[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3167":3,"related-3167":63},{"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":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":35,"paper":36,"created_at":62},3167,"Temporal trend analysis and multi-temporal satellite feature integration for mango orchard acreage estimation using machine learning algorithms approach","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-65828-3","Accurate estimation of horticultural crop acreage is essential for agricultural planning, market forecasting and evidence-based policy formulation. The present study investigated long-term trends in mango cultivation and developed a multi-temporal remote sensing framework for mango orchard acreage estimation in Navsari District using integrated optical, SAR and machine learning approaches. Time-series (temporal) data spanning 23 years (2001–02 to 2023–24) were analyzed using polynomial regression models to evaluate trends in mango area and production. Linear regression best represented area expansion trends (Adj. R² = 0.969), whereas cubic regression better captured production variability (Adj. R² = 0.634), indicating climatic and seasonal influences on productivity. For orchard classification and acreage estimation, multi-temporal Sentinel-2 imagery acquired from November 2023 to March 2024 was processed within a phenology-guided framework. Monthly composites were generated and integrated with Sentinel-1 SAR backscatter data, vegetation indices (NDVI, GNDVI, NDRE, SAVI, EVI and NDMI) and texture metrics derived from Gray Level Co-occurrence Matrix (GLCM) analysis. A comprehensive 72-band feature stack was developed for classification. Four machine learning algorithms, namely Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM) and Multinomial Logistic Regression (MNLR) were evaluated for orchard discrimination. Among the tested models, RF achieved the highest classification performance with an Overall Accuracy of 99.80% and a Kappa coefficient of 0.997, followed by SVM (99.30%), MNLR (98.21%) and XGB (9.20%). The RF model estimated mango orchard area at 36,099.94 ha, showing the closest agreement with official horticultural statistics (34,363 ha) with only 5.05% estimation error. In contrast, SVM and MNLR overestimated orchard extent by 16.03% and 43.37%, respectively. The proposed framework provides a reliable and scalable methodology for operational horticultural monitoring, crop inventory generation and precision agricultural planning in tropical orchard ecosystems.","准确估算园艺作物种植面积对于农业规划、市场预测和循证政策制定至关重要。本研究探讨了芒果种植的长期趋势，并开发了一个多时相遥感框架，结合光学、合成孔径雷达（SAR）和机器学习方法，用于纳夫萨里县芒果园种植面积估算。利用多项式回归模型分析了跨越23年（2001—02年至2023—24年）的时间序列数据，以评估芒果面积和产量的变化趋势。线性回归最能表征面积扩张趋势（调整R² = 0.969），而三次回归更能捕捉产量变异性（调整R² = 0.634），表明气候和季节性因素对生产力具有影响。在果园分类和面积估算方面，基于物候指导框架处理了2023年11月至2024年3月获取的多时相Sentinel-2影像。生成了月度合成影像，并将其与Sentinel-1 SAR后向散射数据、植被指数（NDVI、GNDVI、NDRE、SAVI、EVI和NDMI）以及基于灰度共生矩阵（GLCM）分析提取的纹理指标进行整合。构建了一个包含72个波段的综合特征集用于分类。评估了四种机器学习算法，即随机森林（RF）、XGBoost（XGB）、支持向量机（SVM）和多项逻辑回归（MNLR），用于果园判别。在测试的模型中，RF取得了最高的分类性能，总体精度为99.80%，Kappa系数为0.997，其次是SVM（99.30%）、MNLR（98.21%）和XGB（9.20%）。RF模型估算的芒果园面积为36,099.94公顷，与官方园艺统计数据（34,363公顷）最为接近，估算误差仅为5.05%。