[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4171":3,"related-4171":113},{"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":38,"classification":39,"search_phrases":77,"slug":80,"view_count":54,"doi":81,"paper":82,"created_at":112},4171,"A machine learning framework for predicting potential urban cool island locations using Landsat 9 data: a case study of Oued Zenati, Algeria","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72875-3","Abstract Urban heat stress is increasing in semi-arid Mediterranean towns, making the identification of Urban Cool Island (UCI) patterns essential for climate-resilient urban planning. This study developed an integrated remote sensing, GIS, and explainable machine-learning framework to map potential UCI locations in Oued Zenati, northeastern Algeria, using Landsat 9 imagery and geospatial environmental variables processed in Google Earth Engine. ArcGIS Desktop 10.8 was used for the final cartographic layout and map generation. Predictor variables included land surface temperature, albedo, elevation, slope, distance to roads, distance to rivers, and population density. Because field observations of UCIs were unavailable, proxy reference samples were generated from vegetation, moisture, and built-up indicators. Under the prevailing semi-arid conditions, NDWI values remained below − 0.06 across the study area, and all cool-zone reference samples were therefore derived from the vegetation criterion (NDVI ≥ 0.20). Four machine-learning algorithms (Random Forest, Gradient Tree Boosting, CART, and MaxEnt) were evaluated using a 70\u002F30 training-testing split and independent validation. Gradient Tree Boosting achieved the highest discrimination ability (ROC-AUC = 0.916), whereas Random Forest provided the best threshold-dependent performance, with an accuracy of 0.900, Kappa of 0.800, Precision of 0.929, and F1-score of 0.897. Susceptibility maps showed that cooling potential is concentrated in vegetated and agricultural areas, while built-up and bare surfaces exhibit low cooling potential. SHAP analysis identified elevation as the most influential predictor, followed by slope, albedo, and land surface temperature, whereas distance to roads, distance to rivers, and population density exerted comparatively weaker effects. The proposed framework provides a robust, interpretable, and transferable approach for Urban Cool Island susceptibility mapping, supporting climate adaptation, green infrastructure planning, and sustainable urban development in semi-arid cities.","摘要 在半干旱地中海城镇，城市热胁迫日益加剧，因此识别城市冷岛（Urban Cool Island, UCI）格局对于气候适应性城市规划至关重要。本研究开发了一个集成遥感、GIS和可解释机器学习的框架，利用Landsat 9影像和在Google Earth Engine中处理的地理空间环境变量，绘制了阿尔及利亚东北部Oued Zenati潜在UCI位置图。使用ArcGIS Desktop 10.8进行最终制图布局和地图生成。预测变量包括地表温度、反照率、高程、坡度、距道路距离、距河流距离和人口密度。由于缺乏UCI的实地观测数据，研究从植被、湿度和建成区指标生成了代理参考样本。在半干旱条件下，研究区NDWI值均低于−0.06，因此所有冷区参考样本均基于植被标准（NDVI ≥ 0.20）提取。采用70\u002F30的训练-测试划分和独立验证，评估了四种机器学习算法（随机森林、梯度树提升、CART和MaxEnt）。梯度树提升取得了最高的判别能力（ROC-AUC = 0.916），而随机森林在阈值依赖性能方面表现最佳，准确率为0.900，Kappa为0.800，精确率为0.929，F1分数为0.897。敏感性图显示，降温潜力集中在植被和农业区域，而建成区和裸地表面的降温潜力较低。SHAP分析表明，高程是最具影响力的预测变量，其次是坡度、反照率和地表温度，而距道路距离、距河流距离和人口密度的影响相对较弱。