[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2951":3,"related-2951":57},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":56},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",[33,34],"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",0,"10.24925\u002Fturjaf.v14i9.2678-2694.8977",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":47,"card":48,"direction":54,"ingested_from":55},"W7213465585",[41,44],{"name":42,"orcid":43},"Mehmet ÖZCANLI","https:\u002F\u002Forcid.org\u002F0000-0003-2228-8298",{"name":45,"orcid":46},"Kerim Karadağ","https:\u002F\u002Forcid.org\u002F0000-0001-5167-4054","https:\u002F\u002Fwww.agrifoodscience.com\u002Findex.php\u002FTURJAF\u002Farticle\u002Fdownload\u002F8977\u002F4317",{"tldr":49,"method":50,"finding":51,"direction":52,"opportunity":53},"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":58,"page":22,"page_size":58,"items":59},6,[60,106,139,193,227,253],{"id":61,"title":62,"url":63,"summary":64,"summary_zh":65,"content":9,"source_name":66,"source_url":63,"published_at":67,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":68,"score_detail":69,"sources":74,"tags":76,"search_phrases":80,"slug":83,"view_count":36,"doi":84,"paper":85,"created_at":105},2654,"Design and Implementation of a Low-Cost GNSSBased Precision Agricultural Monitoring System with Real-Time IoT Visualization","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26aug1576","Precision agriculture provides an effective approach for improving agricultural productivity through spatially and temporally informed monitoring of soil, environmental, and crop conditions. However, many precision agriculture technologies remain relatively expensive and technically complex for small- and medium-scale farmers, particularly in developing countries. This paper presents a low-cost GNSS-based precision agricultural monitoring system integrating an ESP32, agricultural sensors, IoT communication, and real-time web visualization. The system acquires soil moisture, temperature, relative humidity, and GNSS data and transmits location-tagged measurements to a web server for remote monitoring.","精准农业通过基于空间和时间信息的土壤、环境及作物状况监测，为提高农业生产率提供了有效途径。然而，许多精准农业技术对中小规模农户而言仍然相对昂贵且技术复杂，尤其是在发展中国家。本文提出了一种基于GNSS的低成本精准农业监测系统，集成了ESP32、农业传感器、物联网通信和实时网页可视化。该系统采集土壤水分、温度、相对湿度和GNSS数据，并将带有位置标记的测量数据传输至网页服务器以实现远程监测。","International Journal of Innovative Science and Research Technology (IJISRT)","2026-09-14T00:00:00Z",52,{"impact":70,"substance":71,"depth":72,"authority":58,"freshness":70,"relevant":22,"comment":73},8,16,14,"面向中小农户的低成本GNSS+ESP32物联网监测方案，方法具体但属单篇应用型论文，影响范围有限，可作为智慧农业技术案例收录。",[75],{"name":66,"url":63},[77,78,30,31,79],"智慧农业","物联网","GNSS",[81,82],"土壤墒情 智慧农业 精准农业 物联网","土壤墒情 智慧农业","土壤墒情智慧农业精准农业物联网-2654","10.38124\u002Fijisrt\u002F26aug1576",{"doi":84,"openalex_id":86,"authors":87,"venue":66,"cited_by_count":36,"oa_url":98,"card":99,"direction":103,"ingested_from":55},"W7213355767",[88,90,92,94,96],{"name":89,"orcid":9},"D. Z. Zakut",{"name":91,"orcid":9},"O. B. Goodtalk",{"name":93,"orcid":9},"A. N. Lawal",{"name":95,"orcid":9},"S. A. Yusuf",{"name":97,"orcid":9},"I. Isa","https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26AUG1576.pdf",{"tldr":100,"method":101,"finding":102,"direction":103,"opportunity":104},"设计并实现了一套低成本GNSS精准农业监测系统，集成ESP32与物联网实时可视化。","采用ESP32、农业传感器、GNSS模块与IoT通信，采集土壤水分、温湿度等数据","系统能以低成本实现位置标记的农田环境实时远程监测，适合中小农户。","智慧农业 \u002F 农业物联网","可进一步研究低功耗长期部署、多源数据融合与面向小农户的智能预警决策模型。","2026-09-16T23:30:17.408536Z",{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":9,"source_name":112,"source_url":109,"published_at":67,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":113,"score_detail":114,"sources":118,"tags":120,"search_phrases":123,"slug":126,"view_count":36,"doi":127,"paper":128,"created_at":138},2518,"AI-Driven Transformation in Sustainable Agriculture: A Systematic Review","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fijare.a-6627","The agriculture sector is facing unprecedented pressure from a rapidly growing global population, erratic weather conditions and declining water availability. Traditional farming methods are increasingly proving insufficient in meeting the rising food demand, necessitating a shift towards technology-driven solutions. This paper provides a critical and systematic review of the application of Artificial Intelligence (AI) in agriculture, analysing its impact on productivity and sustainability, while identifying potential barriers hindering adoption. Employing a Systematic Literature Review approach, this study synthesizes recent research organized around the core domains of agricultural production - crop, soil and nutrient, water and irrigation and crop-protection (pest, disease and weed) management, alongside yield forecasting - examining how data-driven tools such as machine learning, the Internet of Things, robotics and computer vision are applied within each and prioritizing studies that reported agronomically meaningful outcomes such as yield gains and resource-use efficiency. The strongest gains identified across the review fall within three agricultural areas - crop and soil monitoring, yield forecasting and robotic field operations such as targeted detection, weeding and harvesting. The technologies enabling them, chiefly machine-learning models such as convolutional neural networks combined with field and remote sensors, have delivered high in-field precision, with reported object-detection precision of up to 95.78% in field conditions. In practical terms, this translates into more efficient use of water, fertilizer and other inputs and into improved yield and crop quality. The review concludes that prioritizing Explainable AI and interoperable farm systems is central to building farmer trust and safeguarding food security under a changing climate.","农业部门正面临着来自全球人口快速增长、天气条件不稳定以及水资源日益减少的前所未有的压力。传统耕作方法在满足不断增长的粮食需求方面日益显得力不从心，因此有必要转向技术驱动的解决方案。本文对人工智能（Artificial Intelligence, AI）在农业中的应用进行了批判性和系统性的综述，分析了其对生产力和可持续性的影响，同时识别了阻碍其采用的潜在障碍。本研究采用系统性文献综述方法，围绕农业生产的核心领域——作物、土壤与养分、水资源与灌溉、作物保护（病虫害和杂草）管理以及产量预测——对近期研究进行了综合梳理，考察了机器学习、物联网、机器人技术和计算机视觉等数据驱动工具在各领域中的应用方式，并优先关注报告了具有农学意义成果（如产量提升和资源利用效率）的研究。综述中发现的最显著收益集中在三个农业领域——作物与土壤监测、产量预测以及机器人田间作业（如靶向检测、除草和收获）。实现这些收益的技术，主要是卷积神经网络等机器学习模型与田间及遥感传感器的结合，已在田间条件下实现了高精度，所报告的目标检测精度在田间条件下高达95.78%。在实际应用中，这意味着水、肥料及其他投入品的更高效利用，以及产量和作物品质的提升。综述得出结论：优先发展可解释人工智能（Explainable AI）和可互操作的农场系统，对于在气候变化背景下建立农民信任和保障粮食安全至关重要。","Indian Journal of Agricultural Research",76,{"impact":19,"substance":115,"depth":116,"authority":20,"freshness":70,"relevant":22,"comment":117},20,17,"系统综述梳理AI在作物土壤监测、产量预测与机器人田间作业中的应用成效与推广障碍，结论扎实，对智慧农业方向有参考价值。",[119],{"name":112,"url":109},[77,121,28,122,30],"农业人工智能","可解释AI",[124,125],"农业人工智能 产量预测 智慧农业 精准农业","农业人工智能 产量预测","农业人工智能产量预测智慧农业精准农业-2518","10.18805\u002Fijare.a-6627",{"doi":127,"openalex_id":129,"authors":130,"venue":112,"cited_by_count":36,"oa_url":109,"card":133,"direction":103,"ingested_from":55},"W7212616413",[131],{"name":132,"orcid":9},"Manish Maan",{"tldr":134,"method":135,"finding":136,"direction":52,"opportunity":137},"系统综述AI在可持续农业中的应用，分析生产力与可持续性影响及推广障碍。","系统文献综述，围绕作物、土壤、水、植保和产量预测梳理ML、IoT、机器人、计算机","作物与土壤监测、产量预测和机器人田间作业收益最大，检测精度达95.78%，需可解释AI与互操作系统。","可解释AI与互操作农场系统在农户信任和气候适应中的作用尚缺实证，是值得深入的研究空白。","2026-09-15T23:30:13.705421Z",{"id":140,"title":141,"url":142,"summary":143,"summary_zh":144,"content":9,"source_name":145,"source_url":142,"published_at":67,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":113,"score_detail":146,"sources":149,"tags":151,"search_phrases":153,"slug":156,"view_count":36,"doi":157,"paper":158,"created_at":192},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",{"impact":71,"substance":147,"depth":116,"authority":20,"freshness":21,"relevant":22,"comment":148},21,"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。",[150],{"name":145,"url":142},[77,121,27,30,152],"遥感",[154,155],"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":157,"openalex_id":159,"authors":160,"venue":145,"cited_by_count":36,"oa_url":186,"card":187,"direction":103,"ingested_from":55},"W7212834743",[161,163,165,167,169,172,174,176,178,181,183],{"name":162,"orcid":9},"Amrutha