相比之下，SVM和MNLR分别高估了果园面积16.03%和43.37%。所提出的框架为热带果园生态系统中的业务化园艺监测、作物清单生成和精准农业规划提供了一种可靠且可扩展的方法。",null,"Scientific Reports","2026-09-22T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":13,"relevant":20,"comment":21},14,22,18,1,"方法扎实、数据规模大且精度高，但属区域性作物遥感估产研究，产业影响有限，可作为技术方法类精选。",[23],{"name":10,"url":6},[25,26,27,28,29],"智慧农业","机器学习","芒果","遥感监测","作物估产",[31,32],"Navsari 芒果 遥感估产","Sentinel-2 芒果 果园面积","Navsari芒果遥感估产-3167",0,"10.1038\u002Fs41598-026-65828-3",{"doi":35,"openalex_id":37,"authors":38,"venue":10,"cited_by_count":34,"oa_url":6,"card":55,"direction":59,"ingested_from":61},"W7213920056",[39,41,44,46,48,51,53],{"name":40,"orcid":9},"V. Raju",{"name":42,"orcid":43},"Yogesh A. Garde","https:\u002F\u002Forcid.org\u002F0000-0002-0297-316X",{"name":45,"orcid":9},"Dr. V. S. Thorat",{"name":47,"orcid":9},"V. T. Shinde",{"name":49,"orcid":50},"Nitin Varshney","https:\u002F\u002Forcid.org\u002F0000-0001-9144-5475",{"name":52,"orcid":9},"Alok Shrivastava",{"name":54,"orcid":9},"A. P. Chaudhary",{"tldr":56,"method":57,"finding":58,"direction":59,"opportunity":60},"融合多时相Sentinel-1\u002F2与机器学习，估算印度芒果园面积并分析23年种植趋势。","23年时序回归分析；Sentinel-2月合成+SAR+植被指数+GLCM纹理共","RF精度最高（总体精度99.80%，Kappa 0.997），面积估算误差仅5.05%，优于SVM和","农业遥感与作物表型","可迁移该多时相SAR-光学特征框架至其他热带果园，并探索深度学习与物候自适应特征优化。","openalex","2026-09-22T23:30:22.619289Z",{"total":64,"page":20,"page_size":64,"items":65},6,[66,95,150,174,207,251],{"id":67,"title":68,"url":69,"summary":70,"summary_zh":9,"content":9,"source_name":71,"source_url":9,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":72,"score_detail":73,"sources":76,"tags":78,"search_phrases":81,"slug":84,"view_count":34,"doi":9,"paper":85,"created_at":94},3248,"Crop recommendation in precision agriculture: a systematic literature review of methods, trends, and challenges（精准农业中的作物推荐：方法、趋势与挑战系统综述）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37471110","MDPI 发表精准农业作物推荐方法系统综述：从183项研究中严格筛选129篇发表于2020-2026年的文章，使用PRISMA协议进行综合分析。研究表明集成学习方法（特别是随机森林和XGBoost）在各种农业数据集的预测性能上具有强大能力；支持向量机、决策树、k近邻等传统ML方法仍被广泛使用；同时CNN和LSTM被用于遥感和时间相关农业分析。最常用的数据集来源是Kaggle，典型输入包括土壤养分（NPK）、土壤pH、天气条件和NDVI、EVI等卫星指数。研究主要研究空白：有限的实时部署、低多数据源集成、低跨区域验证、低模型可解释性。研究表明可扩展、可解释的AI系统对农业实际应用具有重要意义。","MDPI",81,{"impact":19,"substance":18,"depth":19,"authority":74,"freshness":13,"relevant":20,"comment":75},13,"基于PRISMA的129篇文献系统综述，梳理作物推荐主流方法与四大研究空白，对农业AI落地有参考价值。",[77],{"name":71,"url":69},[25,79,26,80,28],"农业人工智能","作物推荐",[82,83],"精准农业 作物推荐 系统综述","XGBoost 随机森林 作物推荐","精准农业作物推荐系统综述-3248",{"doi":9,"openalex_id":9,"authors":86,"venue":9,"cited_by_count":34,"oa_url":9,"card":87,"direction":91,"ingested_from":93},[],{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"系统综述129篇2020-2026年文献，梳理精准农业作物推荐的方法、趋势与挑战。","PRISMA协议系统综述，分析183项研究筛选出的129篇文献。","集成学习（随机森林、XGBoost）表现最强，主要空白为实时部署、多源集成、跨区域验证与可解释性。","农业人工智能与决策模型","可探索可解释、可跨区域泛化的实时作物推荐系统，并融合多源遥感与物联网数据。","agent","2026-09-23T00:04:33.331160Z",{"id":96,"title":97,"url":98,"summary":99,"summary_zh":100,"content":9,"source_name":101,"source_url":98,"published_at":102,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":103,"score_detail":104,"sources":110,"tags":112,"search_phrases":115,"slug":118,"view_count":34,"doi":119,"paper":120,"created_at":149},2536,"Mapping