所提出的框架为城市冷岛敏感性制图提供了一种稳健、可解释且可迁移的方法，可支持半干旱城市的气候适应、绿色基础设施规划和可持续城市发展。",null,"Scientific Reports","2026-10-03T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,16,13,9,1,"方法新颖、结论可靠的城市冷岛遥感预测研究，对农业信息化与气候适应型规划有参考价值，但属区域案例、影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","机器学习","遥感","气候适应","城市热岛",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,59,65,72],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},12,true,[49],"machine learning",[28],[28],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":58},"H7",7,[],[30,57],"干旱",[30],{"code":60,"score":54,"supported":47,"title":61,"abstract":62,"tags":64},"I3",[],[29,63],"remote sensing",[29],{"code":66,"score":67,"supported":47,"title":68,"abstract":69,"tags":71},"I4",3,[],[70],"GIS",[],{"code":73,"score":22,"supported":14,"title":74,"abstract":75,"tags":76},"I1",[],[],[27],[78,79],"Landsat 9 城市冷岛 阿尔及利亚","Oued Zenati 城市冷岛 遥感","Landsat9城市冷岛阿尔及利亚-4171","10.1038\u002Fs41598-026-72875-3",{"doi":81,"openalex_id":83,"authors":84,"venue":10,"cited_by_count":104,"oa_url":6,"card":105,"direction":109,"ingested_from":111},"W7218855599",[85,87,89,92,95,98,101],{"name":86,"orcid":9},"Amina Naidja",{"name":88,"orcid":9},"Dalal FARID",{"name":90,"orcid":91},"Djamal Bengusmia","https:\u002F\u002Forcid.org\u002F0000-0003-4480-8637",{"name":93,"orcid":94},"Haythem Dinar","https:\u002F\u002Forcid.org\u002F0000-0002-4504-2963",{"name":96,"orcid":97},"Nouh Rebouh","https:\u002F\u002Forcid.org\u002F0000-0001-6214-2942",{"name":99,"orcid":100},"Imtiyaz Akbar Najar","https:\u002F\u002Forcid.org\u002F0000-0002-7338-6771",{"name":102,"orcid":103},"Nadeem Ahmad Khan","https:\u002F\u002Forcid.org\u002F0000-0003-4366-9639",0,{"tldr":106,"method":107,"finding":108,"direction":109,"opportunity":110},"用遥感与可解释机器学习预测阿尔及利亚半干旱城市潜在城市冷岛位置。","Landsat 9、Google Earth Engine、GIS与四种机器学习","梯度提升树判别最优（AUC=0.916），冷岛潜力集中于植被与农业区，海拔为最关键预测因子。","农业遥感与作物表型","可迁移至其他半干旱城市，并引入时序遥感与实地冷岛观测验证，提升模型泛化性。","openalex","2026-10-04T23:30:24.545092Z",{"total":114,"page":22,"page_size":114,"items":115},6,[116,180,245,322,407,512],{"id":117,"title":118,"url":119,"summary":120,"summary_zh":9,"content":9,"source_name":121,"source_url":9,"published_at":122,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":123,"score_detail":124,"sources":129,"tags":131,"primary_topic":134,"secondary_topics":135,"classification":142,"search_phrases":166,"slug":169,"view_count":158,"doi":9,"paper":170,"created_at":179},4286,"Applications of Artificial Intelligence in Soil Characterization and Agriculture: A Systematic Review","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37386408","Navarro Rubio等系统综述2018-2026年196项AI土壤特性与农业应用研究，遵循PRISMA 2020框架。综述发现研究明显向数据驱动方法转变，机器学习和深度学习模型主导近期研究；随机森林、支持向量机、梯度提升方法、人工神经网络、卷积神经网络、长短期记忆架构最常被报告；主要数据源包括原位传感、实验室测量、遥感影像和环境协变量，常通过多源数据融合框架集成；树集成模型在不同土壤特性上提供稳健性能。","Agronomy 2026 16(13):1241 DOI:10.3390\u002Fagronomy16131241","2026-10-02T00:00:00Z",78,{"impact":125,"substance":126,"depth":125,"authority":127,"freshness":114,"relevant":22,"comment":128},18,22,14,"PRISMA框架下196项研究的系统综述，方法学扎实、结论可靠，对农业AI土壤应用具较高参考价值。",[130],{"name":121,"url":119},[27,132,28,29,133],"农业人工智能","土壤监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[136],{"code":137,"slug":138,"name":139,"path":140,"parent_code":141},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":143},{"candidates":144,"agricultural_evidence":47},[145,151,157,162],{"code":33,"score":19,"supported":47,"title":146,"abstract":148,"tags":150},[147],"artificial intelligence",[149,28],"深度学习",[132,28],{"code":137,"score":152,"supported":47,"title":153,"abstract":154,"tags":156},11,[138],[155],"土壤",[],{"code":60,"score":158,"supported":47,"title":159,"abstract":160,"tags":161},4,[],[29],[29],{"code":73,"score":22,"supported":14,"title":163,"abstract":164,"tags":165},[],[],[27],[167,168],"AI 