[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4914":3,"related-4914":87},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":11,"published_at":12,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":16,"score_detail":17,"sources":25,"tags":27,"primary_topic":32,"secondary_topics":38,"classification":45,"search_phrases":65,"slug":68,"view_count":23,"doi":69,"paper":70,"created_at":86},4914,"Water Optimizer: A Tool for Decision Support and Policy Analysis","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.413426","Water Optimizer is a tool for analyzing alternative water management strategies when the available water supply is limited. It can be used by producers to determine the profit maximizing crops to produce and the optimum amount of water to apply to each crop, given a particular water supply. It can also be used to evaluate the economic consequences of public policies which expand or reduce the amount of irrigation.","水资源优化器是一种在可用供水有限时分析替代水资源管理策略的工具。生产者可用其确定在特定供水量下能实现利润最大化的作物种类及每种作物的最优灌溉水量，也可用于评估扩大或减少灌溉面积的公共政策的经济后果。",null,"Lincoln (University of Nebraska)","https:\u002F\u002Fdigitalcommons.unl.edu\u002Fageconworkpap\u002F43","2026-10-13T00:00:00Z","论文",10,false,67,{"impact":18,"substance":19,"depth":20,"authority":21,"freshness":22,"relevant":23,"comment":24},12,18,16,13,8,1,"内布拉斯加大学开发的灌溉决策支持工具，方法实用但属细分领域工具类论文，公共影响有限。",[26],{"name":10,"url":11},[28,29,30,31],"决策支持系统","水资源管理","灌溉决策","农业政策分析",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":46,"locked":15,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,59],{"code":33,"score":52,"supported":53,"title":54,"abstract":55,"tags":58},11,true,[],[56,57,34],"灌溉","水资源",[29,30],{"code":40,"score":60,"supported":53,"title":61,"abstract":63,"tags":64},9,[62],"decision support",[],[28],[66,67],"Water Optimizer 灌溉决策","内布拉斯加大学 水资源管理","WaterOptimizer灌溉决策-4914","10.22004\u002Fag.econ.413426",{"doi":69,"openalex_id":71,"authors":72,"venue":10,"cited_by_count":77,"oa_url":78,"card":79,"direction":83,"ingested_from":85},"W38113320",[73,75],{"name":74,"orcid":9},"Derrel L. Martin",{"name":76,"orcid":9},"Ray Supalla",0,"http:\u002F\u002Fdigitalcommons.unl.edu\u002Fageconworkpap\u002F43",{"tldr":80,"method":81,"finding":82,"direction":83,"opportunity":84},"开发了Water Optimizer工具，在有限供水下优化作物种植与灌溉量以最大化利润。","基于优化模型与决策支持工具，分析不同供水策略及政策的经济影响。","该工具能帮助生产者选择利润最大化作物和最优灌溉量，并评估灌溉政策的经济后果。","农业人工智能与决策模型","可结合实时气象与土壤数据，开发动态灌溉决策模型，并拓展至多目标（经济-环境）优化。","openalex","2026-10-14T23:30:55.832240Z",{"total":88,"page":23,"page_size":88,"items":89},6,[90,155,234,303,373,447],{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":9,"source_name":96,"source_url":93,"published_at":97,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":98,"score_detail":99,"sources":102,"tags":104,"primary_topic":109,"secondary_topics":110,"classification":112,"search_phrases":137,"slug":140,"view_count":77,"doi":141,"paper":142,"created_at":154},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",69,{"impact":22,"substance":100,"depth":19,"authority":18,"freshness":60,"relevant":23,"comment":101},22,"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[103],{"name":96,"url":93},[105,106,30,107,108],"智慧农业","农业人工智能","安全保证","试验设计",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[111],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":15,"version":47,"method":48,"evidence":113},{"candidates":114,"agricultural_evidence":53},[115,120,125,132],{"code":33,"score":116,"supported":53,"title":117,"abstract":118,"tags":119},15,[34],[56,34],[30],{"code":40,"score":18,"supported":53,"title":121,"abstract":123,"tags":124},[122],"artificial