Native Grass Cover with Random Forest Models: Sentinel-2 Versus Sentinel-2 Combined with Sentinel-1 SAR-Derived GLCM Texture Metrics","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183150","Temperate native grasslands in southeastern Australia have been extensively cleared for agriculture, and the remaining patches are under growing pressure from further land use change, climate variability, and invasive species. Mapping and monitoring their distribution and the cover of native and exotic grasses are critical for their conservation and management. Field-based methods are not always scalable or time-effective, and this study aimed to develop a scalable method to map and monitor the fractional cover-class maps of native C3 and native C4 grass cover as a component of remnant native grasslands on the western outskirts of Melbourne, Victoria, Australia. Field-based reference data for training and validation of random forest machine learning models were collected across multiple sites in 2021. Sentinel-2 optical spectral bands and vegetation indices were used as the primary input data, and Sentinel-1 Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture metrics were assessed for their capacity to improve the model. Results show that random forest models trained on Sentinel-2 data without GLCM texture information derived from Sentinel-1 SAR data provided a moderate overall accuracy (C3: 59.1%, C4: 78.1%). Class-specific metrics showed that reliability was highest for better represented lower-cover classes, particularly the 6–25% native C3 class and the 0–5% native C4 class, while higher-cover classes were less reliable because of the limited number of training and validation samples. Grass cover fractions were modelled well for sparse to moderate grass cover, but dense grass cover was not modelled accurately, probably due to limited high-cover samples in the training dataset. Model performance was not improved by the inclusion of Sentinel-1 SAR-derived GLCM texture metrics, indicating that C-band VH-polarised SAR is not sensitive to the fine-scale structural heterogeneity that characterises native grassland ecosystems. Sparse native C3 and C4 grasses could be mapped most reliably in the lower-cover classes as a component of grasslands with optical remote sensing, and the method developed here can now be applied to enable evidence-based management of grasslands, biodiversity conservation and the monitoring of grassland composition in the WGR and elsewhere. Higher-resolution structural datasets and more sophisticated machine learning approaches may be required to accurately predict native C3 and C4 grass cover fractions in denser grasslands.","澳大利亚东南部的温带原生草原已被大面积开垦用于农业，残余斑块正面临土地利用进一步变化、气候变率和入侵物种日益增大的压力。对其分布以及原生和外来草类覆盖度进行制图和监测，对于草原的保护和管理至关重要。基于实地调查的方法并非总是可扩展或省时的，本研究旨在开发一种可扩展的方法，以制图和监测作为残余原生草原组成部分的原生C3和原生C4草类覆盖度的分数覆盖等级图，研究区位于澳大利亚维多利亚州墨尔本西郊。用于训练和验证随机森林机器学习模型的实地参考数据于2021年在多个样点采集。研究以Sentinel-2光学光谱波段和植被指数作为主要输入数据，并评估了Sentinel-1合成孔径雷达（SAR）衍生的灰度共生矩阵（GLCM）纹理指标对提升模型性能的能力。结果表明，仅使用Sentinel-2数据（不含Sentinel-1 SAR衍生的GLCM纹理信息）训练的随机森林模型提供了中等的总体精度（C3：59.1%，C4：78.1%）。分类别指标显示，对于代表性较好的低覆盖度类别，可靠性最高，尤其是6–25%原生C3类别和0–5%原生C4类别，而较高覆盖度类别的可靠性较低，原因是训练和验证样本数量有限。