accuracy (OA), precision (P), recall (R), kappa coefficient (K), F1-score (F1), and mean Average Precision (mAP). Among the tested classifiers, the ResNet-50+NN model consistently achieved the highest average overall accuracies of 93.16%, 95.81%, and 93.31% at T 1 , T 2 , and T 3 , respectively, outperforming all other models, whose accuracies ranged from 78.05% to 92.07%, 86.11%–95.24%, and 77.47%–92.09% across the respective time intervals. The ResNet-50+NN model also recorded the highest precision (0.93–0.97), recall (0.92–0.97), F1-score (0.92–0.97), and kappa coefficient (0.89–0.96), demonstrating superior and consistent classification performance across all observation dates. The methodology developed in this work enables precise identification of rice establishment methods, improving crop mapping and monitoring. This identification enhances resource efficiency, optimizes input use, and supports site-specific management, contributing to sustainable precision agriculture.","利用分类技术对无人机影像进行作物面积精确估算，在精准农业中发挥着重要作用。传统机器学习（ML）方法已广泛用于农业图像分类；然而，先进的深度学习（DL）模型通常对复杂作物模式具有更优越的特征提取和分类能力。区分不同的水稻种植方式对于精确估算面积至关重要。本研究采用先进的深度学习模型，利用无人机影像对三种水稻种植类型进行分类：（i）撒播直播稻（DSR），（ii）湿润直播稻（Wet DSR），以及（iii）移栽稻（TR）。无人机影像采集自印度安得拉邦巴帕特拉县Praanadhaara Organised Agro Forestry Private Limited的试验田。图像在可见光谱（红、绿、蓝波段）下于三个日期拍摄，即2023年10月15日、2023年10月27日和2023年12月1日，飞行高度为40 m，用于训练和测试分类模型。分类采用六种基于ResNet-50的混合模型，即ResNet-50+K近邻（ResNet-50+KNN）、ResNet-50+支持向量机（ResNet-50+SVM）、ResNet-50+决策树（ResNet-50+DT）、ResNet-50+随机森林（ResNet-50+RF）、ResNet-50+朴素贝叶斯（ResNet-50+NB）和ResNet-50+神经网络（ResNet-50+NN），以及两种额外的深度学习架构，即You Only Look Once第5版（YOLOv5）和You Only Look Once第8版（YOLOv8）。采用总体精度（OA）、精确率（P）、召回率（R）、Kappa系数（K）、F1分数（F1）和平均精度均值（mAP）评估模型性能。在测试的分类器中，ResNet-50+NN模型在T₁、T₂和T₃分别持续取得最高的平均总体精度，为93.16%、95.81%和93.31%，优于所有其他模型，后者的精度在相应时间段分别为78.05%–92.07%、86.11%–95.24%和77.47%–92.09%。ResNet-50+NN模型还记录了最高的精确率（0.93–0.97）、召回率（0.92–0.97）、F1分数（0.92–0.97）和Kappa系数（0.89–0.96），在所有观测日期均表现出优越且稳定的分类性能。本研究开发的方法能够精确识别水稻种植方式，改进作物制图和监测。这种识别增强了资源","Frontiers in Remote Sensing","2026-09-14T00:00:00Z",{"impact":216,"substance":197,"depth":198,"authority":20,"freshness":21,"relevant":22,"comment":418},"基于无人机可见光影像与ResNet-50混合模型区分三类水稻种植方式，方法对比系统、精度数据扎实，对精准农业作物制图有参考价值，但属细分技术进展，影响范围有限。",[420],{"name":415,"url":412},[124,125,27,30,126],{"code":33,"slug":34,"name":27,"path":35,"parent_code":36},[424],{"code":60,"slug":209,"name":210,"path":211,"parent_code":133},{"status":39,"locked":14,"version":40,"method":41,"evidence":426},{"candidates":427,"agricultural_evidence":46},[428,434,439,443],{"code":60,"score":19,"supported":46,"title":429,"abstract":431,"tags":433},[430],"deep learning",[432,228,226,430],"深度学习",[125],{"code":67,"score":216,"supported":46,"title":435,"abstract":437,"tags":438},[436],"precision agriculture",[30,436],[124,30],{"code":33,"score":45,"supported":46,"title":440,"abstract":441,"tags":442},[34],[27,34],[27],{"code":72,"score":22,"supported":14,"title":444,"abstract":445,"tags":446},[],[],[126],[448,449],"农业人工智能 智慧农业 精准农业 水稻","农业人工智能 智慧农业","农业人工智能智慧农业精准农业水稻-2512","10.3389\u002Ffrsen.2026.1698781",{"doi":451,"openalex_id":453,"authors":454,"venue":415,"cited_by_count":95,"oa_url":480,"card":481,"direction":486,"ingested_from":104},"W7212834743",[455,457,459,461,463,466,468,470,472,475,477],{"name":456,"orcid":9},"Amrutha