intelligence",[62],[106],{"code":126,"score":88,"supported":53,"title":127,"abstract":128,"tags":131},"H2",[],[129,130],"土壤","soil",[],{"code":133,"score":23,"supported":15,"title":134,"abstract":135,"tags":136},"I1",[],[],[105],[138,139],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":141,"openalex_id":143,"authors":144,"venue":96,"cited_by_count":77,"oa_url":93,"card":149,"direction":83,"ingested_from":85},"W7223131205",[145,147],{"name":146,"orcid":9},"Nassima Benkhelifa",{"name":148,"orcid":9},"Yacine Aït Ahmed",{"tldr":150,"method":151,"finding":152,"direction":83,"opportunity":153},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z",{"id":156,"title":157,"url":158,"summary":159,"summary_zh":160,"content":9,"source_name":161,"source_url":158,"published_at":162,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":98,"score_detail":163,"sources":167,"tags":169,"primary_topic":172,"secondary_topics":173,"classification":175,"search_phrases":212,"slug":215,"view_count":77,"doi":216,"paper":217,"created_at":233},5496,"Multi-Crop Irrigation Need Classification Based on Machine Learning with an Indonesian-Language GenAI-UX Recommendation Layer","https:\u002F\u002Fdoi.org\u002F10.30871\u002Fjaic.v10i5.13774","Inefficient irrigation water use remains a critical challenge in Indonesian agriculture, and the complexity of raw sensor data hinders adoption by smallholder farmers who lack data-analysis skills. This study develops an intelligent irrigation Decision Support System that integrates interpretable Machine Learning with an Indonesian-language GenAI-UX recommendation layer, using a publicly available multi-crop irrigation scheduling dataset (501 records, nine crops) with an analytical focus on paddy and maize. Four components are combined into one pipeline: (i) irrigation-need classification compared across six algorithms; (ii) regression of a continuous water-requirement score; (iii) sensor-anomaly detection using Isolation Forest cross-validated with the Interquartile Range method; and (iv) a rule-based natural-language recommendation layer whose operating point is optimized by a Genetic Algorithm. Validity was safeguarded through stratified five-fold cross-validation and leakage-controlled preprocessing. Gradient Boosting achieved the best classification performance (F1-score 0.980; cross-validated F1 0.963), Random Forest Regressor reached R square of 0.807, Isolation Forest agreed with IQR outliers at 95.2%, and the Genetic Algorithm attained a confidence of 0.993 within realistic agronomic bounds. In an illustrative usability evaluation, the GenAI-UX interface obtained the highest System Usability Scale score (Good category). The results indicate that pairing accurate, interpretable models with a language-based delivery layer can transform complex sensor data into actionable irrigation recommendations, lowering the adoption barrier for non-technical users. Field validation with real respondents and edge deployment are identified as the next steps.","灌溉用水效率低下仍是印度尼西亚农业面临的一项关键挑战，而原始传感器数据的复杂性阻碍了缺乏数据分析技能的小农户采用相关技术。本研究开发了一种智能灌溉决策支持系统（Decision Support System），将可解释的机器学习与印度尼西亚语生成式人工智能用户体验（GenAI-UX）推荐层相结合，使用公开可用的多作物灌溉调度数据集（501条记录，九种作物），并以水稻和玉米为分析重点。四个组件被整合为一条流水线：（i）在六种算法之间比较灌溉需求分类；（ii）对连续需水量得分进行回归；（iii）使用孤立森林（Isolation Forest）进行传感器异常检测，并与四分位距（IQR）方法交叉验证；（iv）基于规则的自然语言推荐层，其操作点由遗传算法优化。通过分层五折交叉验证和防泄漏预处理保障了有效性。梯度提升（Gradient Boosting）取得了最佳分类性能（F1分数0.980；交叉验证F1为0.963），随机森林回归器（Random Forest Regressor）达到R平方0.807，孤立森林与IQR异常值的一致率为95.2%，遗传算法在现实农艺范围内达到0.993的置信度。