草类覆盖度分数在稀疏至中等草类覆盖条件下建模效果良好，但茂密草类覆盖未能准确建模，可能是由于训练数据集中高覆盖度样本有限。纳入Sentinel-1 SAR衍生的GLCM纹理指标并未改善模型性能，表明C波段VH极化SAR对原生草原生态系统所特有的精细尺度结构异质性不敏感。作为草原的组成部分，稀疏的原生C3和C4草类在低覆盖度类别中利用光学遥感可最可靠地制图，本研究开发的方法现可应用于西维多利亚草原（WGR）及其他地区，以实现基于证据的草原管理、生物多样性保护和草原组成监测。准确预测原生C3和C4草类覆盖度分数可能需要更高分辨率的结构数据集和更先进的机器学习方法。","Remote Sensing","2026-09-13T00:00:00Z",71,{"impact":105,"substance":106,"depth":107,"authority":17,"freshness":108,"relevant":20,"comment":109},12,20,17,8,"方法新颖、结论明确（SAR纹理未提升精度），对草地遥感监测有参考价值，但属细分领域研究，影响范围有限。",[111],{"name":101,"url":98},[25,26,28,113,114],"草原生态","植被覆盖",[116,117],"智慧农业 机器学习 植被覆盖 草原生态","智慧农业 机器学习","智慧农业机器学习植被覆盖草原生态-2536","10.3390\u002Frs18183150",{"doi":119,"openalex_id":121,"authors":122,"venue":101,"cited_by_count":34,"oa_url":98,"card":144,"direction":59,"ingested_from":61},"W7212561645",[123,126,129,132,134,136,139,141],{"name":124,"orcid":125},"Sabah Sabaghy","https:\u002F\u002Forcid.org\u002F0000-0002-9453-8922",{"name":127,"orcid":128},"M. Abuzar","https:\u002F\u002Forcid.org\u002F0000-0002-6101-1307",{"name":130,"orcid":131},"Steve J. Sinclair","https:\u002F\u002Forcid.org\u002F0000-0002-4282-1021",{"name":133,"orcid":9},"Tony Dugdale",{"name":135,"orcid":9},"Vanessa Hutchins",{"name":137,"orcid":138},"Yogendra K. Karna","https:\u002F\u002Forcid.org\u002F0000-0002-2120-4710",{"name":140,"orcid":9},"Jonathan Wilson",{"name":142,"orcid":143},"Kathryn Sheffield","https:\u002F\u002Forcid.org\u002F0000-0003-2624-9739",{"tldr":145,"method":146,"finding":147,"direction":59,"opportunity":148},"用随机森林结合Sentinel-2与Sentinel-1纹理特征，绘制澳洲原生草地C3\u002FC4草覆盖度","2021年野外样点训练随机森林，Sentinel-2光谱与植被指数为主，Sent","仅用Sentinel-2精度中等（C3 59.1%、C4 78.1%），加入SAR纹理未提升，高覆盖","高覆盖度草地样本不足且C波段SAR不敏感，可探索高分辨率结构数据与深度模型提升密草覆盖反演。","2026-09-15T23:30:21.287053Z",{"id":151,"title":152,"url":153,"summary":154,"summary_zh":9,"content":155,"source_name":156,"source_url":9,"published_at":157,"category":158,"cover_url":9,"hotness":13,"is_selected":14,"score":159,"score_detail":160,"sources":163,"tags":165,"search_phrases":169,"slug":172,"view_count":34,"doi":9,"paper":9,"created_at":173},3230,"农业科普进校园 科技种子润心田——黑龙江省农科院\"农业科普进校园\"活动走进萧红中学","https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1079072228_121106822","黑龙江省农科院\"农业科普进校园\"活动走进哈尔滨市萧红中学，七位专家把实验室里的科研成果\"翻译\"成生动有趣的科普课堂，围绕食品安全、现代种业、智慧农业、遥感监测、生物防治、健康养殖、未来食品等方向，用图文展示、实物观察、视频演示和互动问答，把硬核科研变成通俗易懂的知识。作为省农科院2026年全国科普月重点活动之一，本次活动紧扣\"科技改变生活 创新赢得未来\"主题，聚焦现代农业科普和青少年科学素养提升，是落实全国科普月\"科普讲堂话前沿\"\"科学文化进基层\"部署、推动农业科普资源进校园的具体实践。","## 农业科普进校园 科技种子润心田 ——省农科院“农业科普进校园”活动走进萧红中学\n\n2026-09-21 17:29 来源: [大东北生活资讯](https:\u002F\u002Fwww.sohu.com\u002F)\n\n发布于：北京市\n\n当“农药去哪了”“玉米的秘密”“未来餐桌”这些话题出现在中学课堂，农业就不再是课本里遥远的名词，而是一场可以看、可以问、可以触摸的科技之旅。\n\n近日，省农科院“农业科普进校园”活动走进哈尔滨市萧红中学。七位专家把实验室里的科研成果“翻译”成生动有趣的科普课堂，带着同学们零距离感受现代农业的魅力。\n\n活动现场，专家们围绕食品安全、现代种业、智慧农业、遥感监测、生物防治、健康养殖、未来食品等方向，用图文展示、实物观察、视频演示和互动问答，把硬核科研变成通俗易懂的知识。同学们在观察、提问、交流中，慢慢理解了“藏粮于技”的意义，也感受到科技给农业带来的改变。\n\n作为省农科院2026年全国科普月重点活动之一，本次活动紧扣“科技改变生活 创新赢得未来”主题，聚焦现代农业科普和青少年科学素养提升，是落实全国科普月“科普讲堂话前沿”“科学文化进基层”部署、推动农业科普资源进校园的具体实践。以青少年为纽带，科研机构与社会公众之间多了一座沟通的桥；通过“请进来”与“走出去”，省农科院在粮食安全、乡村振兴等领域的创新成果被更多年轻人看见。知农、爱农、兴农的种子，正在校园里悄悄发芽。\n\n接下来，省农科院还将继续深化与学校的合作，让更多青少年在沉浸式体验中走近农业、爱上科学，为龙江农业现代化和乡村振兴积蓄青春力量。