Lakshmi Gubbala",{"name":458,"orcid":9},"Santosha Rathod",{"name":460,"orcid":9},"Ramesh Dasyam",{"name":462,"orcid":9},"Mahender Kumar Rapolu",{"name":464,"orcid":465},"Arun Kumar Dasari","https:\u002F\u002Forcid.org\u002F0000-0001-7398-8601",{"name":467,"orcid":9},"Pundarikakshudu Kurra",{"name":469,"orcid":9},"Hanuma Raviteja Madireddy",{"name":471,"orcid":9},"Prajwal R. Shashishekhar",{"name":473,"orcid":474},"Ravi V. Mural","https:\u002F\u002Forcid.org\u002F0000-0002-5489-9918",{"name":476,"orcid":9},"Anil Kumar",{"name":478,"orcid":479},"R. M. Sundaram","https:\u002F\u002Forcid.org\u002F0000-0002-9857-8251","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fremote-sensing\u002Farticles\u002F10.3389\u002Ffrsen.2026.1698781\u002Fpdf",{"tldr":482,"method":483,"finding":484,"direction":103,"opportunity":485},"用无人机可见光影像和深度学习模型区分直播稻、湿直播稻与移栽稻三种水稻种植方式。","无人机RGB影像三个时相，比较ResNet-50混合模型与YOLOv5\u002Fv8分类","ResNet-50+NN表现最优，三期总体精度达93.16%、95.81%、93.31%，优于其他模","可扩展到多光谱\u002F多时相与更大区域验证，并用于种植方式制图与面积精准估算。","智慧农业 \u002F 农业物联网","2026-09-15T23:30:08.600535Z",{"id":489,"title":490,"url":491,"summary":492,"summary_zh":9,"content":9,"source_name":493,"source_url":9,"published_at":494,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":495,"score_detail":496,"sources":498,"tags":500,"primary_topic":502,"secondary_topics":503,"classification":504,"search_phrases":524,"slug":527,"view_count":61,"doi":9,"paper":528,"created_at":536},2219,"《面向部署的中国南方水稻产量预测阶段特异性信息评估:跨年份、地点及地点-年份环境》","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260909000242394.htm","研究人员利用中国南方10个地点、7年、67个地点-年份环境下168个品种的3204条产量记录,开发场景感知的机器学习框架。研究在随机五折交叉验证、留一年验证、前向年份预测、留一地点验证和留一地点-年份验证中比较五组建模特征集。随机五折交叉验证产生最高表观精度,最优田间调查增强模型达到R²=0.844,RMSE为340.59 kg\u002Fha;面向部署的性能较低,在前向年份预测中宏平均R²为0.359,留一地点验证为0.290,留一地点-年份验证为0.131。","Frontiers in Plant Science \u002F 生物通 2026年9月9日","2026-09-09T00:00:00Z",79,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":119,"relevant":22,"comment":497},"基于中国南方10地7年3204条产量记录的场景感知机器学习框架，系统揭示交叉验证精度与真实部署性能的巨大落差，对农业AI落地评估有实质参考价值。",[499],{"name":493,"url":491},[124,125,27,28,501],"模型部署",{"code":33,"slug":34,"name":27,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":505},{"candidates":506,"agricultural_evidence":46},[507,511,516,520],{"code":33,"score":21,"supported":46,"title":508,"abstract":509,"tags":510},[27],[],[27],{"code":60,"score":512,"supported":46,"title":513,"abstract":514,"tags":515},4,[],[228],[125],{"code":67,"score":22,"supported":14,"title":517,"abstract":518,"tags":519},[],[],[124],{"code":72,"score":22,"supported":14,"title":521,"abstract":522,"tags":523},[],[],[28],[525,526],"农业人工智能 产量预测 智慧农业 模型部署","农业人工智能 产量预测","农业人工智能产量预测智慧农业模型部署-2219",{"doi":9,"openalex_id":9,"authors":529,"venue":9,"cited_by_count":95,"oa_url":9,"card":530,"direction":101,"ingested_from":535},[],{"tldr":531,"method":532,"finding":533,"direction":101,"opportunity":534},"基于中国南方多环境水稻数据，评估机器学习产量预测在不同部署场景下的真实精度。","用10地点7年3204条记录，比较五组特征集在多种交叉验证下的表现。","随机交叉验证R²达0.844，但前向年份、留一地点等部署场景R²仅0.13-0.36。","需开发跨年份地点鲁棒建模与迁移学习，缩小随机验证与真实部署间的精度差距。","agent","2026-09-12T00:06:40.813584Z"]