在一项示例性可用性评估中，GenAI-UX界面获得了最高的系统可用性量表（System Usability Scale）得分（良好类别）。结果表明，将准确、可解释的模型与基于语言的交付层相结合，能够将复杂的传感器数据转化为可操作的灌溉建议，从而降低非技术用户的采用门槛。与真实受访者进行田间验证以及边缘部署被确定为下一步工作。","Journal of Applied Informatics and Computing","2026-10-19T00:00:00Z",{"impact":18,"substance":164,"depth":165,"authority":18,"freshness":22,"relevant":23,"comment":166},20,17,"方法组合完整、指标扎实的农业AI决策支持论文，但属印尼小农户场景的实验室级研究，公共影响有限，适合作为技术参考而非每日精选头条。",[168],{"name":161,"url":158},[105,106,170,171,30],"水稻","玉米",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[174],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":15,"version":47,"method":48,"evidence":176},{"candidates":177,"agricultural_evidence":53},[178,184,188,195,201,208],{"code":40,"score":19,"supported":53,"title":179,"abstract":181,"tags":183},[180],"machine learning",[182,180,62],"机器学习",[106],{"code":33,"score":116,"supported":53,"title":185,"abstract":186,"tags":187},[34],[56,34],[30],{"code":189,"score":190,"supported":53,"title":191,"abstract":192,"tags":194},"A1",7,[],[170,193],"paddy",[170],{"code":196,"score":190,"supported":53,"title":197,"abstract":198,"tags":200},"A3",[],[171,199],"maize",[171],{"code":202,"score":88,"supported":53,"title":203,"abstract":204,"tags":207},"G2",[],[205,206],"小农户","smallholder",[],{"code":133,"score":23,"supported":15,"title":209,"abstract":210,"tags":211},[],[],[105],[213,214],"印尼 多作物 灌溉决策","GenAI 灌溉推荐","印尼多作物灌溉决策-5496","10.30871\u002Fjaic.v10i5.13774",{"doi":216,"openalex_id":218,"authors":219,"venue":161,"cited_by_count":77,"oa_url":158,"card":227,"direction":83,"ingested_from":85},"W7223016583",[220,223,225],{"name":221,"orcid":222},"Muhammad Rusdi","https:\u002F\u002Forcid.org\u002F0000-0002-1048-5000",{"name":224,"orcid":9},"Zaenuddin Zaenuddin",{"name":226,"orcid":9},"Hendra Sanjaya",{"tldr":228,"method":229,"finding":230,"direction":231,"opportunity":232},"构建融合可解释机器学习与印尼语GenAI推荐层的智能灌溉决策支持系统。","六种分类算法、回归、孤立森林异常检测与遗传算法优化规则推荐层。","梯度提升分类F1达0.980，GenAI界面可用性最佳，降低非技术用户使用门槛。","智慧农业 \u002F 农业物联网","可探索多语言本地化GenAI推荐层与边缘部署，并开展真实农户田间验证。","2026-10-21T23:30:46.441864Z",{"id":235,"title":236,"url":237,"summary":238,"summary_zh":239,"content":9,"source_name":240,"source_url":237,"published_at":241,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":242,"score_detail":243,"sources":246,"tags":248,"primary_topic":251,"secondary_topics":252,"classification":254,"search_phrases":286,"slug":289,"view_count":77,"doi":290,"paper":291,"created_at":302},4801,"CROPINTEL DATASET: A COMPREHENSIVE AGROENVIRONMENTAL RESOURCE FOR CROP CLASSIFICATION, IRRIGATION PLANNING, AND YIELD ANALYSIS","https:\u002F\u002Fdoi.org\u002F10.30546\u002F09090.2025.210.005","The growing demand for sustainable agricultural intensification necessitates advanced tools for optimizing crop selection and resource management.This study presents an integrated analytical framework using machine learning to derive agro-environmental intelligence from the comprehensive CropIntel dataset.We evaluated a suite of over fifteen machine learning algorithms for three critical precision agriculture tasks: crop classification, seasonal irrigation requirement prediction, and yield potential forecasting.The results demonstrate the exceptional capability of tree-based models in this domain.Notably, Decision Tree, Bagging, and Gaussian Naïve Bayes classifiers achieved near-perfect accuracy (Acc≈99.7%) in identifying suitable crops based on climatic and edaphic conditions.For regression tasks, LightGBM and Histogram Gradient Boosting models proved most effective, explaining approximately 84% of the variance in irrigation needs (R²≈0.84)and about 70% of the variance in yield potential (R²≈0.696).These findings underscore the potential of machine learning to create powerful decision support systems that can guide crop selection, optimize water allocation, and provide reliable yield forecasts.This research contributes to a holistic, data-driven methodology that can enhance the efficiency and sustainability of agricultural practices.","对可持续农业集约化的需求日益增长，亟需先进工具来优化作物选择与资源管理。