\n\n![Image 1](https:\u002F\u002Fq0.itc.cn\u002Fq_70\u002Fimages03\u002F20260921\u002Fccb551427fec4af1a347d1e5b521017a.jpeg)\n\n![Image 2](https:\u002F\u002Fq6.itc.cn\u002Fq_70\u002Fimages03\u002F20260921\u002F9f478e5a65a44654acd668ed043bff16.jpeg)\n\n**黑 龙 江 省 农 业 科 学 院 党 宣**\n\n信 息 来 源：科技推广处张唯一\n\n审核：李佳峰\n\n编辑：王红蕾[返回搜狐，查看更多](https:\u002F\u002Fwww.sohu.com\u002F?strategyid=00001 \"点击进入搜狐首页\")","黑龙江省农业科学院","2026-09-19T00:00:00Z","报道",39,{"impact":108,"substance":108,"depth":64,"authority":13,"freshness":161,"relevant":20,"comment":162},7,"省级农科院面向中学的科普活动通稿，属全国科普月系列活动，有科普教育价值但信息增量有限，适合作为科普类资讯收录。",[164],{"name":156,"url":153},[25,166,28,167,168],"农业科普","全国科普月","青少年科学素养",[170,171],"黑龙江省农科院 农业科普进校园","萧红中学 农业科普","黑龙江省农科院农业科普进校园-3230","2026-09-23T00:04:30.535981Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":9,"source_name":180,"source_url":177,"published_at":181,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":182,"score_detail":183,"sources":186,"tags":188,"search_phrases":191,"slug":194,"view_count":34,"doi":195,"paper":196,"created_at":206},3197,"YieldVision: Model-Driven Decision Support System for Crop Yield Prediction","https:\u002F\u002Fdoi.org\u002F10.56201\u002Frjpst.vol.9.no1.2026.pg176.194","Agriculture, a cornerstone of global food security, faces unprecedented challenges in achieving sustainable productivity. Crop yield prediction, a crucial aspect of agricultural planning, suffers from inefficiencies rooted in technological and methodological gaps. While traditional approaches rely on historical data and empirical estimations, they often fail to address the complexity of environmental interactions because they lack real-time data integration and limited access to accurate data, leading to inaccuracies and resource mismanagement. Addressing these issues requires the development of advanced Machine Learning (ML) models-based system that can predict crop yield with high accuracy in dynamic environments. This paper presents YieldVision, a decision support system driven by advanced ML models for Crop Yield Prediction based on environmental, soil, and crop-specific factors. In this work, we proposed three crop prediction models: Random Forest (RF), Long Short Time Memory (LSTM) and Hybrid (RF LSTM). RF regressor effectively captured the complex, nonlinear relationships among soil nutrients, fertilizer application, and temperature. The LSTM network learned the way short-term weather fluctuations impact crop development over time, and the hybrid model combined LSTM for environmental-related factors, and RF for soil-related factors. Experimental results on the Crop Yield Prediction Dataset containing soil and weather parameters showed that RF has the highest accuracy 𝑅2 = 99.10% and outperforms both LSTM and hybrid which have 87.50% and 97.30% accuracy respectively, confirming its suitability for the dataset. RF has the lowest RMSE and MAE compared to LSTM and hybrid models. The models are integrated into a decision support system that is deployed to operationalize the models in real-world settings with user interfaces accessible via mobile or web-delivered real-time alerts and yield forecasts to farmers and agronomists.","农业作为全球粮食安全的基石，在实现可持续生产力方面面临着前所未有的挑战。