土壤特性 系统综述","机器学习 土壤 遥感","AI土壤特性系统综述-4286",{"doi":9,"openalex_id":9,"authors":171,"venue":9,"cited_by_count":104,"oa_url":9,"card":172,"direction":176,"ingested_from":178},[],{"tldr":173,"method":174,"finding":175,"direction":176,"opportunity":177},"系统综述2018-2026年196项AI用于土壤特性与农业的研究。","PRISMA 2020框架，分析机器学习与深度学习模型及多源数据融合。","研究转向数据驱动，树集成模型在不同土壤特性上表现稳健。","农业人工智能与决策模型","可探索可解释AI与多源数据融合在土壤特性预测中的泛化能力。","agent","2026-10-06T00:05:04.976398Z",{"id":181,"title":182,"url":183,"summary":184,"summary_zh":185,"content":9,"source_name":186,"source_url":183,"published_at":122,"category":12,"cover_url":9,"hotness":187,"is_selected":14,"score":188,"score_detail":189,"sources":191,"tags":195,"primary_topic":197,"secondary_topics":198,"classification":199,"search_phrases":228,"slug":231,"view_count":232,"doi":233,"paper":234,"created_at":244},4238,"Comparative Performance of Machine Learning and Deep Learning Algorithms for Agricultural Land Mapping in Anambra State, Nigeria","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23106955","Abstract - Accurate delineation of agricultural land is fundamental to agricultural planning, food-security assessment, land-resource management, and monitoring of land-use transformation. However, the heterogeneous and fragmented character of agricultural landscapes in southeastern Nigeria presents substantial challenges for satellite-image classification. This study comparatively evaluated Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) for agricultural land mapping in Anambra State, Nigeria. Remotely sensed imagery was classified into waterbody, dense forest, agricultural land, built-up area, and bare ground using the four algorithms. Model performance was evaluated using 210 validation samples and multiple confusion-matrix metrics, including sensitivity, precision, specificity, negative predictive value, overall accuracy, balanced accuracy, and Matthew’s correlation coefficient. Agricultural land estimates varied from 1,000.26 km² (21.78%) for SVM to 1,099.12 km² (23.93%) for ANN. SVM achieved the strongest overall performance, recording 88.5% sensitivity, 85.2% precision, 75.0% specificity, 83.3% overall accuracy, 81.7% balanced accuracy, and a Matthews correlation coefficient of 0.643. CNN produced the lowest overall accuracy of 74.3%. The findings demonstrate that increased model complexity does not necessarily translate into superior classification performance and establish SVM as an effective approach for regional agricultural land delineation under heterogeneous tropical landscape conditions. Keywords: agricultural land mapping; machine learning; deep learning; Support Vector Machine; Random Forest; artificial neural network; convolutional neural network; remote sensing","摘要——准确划定农业用地是农业规划、粮食安全评估、土地资源管理和土地利用变化监测的基础。然而，尼日利亚东南部农业景观的异质性和破碎化特征给卫星图像分类带来了巨大挑战。本研究比较评估了随机森林（RF）、支持向量机（SVM）、人工神经网络（ANN）和卷积神经网络（CNN）在尼日利亚阿南布拉州农业用地制图中的表现。利用这四种算法将遥感影像分类为水体、密林、农业用地、建成区和裸地。使用210个验证样本和多种混淆矩阵指标评估模型性能，包括灵敏度、精确率、特异度、阴性预测值、总体精度、平衡精度和马修斯相关系数。农业用地估计值从SVM的1,000.26 km²（21.78%）到ANN的1,099.12 km²（23.93%）不等。SVM取得了最强的总体表现，灵敏度为88.5%，精确率为85.2%，特异度为75.0%，总体精度为83.3%，平衡精度为81.7%，马修斯相关系数为0.643。CNN的总体精度最低，为74.3%。研究结果表明，模型复杂度的增加并不必然带来更优的分类性能，并确立了SVM作为异质性热带景观条件下区域农业用地划定的有效方法。