本研究提出一个集成的分析框架，利用机器学习从综合性的CropIntel数据集中提取农业环境智能信息。我们针对三个关键精准农业任务评估了超过十五种机器学习算法：作物分类、季节性灌溉需求预测和产量潜力预报。结果表明，基于树的模型在该领域具有卓越能力。值得注意的是，决策树、装袋法和高斯朴素贝叶斯分类器在基于气候和土壤条件识别适宜作物方面达到了近乎完美的准确率（Acc≈99.7%）。在回归任务中，LightGBM和直方图梯度提升模型最为有效，分别解释了约84%的灌溉需求方差（R²≈0.84）和约70%的产量潜力方差（R²≈0.696）。这些发现凸显了机器学习在构建强大决策支持系统方面的潜力，可指导作物选择、优化水资源分配并提供可靠的产量预报。本研究为一种整体性、数据驱动的方法论做出了贡献，有助于提升农业实践的效率与可持续性。","JOURNAL OF BAKU ENGINEERING UNIVERSITY- MATHEMATICS AND COMPUTER SCIENCE","2026-10-12T00:00:00Z",72,{"impact":18,"substance":244,"depth":165,"authority":21,"freshness":60,"relevant":23,"comment":245},21,"构建CropIntel农业环境数据集并系统评测15种以上机器学习算法，在作物分类、灌溉需求与产量预测上给出可复现结论，方法扎实但属细分领域学术进展，产业影响有限。",[247],{"name":240,"url":237},[105,106,249,250,30],"产量预测","精准农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[253],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":15,"version":47,"method":48,"evidence":255},{"candidates":256,"agricultural_evidence":53},[257,261,265,270,275,281],{"code":33,"score":19,"supported":53,"title":258,"abstract":259,"tags":260},[34],[56,57,34],[30],{"code":40,"score":14,"supported":53,"title":262,"abstract":263,"tags":264},[],[182,180,62],[106],{"code":133,"score":22,"supported":53,"title":266,"abstract":267,"tags":269},[],[250,268],"precision agriculture",[105,250],{"code":126,"score":271,"supported":53,"title":272,"abstract":273,"tags":274},3,[],[129],[],{"code":276,"score":271,"supported":53,"title":277,"abstract":278,"tags":280},"H8",[],[279],"可持续农业",[],{"code":282,"score":23,"supported":15,"title":283,"abstract":284,"tags":285},"I3",[],[],[249],[287,288],"CropIntel 数据集 作物分类","机器学习 灌溉需求 预测","CropIntel数据集作物分类-4801","10.30546\u002F09090.2025.210.005",{"doi":290,"openalex_id":292,"authors":293,"venue":240,"cited_by_count":77,"oa_url":237,"card":297,"direction":231,"ingested_from":85},"W7221521521",[294],{"name":295,"orcid":296},"Artughrul Gayibov","https:\u002F\u002Forcid.org\u002F0009-0009-7349-0286",{"tldr":298,"method":299,"finding":300,"direction":83,"opportunity":301},"基于CropIntel数据集，用15+种机器学习算法实现作物分类、灌溉需求预测与产量潜力预测。","CropIntel农业环境数据集，对比15+种机器学习算法，含树模型与贝叶斯分类","树模型分类准确率近99.7%，LightGBM等回归模型对灌溉需求R²约0.84、产量约0.70。","可探索多任务联合建模与模型可解释性，并将预测结果嵌入实时灌溉决策系统。","2026-10-13T23:30:08.688669Z",{"id":304,"title":305,"url":306,"summary":307,"summary_zh":308,"content":9,"source_name":309,"source_url":306,"published_at":310,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":311,"score_detail":312,"sources":314,"tags":316,"primary_topic":320,"secondary_topics":321,"classification":323,"search_phrases":349,"slug":352,"view_count":23,"doi":353,"paper":354,"created_at":372},4527,"Socio‐Economic Impact of Sustainable Development of Water Resources in the Semi‐Arid Regions of Village Raleras, Maharashtra: A Case Study Using Machine Learning Techniques","https:\u002F\u002Fdoi.org\u002F10.1111\u002F1477-8947.70100","ABSTRACT From a socio‐economic perspective, it is of interest to find out whether sustainable water resources can truly sustain rural livelihoods. Based on historical research, it is known that an improvement on resilient economic indicators such as ground water is often correlated with