作物产量预测作为农业规划的关键环节，因技术和方法上的不足而效率低下。传统方法依赖历史数据和经验估算，但由于缺乏实时数据集成且难以获取准确数据，往往无法应对环境相互作用的复杂性，导致预测不准确和资源管理不善。解决这些问题需要开发基于先进机器学习（ML）模型的系统，能够在动态环境中高精度地预测作物产量。本文提出了YieldVision，一个由先进机器学习模型驱动的决策支持系统，用于基于环境、土壤和作物特定因素的作物产量预测。在本研究中，我们提出了三种作物预测模型：随机森林（RF）、长短期记忆网络（LSTM）和混合模型（RF-LSTM）。随机森林回归器有效捕捉了土壤养分、施肥量和温度之间复杂的非线性关系。LSTM网络学习了短期天气波动如何随时间影响作物发育，而混合模型将LSTM用于环境相关因素，RF用于土壤相关因素。在包含土壤和天气参数的作物产量预测数据集上的实验结果表明，随机森林具有最高的准确率𝑅2 = 99.10%，优于LSTM和混合模型，后两者的准确率分别为87.50%和97.30%，证实了随机森林对该数据集的适用性。与LSTM和混合模型相比，随机森林具有最低的RMSE和MAE。这些模型被集成到一个决策支持系统中，该系统已部署用于在实际环境中运行这些模型，其用户界面可通过移动端或网页访问，向农民和农艺师提供实时警报和产量预测。","RESEARCH JOURNAL OF PURE SCIENCE AND TECHNOLOGY","2026-09-21T00:00:00Z",68,{"impact":105,"substance":106,"depth":184,"authority":105,"freshness":108,"relevant":20,"comment":185},16,"提出RF、LSTM及混合模型并集成决策支持系统，方法对比与精度数据扎实，对智慧农业产量预测有参考价值，但属单篇论文且来源期刊影响力有限。",[187],{"name":180,"url":177},[25,79,189,26,190],"产量预测","决策支持系统",[192,193],"YieldVision 作物产量预测","RF LSTM 混合模型 产量预测","YieldVision作物产量预测-3197","10.56201\u002Frjpst.vol.9.no1.2026.pg176.194",{"doi":195,"openalex_id":197,"authors":198,"venue":180,"cited_by_count":34,"oa_url":9,"card":201,"direction":91,"ingested_from":61},"W7213883348",[199],{"name":200,"orcid":9},"D.J.S. Sako",{"tldr":202,"method":203,"finding":204,"direction":91,"opportunity":205},"提出YieldVision决策支持系统，用RF、LSTM及混合模型预测作物产量并部署应用。","基于土壤、环境与作物数据，构建RF、LSTM和RF-LSTM混合模型并集成到We","随机森林表现最佳，R²达99.10%，优于LSTM（87.50%）和混合模型（97.30%）。","可探索多源实时数据融合与模型可解释性，提升跨区域泛化能力并降低对高精度历史数据的依赖。","2026-09-22T23:30:43.591303Z",{"id":208,"title":209,"url":210,"summary":211,"summary_zh":212,"content":9,"source_name":101,"source_url":210,"published_at":213,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":72,"score_detail":214,"sources":217,"tags":219,"search_phrases":223,"slug":226,"view_count":34,"doi":227,"paper":228,"created_at":250},3186,"Cross-Regional Classification of Rice Cropping Systems Based on Within-Year Seasonal Composition Using Multimodal Remote-Sensing Time Series","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183241","Rice cropping-intensity products indicate the number of rice-growing seasons within a year but cannot reveal the seasonal compositions that define rice cropping systems. This limitation constrains detailed characterization of regional rice production patterns. To address this gap, this study developed a framework for rice cropping-system classification based on combinations of early-, middle-, and late-season rice cultivation. The framework integrates SAR-assisted reconstruction of cloud-contaminated Sentinel-2 time series using Sentinel-1 observations, Phenology-Constrained Time-Series Splicing (PTS) to construct PTS samples for underrepresented rice cropping-system classes, and an Ordered-Cycle Query Transformer (OCQT), which encodes annual-global and chronologically ordered-cycle representations with learnable season-query tokens for eight-class classification. On the spatial-block validation set, OCQT trained using real samples together with PTS samples achieved an overall accuracy of 0.9040, a macro-F1 of 0.8643, and a region-balanced macro-F1 of 0.8275, outperforming OCQT trained using only real samples. Across all four independent regional test sets, this configuration increased weighted-F1 by an average of 3.98 percentage points, with a maximum gain of 9.88 percentage points. This study advances rice monitoring from cropping intensity to explicit characterization of within-year seasonal composition and provides a framework for detailed agricultural monitoring using multi-source remote sensing and ordered-cycle representation learning.","水稻种植强度产品能够指示一年内的水稻种植季数，但无法揭示界定水稻种植系统的季节组成。