Lakshmi Gubbala",{"name":164,"orcid":9},"Santosha Rathod",{"name":166,"orcid":9},"Ramesh Dasyam",{"name":168,"orcid":9},"Mahender Kumar Rapolu",{"name":170,"orcid":171},"Arun Kumar Dasari","https:\u002F\u002Forcid.org\u002F0000-0001-7398-8601",{"name":173,"orcid":9},"Pundarikakshudu Kurra",{"name":175,"orcid":9},"Hanuma Raviteja Madireddy",{"name":177,"orcid":9},"Prajwal R. Shashishekhar",{"name":179,"orcid":180},"Ravi V. Mural","https:\u002F\u002Forcid.org\u002F0000-0002-5489-9918",{"name":182,"orcid":9},"Anil Kumar",{"name":184,"orcid":185},"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":188,"method":189,"finding":190,"direction":54,"opportunity":191},"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。","2026-09-15T23:30:08.600535Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":9,"source_name":199,"source_url":196,"published_at":200,"category":12,"cover_url":9,"hotness":201,"is_selected":14,"score":202,"score_detail":203,"sources":206,"tags":210,"search_phrases":212,"slug":214,"view_count":36,"doi":215,"paper":216,"created_at":226},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,61,{"impact":71,"substance":19,"depth":204,"authority":17,"freshness":36,"relevant":22,"comment":205},15,"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。",[207,208],{"name":199,"url":196},{"name":199,"url":209},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22723505",[77,121,28,30,211],"数据增强",[213,125],"农业人工智能 产量预测 数据增强 智慧农业","农业人工智能产量预测数据增强智慧农业-2337","10.5281\u002Fzenodo.22723504",{"doi":215,"openalex_id":217,"authors":218,"venue":199,"cited_by_count":36,"oa_url":196,"card":221,"direction":52,"ingested_from":55},"W7212353714",[219],{"name":220,"orcid":9},"S. Kavitha",{"tldr":222,"method":223,"finding":224,"direction":52,"opportunity":225},"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。","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。","2026-09-13T23:30:43.223973Z",{"id":228,"title":229,"url":230,"summary":231,"summary_zh":9,"content":9,"source_name":232,"source_url":9,"published_at":233,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":234,"score_detail":235,"sources":237,"tags":239,"search_phrases":241,"slug":243,"view_count":36,"doi":9,"paper":244,"created_at":252},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":70,"relevant":22,"comment":236},"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。",[238],{"name":232,"url":230},[77,121,27,28,240],"模型部署",[242,125],"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智慧农业模型部署-2219",{"doi":9,"openalex_id":9,"authors":245,"venue":9,"cited_by_count":36,"oa_url":9,"card":246,"direction":52,"ingested_from":251},[],{"tldr":247,"method":248,"finding":249,"direction":52,"opportunity":250},"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",{"id":254,"title":255,"url":256,"summary":257,"summary_zh":9,"content":9,"source_name":258,"source_url":256,"published_at":259,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":260,"score_detail":261,"sources":264,"tags":266,"search_phrases":268,"slug":271,"view_count":36,"doi":272,"paper":273,"created_at":300},1210,"Assessing rice genotype dependency in remote sensing: challenges in nitrogen discrimination and yield forecasting","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10432-8","Abstract Background Rice ( Oryza sativa L.) is a staple crop that accounts for 8% of the global primary