socio‐economic improvements and other aspects of rural livelihoods. The extent of factors affecting livelihood sustenance and improvement in the village of Raleras, Maharashtra, aids in creating a definite understanding of the facets correlating with groundwater restoration. Subsistence channels, arising as an outcome of the created impoundment structures, are tested for their impact on income gains and livelihood improvement. Examination on collected opinion was carried out to provide insights into the ‘before’ and ‘after’ (2015–2023) conditions. A five‐capitals framework has been employed for the interpretation of these results, along with a multivariate analysis to predict a causal framework for the improvement of livelihoods in agrarian economies through creation of check dams and watershed management. Time series study shows that there is greater variance in the NDVI progressively from 2021 to 2023 with values ranging from 0.25 to 0.50 in 2015, to 0.50–0.75 by 2021. Further increase in green patches in 2023 indicates enhanced vegetation health especially in the northern regions. Alternately, NDWI values elaborate initial severe water stress (−0.25 to 0.00) with progressive improvements in localized areas. The spatial variation map shows that eventually there is a notable improvement with more land supporting shrubbery along with more water bodies. The prominent increase in vegetation particularly in the later years can be attributed to water management initiatives undertaken in Raleras. The distributions of the annual average revenue from agricultural operations in 2015 (M = 146,907, SD = 95445.8) and 2023 (M = 706860.5, SD = 655748.3) showed a statistically significant improvement [ t (214) = 13.06, p \u003C 0.001]. The low p ‐value of 0.001205 ( p \u003C 0.05) of the educational facilities utilised indicates a statistically significant result, proving the association between educational quality and improved water facilities. All of the variables under evaluation, including the respondents' vehicle type, landholding type, and house type, have large and positive chi‐squared values, suggesting that the ‘before’ and ‘after’ distributions for each of these variables differ. Principal Component Analysis is used to summarise correlated livelihood indicators and reveals that income growth and livelihood improvement is multi‐dimensional. Multiple pathways to income change are identified through Partial Least Squares Regression, including agriculture and market‐linked channels. A Random Forest Regression Model is utilised to assess the relative importance of livelihood channels and baseline characteristics. Results indicate that improvement in irrigation access, crop diversification, housing quality, livestock ownership and female literacy are strongly associated with higher income gains. Hence, it may be said that the groundwater recharge as a result of construction of check dams must have improved agricultural activity or agricultural output in a material manner, and accordingly all capitals for Raleras have improved.","从社会经济视角来看，探究可持续水资源能否真正维持农村生计具有重要意义。基于历史研究可知，地下水等韧性经济指标的改善往往与社会经济进步及农村生计的其他方面相关联。马哈拉施特拉邦拉莱拉斯村影响生计维持与改善的因素程度，有助于明确理解与地下水恢复相关的各个维度。作为所建蓄水设施产物的生计渠道，其对面收入增长和生计改善的影响得到了检验。对收集到的意见进行了考察，以深入了解“之前”和“之后”（2015—2023年）的状况。研究采用了五资本框架来解读这些结果，并结合多变量分析，预测通过修建拦水坝和流域管理来改善农业经济生计的因果框架。时间序列研究表明，从2021年到2023年，NDVI的变异性逐渐增大，2015年数值范围为0.25至0.50，到2021年升至0.50至0.75。2023年绿色斑块进一步增加，表明植被健康状况改善，尤其是在北部地区。另一方面，NDWI值显示初期存在严重的水分胁迫（−0.25至0.00），后在局部地区逐步改善。空间变异图显示，最终出现了显著改善，更多土地能够支持灌木生长，同时水体也有所增加。植被的显著增加，尤其是在后期，可归因于拉莱拉斯村所实施的水资源管理举措。