这一局限制约了对区域水稻生产格局的精细刻画。为弥补这一不足，本研究构建了一个基于早稻、中稻和晚稻种植组合的水稻种植系统分类框架。该框架集成了以下三个部分：利用Sentinel-1观测对受云污染的Sentinel-2时间序列进行SAR辅助重建；采用物候约束时间序列拼接（Phenology-Constrained Time-Series Splicing, PTS）为样本不足的水稻种植系统类别构建PTS样本；以及有序循环查询Transformer（Ordered-Cycle Query Transformer, OCQT），该模型通过可学习的季节查询令牌编码年度全局表示和时间有序循环表示，以实现八类分类。在空间分块验证集上，使用真实样本与PTS样本共同训练的OCQT达到了0.9040的总体精度、0.8643的宏F1和0.8275的区域均衡宏F1，优于仅使用真实样本训练的OCQT。在全部四个独立区域测试集上，该配置将加权F1平均提升了3.98个百分点，最大提升达9.88个百分点。本研究推动了水稻监测从种植强度向年内季节组成的显式刻画迈进，并为利用多源遥感和有序循环表示学习开展精细农业监测提供了框架。","2026-09-20T00:00:00Z",{"impact":19,"substance":18,"depth":19,"authority":17,"freshness":215,"relevant":20,"comment":216},9,"提出融合SAR与光学时序及有序周期Transformer的水稻种植制度分类框架，方法新颖、精度可靠，对农业遥感监测有实质参考价值。",[218],{"name":101,"url":210},[25,28,220,221,222],"水稻种植","作物分类","多源遥感",[224,225],"Sentinel-1 Sentinel-2 水稻","水稻种植制度 遥感分类","Sentinel-1Sentinel-2水稻-3186","10.3390\u002Frs18183241",{"doi":227,"openalex_id":229,"authors":230,"venue":101,"cited_by_count":34,"oa_url":210,"card":245,"direction":59,"ingested_from":61},"W7213906853",[231,233,236,238,240,242],{"name":232,"orcid":9},"Jingrou Wang",{"name":234,"orcid":235},"Tingting Lu","https:\u002F\u002Forcid.org\u002F0000-0002-3140-5882",{"name":237,"orcid":9},"Beibei Xue",{"name":239,"orcid":9},"Lu Xu",{"name":241,"orcid":9},"Yanyan Shi",{"name":243,"orcid":244},"Dongping Ming","https:\u002F\u002Forcid.org\u002F0000-0002-3422-7399",{"tldr":246,"method":247,"finding":248,"direction":59,"opportunity":249},"提出多模态遥感时序框架，实现跨区域水稻种植制度八分类。","Sentinel-1\u002F2融合重建、PTS样本拼接与OCQT有序周期Transfo","结合PTS样本的OCQT精度达0.904，跨区加权F1平均提升3.98个百分点。","可将有序周期表示学习扩展至其他多季作物制度识别与跨区域泛化研究。","2026-09-22T23:30:25.478404Z",{"id":252,"title":253,"url":254,"summary":255,"summary_zh":256,"content":9,"source_name":257,"source_url":254,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":258,"score_detail":259,"sources":261,"tags":263,"search_phrases":267,"slug":270,"view_count":34,"doi":271,"paper":272,"created_at":299},3169,"Climate change and the rising threat of Macrophomina phaseolina: implications for food security, food safety, and sustainable crop health management","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10725-026-01525-5","Abstract Macrophomina phaseolina is a destructive soil-borne necrotrophic fungal pathogen that causes substantial yield losses in a wide range of economically important crops worldwide. Disease severity is strongly enhanced under drought, high temperature, and salinity stress, conditions that are becoming increasingly prevalent under current climate change scenarios. This review examines the interactions between climate-driven abiotic stress, host physiological regulation, and pathogen aggressiveness, highlighting how stress-induced disruptions in hormonal signalling, reactive