关键词：农业用地制图；机器学习；深度学习；支持向量机；随机森林；人工神经网络；卷积神经网络；遥感","International Journal of Emerging Trends in Engineering and Development",25,61,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":21,"relevant":22,"comment":190},"尼日利亚区域案例研究，方法对比扎实但影响范围限于学术圈，可作为遥感与农业AI主题的参考素材。",[192,193],{"name":186,"url":183},{"name":186,"url":194},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23106954",[27,132,28,29,196],"农地制图",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":200},{"candidates":201,"agricultural_evidence":47},[202,208,212,218,224],{"code":33,"score":203,"supported":47,"title":204,"abstract":206,"tags":207},27,[49,205],"deep learning",[149,28,49,205],[132,28],{"code":60,"score":54,"supported":47,"title":209,"abstract":210,"tags":211},[],[29,63],[29],{"code":213,"score":67,"supported":47,"title":214,"abstract":215,"tags":217},"A8",[],[216],"粮食安全",[],{"code":219,"score":67,"supported":47,"title":220,"abstract":221,"tags":223},"J1",[],[222],"农业规划",[],{"code":73,"score":22,"supported":14,"title":225,"abstract":226,"tags":227},[],[],[27],[229,230],"农业人工智能 农地制图 智慧农业 机器学习","农业人工智能 农地制图","农业人工智能农地制图智慧农业机器学习-4238",2,"10.5281\u002Fzenodo.23106955",{"doi":233,"openalex_id":235,"authors":236,"venue":186,"cited_by_count":104,"oa_url":183,"card":239,"direction":109,"ingested_from":111},"W7219591984",[237],{"name":238,"orcid":9},"J.I. Igbokwe, C.E Ejikeme, J.O and Obi",{"tldr":240,"method":241,"finding":242,"direction":109,"opportunity":243},"比较RF、SVM、ANN、CNN在尼日利亚农业用地制图中的表现","遥感影像分类，210个验证样本，多种混淆矩阵指标评估","SVM总体精度最高（83.3%），CNN最低（74.3%），模型复杂度不保证性能","在碎片化热带景观中，可探索轻量级集成学习或迁移学习以平衡精度与效率","2026-10-05T23:30:27.987196Z",{"id":246,"title":247,"url":248,"summary":249,"summary_zh":250,"content":9,"source_name":251,"source_url":248,"published_at":252,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":253,"score_detail":254,"sources":257,"tags":259,"primary_topic":262,"secondary_topics":263,"classification":269,"search_phrases":298,"slug":301,"view_count":232,"doi":302,"paper":303,"created_at":321},3916,"Hyperspectral inversion of soil total nitrogen content using spectral and machine learning","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbjb.v55i3.93850","The Agro-pastoral Transitional Ecotone of Northern Shaanxi is an ecologically fragile region. Soil nitrogen is a critical factor influencing crop productivity and regional ecological balance. Rapid and accurate monitoring of farmland soil total nitrogen (TN) content is therefore essential for implementing precision agriculture and ensuring sustainable land use in this area. This study focused on the cultivated soils of Dingbian County, a representative area within this ecotone. Field-collected soil samples underwent TN content determination and laboratory-based hyper spectral reflectance measurement. The original spectral reflectance (R) was first transformed using mathematical methods, including first-derivative (FDR), second-derivative (SDR), and logarithm of the reciprocal's first-derivative (LRFD) analysis, to enhance spectral information and suppress background noise. Subsequently, the successive projections algorithm (SPA) and correlation analysis (CA) were integrated to identify feature wavelengths highly correlated with soil TN from the transformed spectral data. Partial Least Squares Regression (PLSR), Decision Tree (DT), and Random Forest (RF) were developed to estimate soil TN content. Model performance was evaluated and compared using the coefficient of determination (R²), root mean square