2015年（M = 146,907，SD = 95445.8）和2023年（M = 706860.5，SD = 655748.3）农业经营年均收入的分布显示出统计学上显著的改善[t(214) = 13.06，p \u003C 0.001]。所利用教育设施的p值为0.001205（p \u003C 0.05），表明结果具有统计学显著性，证明了教育质量与改善的供水设施之间的关联。所有受评估变量，包括受访者的车辆类型、土地持有类型和房屋类型，均具有较大且正向的卡方值，表明这些变量各自的“之前”和“之后”分布存在差异。","Natural Resources Forum","2026-10-07T00:00:00Z",73,{"impact":18,"substance":100,"depth":19,"authority":21,"freshness":22,"relevant":23,"comment":313},"以机器学习与遥感数据量化印度半干旱村庄水资源可持续性对生计的影响，方法扎实、结论具体，但属境外案例研究，对国内三农信息化仅有方法借鉴价值。",[315],{"name":309,"url":306},[317,182,29,318,319],"乡村振兴","遥感监测","灌溉农业",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[322],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":15,"version":47,"method":48,"evidence":324},{"candidates":325,"agricultural_evidence":53},[326,330,334,340,345],{"code":40,"score":60,"supported":53,"title":327,"abstract":328,"tags":329},[180],[],[182],{"code":33,"score":22,"supported":53,"title":331,"abstract":332,"tags":333},[],[57,34],[29,319],{"code":335,"score":271,"supported":53,"title":336,"abstract":337,"tags":339},"C",[],[338],"livestock",[],{"code":341,"score":23,"supported":15,"title":342,"abstract":343,"tags":344},"G",[],[],[317],{"code":282,"score":23,"supported":15,"title":346,"abstract":347,"tags":348},[],[],[318],[350,351],"Raleras 马哈拉施特拉 水资源","check dam 地下水 生计","Raleras马哈拉施特拉水资源-4527","10.1111\u002F1477-8947.70100",{"doi":353,"openalex_id":355,"authors":356,"venue":309,"cited_by_count":77,"oa_url":9,"card":366,"direction":83,"ingested_from":85},"W7220922038",[357,360,362,364],{"name":358,"orcid":359},"Papiya Deb","https:\u002F\u002Forcid.org\u002F0000-0002-6554-5111",{"name":361,"orcid":9},"Roopsha Deb",{"name":363,"orcid":9},"Udaysinh Bhosale",{"name":365,"orcid":9},"Vemula Sai Vikas",{"tldr":367,"method":368,"finding":369,"direction":370,"opportunity":371},"用机器学习评估印度半干旱村庄水资源可持续开发对农村生计的社会经济影响。","NDVI\u002FNDWI遥感、五资本框架、多元分析与卡方检验，2015–2023数据。","水管理举措显著提升植被覆盖与农业收入，教育设施改善与水资源改善显著相关。","数字乡村与农业信息化","可结合遥感时序与农户调查，量化水利工程对生计的因果效应并推广至其他半干旱区。","2026-10-09T23:30:50.079298Z",{"id":374,"title":375,"url":376,"summary":377,"summary_zh":378,"content":9,"source_name":379,"source_url":376,"published_at":380,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":381,"score_detail":382,"sources":384,"tags":386,"primary_topic":390,"secondary_topics":391,"classification":397,"search_phrases":416,"slug":419,"view_count":420,"doi":421,"paper":422,"created_at":446},3504,"WaterFormer-MS: A multi-stream deep learning framework for surface water segmentation from satellite imagery","https:\u002F\u002Fdoi.org\u002F10.24850\u002Fj-tyca-18-03-08","Accurate mapping of surface water bodies is essential for dam monitoring, irrigation planning, and sustainable water resource management. Conventional methods based on spectral indices, such as NDWI, can face challenges in complex environments due to spectral ambiguity, seasonal variability, and urban or vegetated surfaces. Deep learning approaches such as U-Net and DeepLabv3+ have improved water-body segmentation; however, accurately delineating fine boundaries and detecting small water bodies remain challenging. To address these limitations, this study proposes WaterFormer-MS, a multi-stream deep learning framework for surface water segmentation from Sentinel-2 imagery. The architecture integrates spectral, geometric, and contextual information through dedicated processing streams and combines the extracted features using a convolutional fusion module enhanced with CBAM attention. This design aims to improve feature representation and support accurate water-body delineation. The