crop production. This sector faces an environmental challenge driven by climate change, rising water scarcity, and the need for more resilient and adaptive production systems. Although Remote Sensing (RS) offers solutions for optimizing inputs, most current models are calibrated for specific varieties, limiting their application across diverse cultivars. This study evaluates the transferability of RS models for monitoring nitrogen (N) fertilization status and predicting yield across a highly heterogeneous dataset of rice genotypes, locations and seasons. Materials and methods Six field trials were conducted across three locations in Spain (Valencia and Tarragona) during seasons 2022 and 2023. The study analysed over 170 cultivars, including commercial Japonica and Indica varieties, and a selection of 170 non-commercialized-under development varieties, both subjected to low (100 kg N\u002Fha) and high (200 kg N\u002Fha) fertilization regimes. Multispectral UAV imagery (MAIA S2) was normalized using Accumulated Growing Degree Days (GDD) to align phenological stages across sites. Random Forest (RF) classifiers were employed to analyse the capacity of RS to identify whether rice paddies are under- or over-fertilized. The transferability of N models between rice genotypes was also assessed. Furthermore, the previously established MS3 + yield regression model, originally developed for the JSendra variety, was evaluated against a multi-variety dataset. Results Random Forest classifiers effectively discriminated between nitrogen application rates across diverse genotypes, with several sites exceeding an 85% validation accuracy. A consistent trend emerges when analysing spectral importance: visible (VIS) bands take importance during the early season stages, whereas near-infrared (NIR) and red-edge (RE) reflectance provide critical diagnostic information throughout the entire crop cycle. Notably, 82% of the evaluated varieties demonstrated a high compatibility with the global model. Conversely, the yield model showed limited transferability between varieties. While it performed poorly on the global dataset, it successfully predicted yields for 48.8% of commercial varieties (residues within ± 1 tons per hectare), specifically those with genetic and structural similarities to the training variety. Conclusion The study concludes that N-status monitoring via RS classifiers is robust across varying rice genetics, whereas yield prediction models exhibit strong genotype dependency.","Precision Agriculture","2026-08-29T00:00:00Z",75,{"impact":19,"substance":18,"depth":19,"authority":72,"freshness":262,"relevant":22,"comment":263},3,"研究揭示遥感氮素监测跨基因型稳健，但产量预测依赖品种，对精准农业实践有重要参考。",[265],{"name":258,"url":256},[77,27,28,152,267],"氮素监测",[269,270],"产量预测 智慧农业 氮素监测 水稻","产量预测 智慧农业","产量预测智慧农业氮素监测水稻-1210","10.1007\u002Fs11119-026-10432-8",{"doi":272,"openalex_id":274,"authors":275,"venue":258,"cited_by_count":36,"oa_url":294,"card":295,"direction":103,"ingested_from":55},"W7204667424",[276,278,281,283,285,287,290,292],{"name":277,"orcid":9},"Fàtima Della-Bellver",{"name":279,"orcid":280},"B. Franch","https:\u002F\u002Forcid.org\u002F0000-0003-0593-7874",{"name":282,"orcid":9},"Javier Tarín-Mestre",{"name":284,"orcid":9},"César José Guerrero-Benavent",{"name":286,"orcid":9},"Concha Domingo",{"name":288,"orcid":289},"Mar Català Forner","https:\u002F\u002Forcid.org\u002F0000-0002-1026-7097",{"name":291,"orcid":9},"Karen Marti-Jerez",{"name":293,"orcid":9},"Luis Marqués","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs11119-026-10432-8.pdf",{"tldr":296,"method":297,"finding":298,"direction":54,"opportunity":299},"评估遥感模型在不同水稻基因型间的可转移性，用于氮肥监测和产量预测。","多站点田间试验，无人机多光谱影像，GDD归一化，随机森林分类器，产量回归模型。","氮监测模型跨基因型有效，但产量模型转移性有限，仅对相似品种有效。","可研究开发基于基因型特征的通用产量预测模型，或结合机器学习提升跨品种适应性。","2026-09-01T04:03:03.696050Z"]