oxygen species homeostasis, antioxidant defence systems, and plant metabolism collectively increase susceptibility to M. phaseolina . Recent advances in understanding pathogen virulence mechanisms, plant immune responses, and resistance-associated molecular pathways are synthesised together with emerging evidence from transcriptomics, proteomics, metabolomics, and comparative genomics. The review further evaluates current management strategies, including host resistance, biological control, plant growth-promoting microorganisms, stress priming, and integrated disease management, while discussing their limitations under field conditions. Emerging technologies such as precision agriculture, remote sensing, artificial intelligence-assisted disease forecasting, and multi-omics approaches are highlighted as promising tools for improving early diagnosis, risk prediction, and climate-resilient disease management. By integrating advances in plant physiology, molecular biology, and sustainable crop protection, this review provides a comprehensive framework for understanding M. phaseolina pathogenesis under changing environmental conditions. It identifies key research priorities to improve crop resilience and safeguard global food security.","摘要 菜豆壳球孢（Macrophomina phaseolina）是一种具有破坏性的土传死体营养型真菌病原菌，在全球范围内对多种具有重要经济价值的作物造成严重产量损失。在干旱、高温和盐胁迫条件下，病害严重程度显著加剧，而这些条件在当前气候变化情景下正变得越来越普遍。本文综述了气候驱动的非生物胁迫、寄主生理调控与病原菌致病力之间的相互作用，重点阐述了胁迫诱导的激素信号传导紊乱、活性氧稳态失衡、抗氧化防御系统受损以及植物代谢改变如何共同增加对菜豆壳球孢的易感性。本文综合了病原菌毒力机制、植物免疫反应及抗性相关分子通路方面的最新研究进展，并结合转录组学、蛋白质组学、代谢组学和比较基因组学的新兴证据。本文进一步评估了当前的管理策略，包括寄主抗性、生物防治、植物促生微生物、胁迫 priming 和病害综合管理，同时讨论了这些策略在田间条件下的局限性。精准农业、遥感、人工智能辅助病害预测和多组学方法等新兴技术被重点介绍为改善早期诊断、风险预测和气候韧性病害管理的有前景的工具。通过整合植物生理学、分子生物学和可持续作物保护方面的进展，本文为理解变化环境条件下菜豆壳球孢的致病机制提供了综合框架，并确定了提高作物韧性和保障全球粮食安全的关键研究优先方向。","Plant Growth Regulation",80,{"impact":19,"substance":106,"depth":19,"authority":17,"freshness":13,"relevant":20,"comment":260},"核心期刊综述，系统梳理气候胁迫下土传病害机制与AI遥感等智慧防控手段，对农业信息化与粮食安全主题有聚合价值。",[262],{"name":257,"url":254},[25,264,265,266,28],"粮食安全","植物病害","气候变化",[268,269],"Macrophomina phaseolina 病害 防控","气候变暖 土传病害 粮食安全","Macrophominaphaseolina病害防控-3169","10.1007\u002Fs10725-026-01525-5",{"doi":271,"openalex_id":273,"authors":274,"venue":257,"cited_by_count":34,"oa_url":254,"card":294,"direction":59,"ingested_from":61},"W7213972258",[275,277,279,282,285,288,291],{"name":276,"orcid":9},"Sindiswa Khawula",{"name":278,"orcid":9},"Siyabonga Ntshalitshali",{"name":280,"orcid":281},"Arun Gokul","https:\u002F\u002Forcid.org\u002F0000-0003-1575-0632",{"name":283,"orcid":284},"Lee‐Ann Niekerk","https:\u002F\u002Forcid.org\u002F0000-0002-9788-3131",{"name":286,"orcid":287},"Ashwil Klein","https:\u002F\u002Forcid.org\u002F0000-0002-5606-886X",{"name":289,"orcid":290},"Marshall Keyster","https:\u002F\u002Forcid.org\u002F0000-0002-8718-736X",{"name":292,"orcid":293},"Mbukeni Nkomo","https:\u002F\u002Forcid.org\u002F0000-0002-7652-1588",{"tldr":295,"method":296,"finding":297,"direction":91,"opportunity":298},"综述气候变化下干旱高温盐胁迫加剧菜豆壳球孢菌病害的机制与可持续防控策略。","整合转录组、蛋白组、代谢组、比较基因组及精准农业、遥感、AI预测等技术。","气候胁迫破坏激素与ROS平衡降低作物抗性，需多组学与智能技术实现早期预警和抗性管理。","可构建融合多组学与气象遥感的AI病害预警模型，并研发胁迫 priming 与生防协同的田间方案。","2026-09-22T23:30:22.790623Z"]