proposed model was trained and evaluated using Sentinel-2 imagery from a publicly available Kaggle dataset containing diverse land-cover conditions, including urban, vegetated, and agricultural environments. At 300 epochs, WaterFormer-MS achieved an overall accuracy of 97.4%, a precision of 93.2%, a recall of 94.2%, an F1-score of 93.7%, and an IoU of 89.7%. Within the experimental framework, WaterFormer-MS achieved higher values than the other proposed variants. Comparisons with conventional NDWI-based approaches and published deep learning methods suggest competitive performance; however, these comparisons should be interpreted cautiously because datasets, preprocessing procedures, training protocols, and evaluation settings may differ. The results support the potential of the proposed framework for surface water segmentation and environmental monitoring applications in diverse remote sensing scenarios.","准确绘制地表水体分布对于大坝监测、灌溉规划和水资源可持续管理至关重要。基于NDWI等光谱指数的传统方法在复杂环境中可能因光谱模糊性、季节性变化以及城市或植被覆盖地表而面临挑战。U-Net和DeepLabv3+等深度学习方法改善了水体分割效果，但精确描绘精细边界和检测小型水体仍然具有挑战性。为解决这些局限，本研究提出WaterFormer-MS，一种用于Sentinel-2影像地表水分割的多流深度学习框架。该架构通过专门的处理流整合光谱、几何和上下文信息，并利用经CBAM注意力增强的卷积融合模块将提取的特征进行融合。该设计旨在改善特征表征并支持精确的水体描绘。所提模型使用来自公开Kaggle数据集的Sentinel-2影像进行训练和评估，该数据集包含城市、植被和农业环境等多种土地覆盖条件。在300个训练轮次时，WaterFormer-MS取得了97.4%的总体精度、93.2%的精确率、94.2%的召回率、93.7%的F1分数和89.7%的IoU。在实验框架内，WaterFormer-MS取得了高于其他所提变体的数值。与传统基于NDWI的方法及已发表的深度学习方法进行比较，结果表明其性能具有竞争力；然而，这些比较应谨慎解读，因为数据集、预处理流程、训练方案和评估设置可能存在差异。研究结果支持所提框架在多样化遥感场景中用于地表水分割和环境监测应用的潜力。","Tecnología y Ciencias del Agua","2026-09-24T00:00:00Z",74,{"impact":116,"substance":100,"depth":19,"authority":14,"freshness":60,"relevant":23,"comment":383},"提出多流深度学习框架用于卫星影像地表水提取，精度指标明确，对灌溉规划与水资源监测有参考价值，但属方法类论文，产业影响有限。",[385],{"name":379,"url":376},[106,387,388,29,389],"Sentinel-2","遥感","地表水体提取",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[392,393],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":282,"slug":394,"name":395,"path":396,"parent_code":44},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":46,"locked":15,"version":47,"method":48,"evidence":398},{"candidates":399,"agricultural_evidence":53},[400,406,411],{"code":40,"score":116,"supported":53,"title":401,"abstract":403,"tags":405},[402],"deep learning",[404,402],"深度学习",[106],{"code":33,"score":14,"supported":53,"title":407,"abstract":408,"tags":410},[],[56,57,34,409],"water resource",[29],{"code":282,"score":22,"supported":53,"title":412,"abstract":413,"tags":415},[],[388,414],"remote sensing",[387,388],[417,418],"WaterFormer-MS 地表水 分割","Sentinel-2 水体提取 深度学习","WaterFormer-MS地表水分割-3504",2,"10.24850\u002Fj-tyca-18-03-08",{"doi":421,"openalex_id":423,"authors":424,"venue":379,"cited_by_count":77,"oa_url":376,"card":440,"direction":444,"ingested_from":85},"W7214228825",[425,428,431,434,437],{"name":426,"orcid":427},"Ayoub Benali","https:\u002F\u002Forcid.org\u002F0000-0002-8989-755X",{"name":429,"orcid":430},"Yesma Bendaha","https:\u002F\u002Forcid.org\u002F0000-0002-1347-856X",{"name":432,"orcid":433},"Malika Seddik Bouchouicha","https:\u002F\u002Forcid.org\u002F0009-0001-6092-784X",{"name":435,"orcid":436},"Zakaria Bellahcene","https:\u002F\u002Forcid.org\u002F0000-0003-4353-7871",{"name":438,"orcid":439},"Rachid Rimani","https:\u002F\u002Forcid.org\u002F0000-0002-1923-4312",{"tldr":441,"method":442,"finding":443,"direction":444,"opportunity":445},"提出多流深度学习框架WaterFormer-MS，从Sentinel-2影像中精准分割地表水体。","多流架构融合光谱、几何与上下文特征，CBAM注意力卷积融合，Sentinel-2","300轮训练下总体精度97.4%、IoU 