error (RMSE), and ratio of performance to deviation (RPD). The results indicated that: (1) Spectral derivative transformations effectively highlighted subtle information, with LRFD showing the greatest sensitivity to TN. Models built using LRFD-derived features consistently outperformed those based on original reflectance. (2) Among the machine learning algorithms, the Random Forest (RF) model demonstrated superior predictive performance on the validation set (R² = 0.85, RMSE = 0.11 g\u002Fkg, RPD = 2.15), indicating the highest stability and generalization capability. The PLSR model performed moderately well, while the DT model was prone to over fitting and yielded the lowest accuracy. (3) This study confirms that the integration of hyper spectral technology and machine learning algorithms enables efficient and precise estimation of soil TN content in farmland within the Northern Shaanxi agro-pastoral ecotone, providing a reliable technical framework for soil fertility assessment and precision nutrient management in the region. Bangladesh J. Bot. 55(3): 605-614, 2026 (September)","陕北农牧交错带是生态脆弱区，土壤氮素是影响作物生产力和区域生态平衡的关键因子。快速、准确地监测农田土壤全氮（TN）含量，对于该区实施精准农业和保障土地可持续利用至关重要。本研究以该交错带典型区域定边县的耕作土壤为研究对象，对野外采集的土壤样品进行TN含量测定和实验室高光谱反射率测量。首先采用数学方法对原始光谱反射率（R）进行变换，包括一阶微分（FDR）、二阶微分（SDR）和倒数对数一阶微分（LRFD）分析，以增强光谱信息并抑制背景噪声。随后，将连续投影算法（SPA）与相关分析（CA）相结合，从变换后的光谱数据中筛选出与土壤TN高度相关的特征波长。构建偏最小二乘回归（PLSR）、决策树（DT）和随机森林（RF）模型估算土壤TN含量。采用决定系数（R²）、均方根误差（RMSE）和相对分析误差（RPD）对模型性能进行评价与比较。结果表明：（1）光谱微分变换有效凸显了细微信息，其中LRFD对TN的敏感性最强，基于LRFD衍生特征构建的模型始终优于基于原始反射率的模型。（2）在机器学习算法中，随机森林（RF）模型在验证集上表现出优越的预测性能（R² = 0.85，RMSE = 0.11 g\u002Fkg，RPD = 2.15），具有最高的稳定性和泛化能力。PLSR模型表现中等，而DT模型易于过拟合，精度最低。（3）本研究证实，高光谱技术与机器学习算法相结合能够高效、精确地估算陕北农牧交错带农田土壤TN含量，为该区域土壤肥力评估和精准养分管理提供了可靠的技术框架。Bangladesh J. Bot. 55(3): 605-614, 2026 (September)","Bangladesh Journal of Botany","2026-09-30T00:00:00Z",70,{"impact":46,"substance":18,"depth":255,"authority":20,"freshness":17,"relevant":22,"comment":256},17,"高光谱结合机器学习反演土壤全氮，方法对比扎实、结论可靠，对精准农业养分管理有参考价值，但属区域性田间研究，影响范围有限。",[258],{"name":251,"url":248},[27,260,28,29,261],"精准施肥","土壤氮素",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[264,265],{"code":137,"slug":138,"name":139,"path":140,"parent_code":141},{"code":60,"slug":266,"name":267,"path":268,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":270},{"candidates":271,"agricultural_evidence":47},[272,277,281,286,292],{"code":33,"score":273,"supported":47,"title":274,"abstract":275,"tags":276},15,[49],[28,49],[28],{"code":137,"score":127,"supported":47,"title":278,"abstract":279,"tags":280},[138],[155,138],[],{"code":60,"score":21,"supported":47,"title":282,"abstract":284,"tags":285},[283],"hyperspectral",[],[29],{"code":73,"score":54,"supported":47,"title":287,"abstract":288,"tags":291},[],[289,290],"精准农业","precision agriculture",[27],{"code":293,"score":158,"supported":47,"title":294,"abstract":295,"tags":297},"F3",[],[296],"养分管理",[260],[299,300],"定边县 土壤全氮 高光谱","陕北农牧交错带 土壤氮素 反演","定边县土壤全氮高光谱-3916","10.3329\u002Fbjb.v55i3.93850",{"doi":302,"openalex_id":304,"authors":305,"venue":251,"cited_by_count":104,"oa_url":248,"card":316,"direction":176,"ingested_from":111},"W7214885868",[306,309,311,313],{"name":307,"orcid":308},"Zhenyu Dong","https:\u002F\u002Forcid.org\u002F0000-0003-0028-3293",{"name":310,"orcid":9},"Ni Wang",{"name":312,"orcid":9},"Jiancang Xie",{"name":314,"orcid":315},"Jungang