89.7%，优于NDWI及对比深度学习方法。","农业遥感与作物表型","可探索多流框架在农业灌溉水体与小型水塘精细提取中的迁移，并引入时序Sentinel-2提升季节鲁棒性。","2026-09-25T23:30:31.338437Z",{"id":448,"title":449,"url":450,"summary":451,"summary_zh":452,"content":9,"source_name":453,"source_url":450,"published_at":454,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":455,"score_detail":456,"sources":458,"tags":460,"primary_topic":462,"secondary_topics":463,"classification":465,"search_phrases":497,"slug":500,"view_count":23,"doi":501,"paper":502,"created_at":512},1886,"Usage of Vermicompost Farming Enhanced Machine Learning","https:\u002F\u002Fdoi.org\u002F10.7753\u002Fijcatr1509.1005","Global food demand continues to rise, requiring agricultural systems that conserve limited water resources while maintaining productivity.This paper presents an IoT-based smart irrigation framework adapted for crops grown in Vermicompost, a substrate with distinct water retention properties compared to conventional soil.Environmental parameters including temperature, humidity, and substrate moisture are measured using field sensors and transmitted to a Raspberry Pi gateway for processing and cloud storage.A decision tree classifier, trained on Vermicompost-specific datasets, predicts irrigation requirements with improved accuracy.Farmers receive real-time email alerts, and all sensor data is archived for analytics.Experimental results demonstrate enhanced irrigation scheduling, reduced water consumption, and improved crop health when Vermicompost-specific thresholds are applied.The proposed system highlights the potential of integrating machine learning with eco-friendly substrates to achieve sustainable and climate-resilient farming.","全球粮食需求持续增长，要求农业系统在维持生产力的同时节约有限的水资源。本文提出了一种基于物联网的智能灌溉框架，适用于在蚯蚓粪（Vermicompost）中种植的作物——蚯蚓粪是一种相较于传统土壤具有独特保水特性的基质。环境参数，包括温度、湿度和基质含水量，通过田间传感器进行测量，并传输至树莓派（Raspberry Pi）网关进行处理和云端存储。基于蚯蚓粪特定数据集训练的决策树分类器，能够以更高的精度预测灌溉需求。农户可实时接收电子邮件警报，所有传感器数据均被存档以供分析。实验结果表明，应用蚯蚓粪特定阈值后，灌溉调度得到优化，用水量减少，作物健康状况得到改善。该系统凸显了将机器学习与生态友好型基质相结合，以实现可持续和气候适应性农业的潜力。","International Journal of Computer Applications Technology and Research","2026-09-07T00:00:00Z",57,{"impact":18,"substance":19,"depth":116,"authority":14,"freshness":420,"relevant":23,"comment":457},"论文提出基于蚯蚓粪基质的IoT智能灌溉框架，结合机器学习优化灌溉，具有节水增效潜力，但影响范围有限，时效性较低。",[459],{"name":453,"url":450},[105,106,461,30],"蚯蚓粪",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[464],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":15,"version":47,"method":48,"evidence":466},{"candidates":467,"agricultural_evidence":53},[468,472,476,480,487,493],{"code":40,"score":116,"supported":53,"title":469,"abstract":470,"tags":471},[180],[182,180],[106],{"code":33,"score":14,"supported":53,"title":473,"abstract":474,"tags":475},[],[56,57,34],[30],{"code":126,"score":88,"supported":53,"title":477,"abstract":478,"tags":479},[],[129,130],[],{"code":481,"score":88,"supported":53,"title":482,"abstract":483,"tags":486},"I5",[],[484,485],"物联网","IoT",[],{"code":488,"score":271,"supported":53,"title":489,"abstract":490,"tags":492},"H7",[],[491],"气候适应",[],{"code":133,"score":23,"supported":15,"title":494,"abstract":495,"tags":496},[],[],[105],[498,499],"农业人工智能 智慧农业 灌溉决策 蚯蚓粪","农业人工智能 智慧农业","农业人工智能智慧农业灌溉决策蚯蚓粪-1886","10.7753\u002Fijcatr1509.1005",{"doi":501,"openalex_id":503,"authors":504,"venue":453,"cited_by_count":77,"oa_url":450,"card":507,"direction":231,"ingested_from":85},"W7211898479",[505],{"name":506,"orcid":9},"J Devika",{"tldr":508,"method":509,"finding":510,"direction":231,"opportunity":511},"提出基于物联网和机器学习的蚯蚓粪种植智能灌溉系统，优化灌溉调度。","使用传感器采集环境数据，经树莓派网关处理，决策树分类器预测灌溉需求。","蚯蚓粪特定阈值下灌溉更精准，节水且改善作物健康。","可探索蚯蚓粪与其他有机基质结合不同作物，或引入深度强化学习优化灌溉策略。","2026-09-08T23:30:08.048441Z"]