Luo","https:\u002F\u002Forcid.org\u002F0009-0009-0285-4839",{"tldr":317,"method":318,"finding":319,"direction":109,"opportunity":320},"用高光谱与机器学习反演陕北农牧交错带农田土壤全氮含量。","野外采样测光谱，经FDR\u002FSDR\u002FLRFD变换，SPA+CA选特征波长，建PLS","LRFD变换最敏感，RF模型验证集R²=0.85、RPD=2.15，精度与泛化最优。","可探索多源遥感与深度学习融合，提升生态脆弱区土壤氮素时空反演与制图能力。","2026-10-01T23:30:53.960449Z",{"id":323,"title":324,"url":325,"summary":326,"summary_zh":327,"content":9,"source_name":328,"source_url":325,"published_at":329,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":253,"score_detail":330,"sources":332,"tags":334,"primary_topic":337,"secondary_topics":342,"classification":345,"search_phrases":375,"slug":378,"view_count":22,"doi":379,"paper":380,"created_at":406},3863,"Species identification of crops and weeds in wheat fields using hyperspectral data and machine learning algorithms","https:\u002F\u002Fdoi.org\u002F10.31489\u002F2026feb3\u002F137-151","Based on establishing the relationship between the morphological and anatomical properties of plant tissues and the recognition efficiency of the machine learning algorithm, a comprehensive assessment of the limitations of ML algorithms (using Random Forest as an example) in the multiclass identification of crops and weeds in the agroecosystems of northeastern Kazakhstan was carried out for the first time. The study substantiates the principal biological causes manifested in the optical overlap of classes in the hyperspectral feature space. Data collection was performed with a hyperspectral sensor in the VIS-NIR range. The analysis of spectral profiles was carried out based on variance distribution assessment using principal component analysis and the construction of reflectance curves. Classification of the extracted data was performed using the Random Forest algorithm. Spring wheat (Triticum aestivum) crops at different phenological phases and five dominant weed species (Euphorbia virgata, Descurainia sophia, Vicia hirsuta, Fallopia convolvulus, Echinochloa crus-galli) were selected as research objects. The algorithm demonstrated \"crop-weed\" segregation efficiency with an overall classification accuracy on test data of 72.94%. High identification accuracy was revealed for wheat (MCC=0.994) due to its spectral stability and pronounced red edge features (680-730 nm). Reduced accuracy in intra-class weed separation is driven by species-specific anatomy: thick cuticles in Euphorbia virgata increase reflectance, complex structure in Vicia hirsuta causes spectral dispersion, and mesophyll similarity between Echinochloa crus-galli and grasses leads to optical overlap with crop features. Thus, the model enables reliable crop-weed differentiation, a key requirement for precision agriculture and targeted agrochemical application.","在建立植物组织形态解剖特性与机器学习算法识别效率之间关系的基础上，首次对机器学习算法（以随机森林为例）在哈萨克斯坦东北部农业生态系统中作物与杂草多类识别方面的局限性进行了综合评估。研究证实了在高光谱特征空间中表现为类别光谱重叠的主要生物学成因。数据采集使用VIS-NIR波段的高光谱传感器完成。光谱剖面分析基于主成分分析的方差分布评估及反射率曲线构建进行。提取数据的分类采用随机森林算法完成。选取不同物候期的春小麦（Triticum aestivum）作物及五种优势杂草物种（Euphorbia virgata、Descurainia sophia、Vicia hirsuta、Fallopia convolvulus、Echinochloa crus-galli）作为研究对象。该算法在测试数据上实现了“作物-杂草”分离，总体分类精度为72.94%。小麦因其光谱稳定性及显著的红边特征（680-730 nm）而表现出较高的识别精度（MCC=0.994）。杂草类内分离精度降低由物种特异性解剖结构所致：Euphorbia virgata的厚角质层增加了反射率，Vicia hirsuta的复杂结构导致光谱离散，而Echinochloa crus-galli与禾本科植物之间的叶肉相似性导致其与作物特征发生光谱重叠。因此，该模型能够实现可靠的作物-杂草区分，这是精准农业和靶向农化施用的关键要求。","Fundamental and Experimental Biology","2026-09-29T00:00:00Z",{"impact":46,"substance":18,"depth":255,"authority":46,"freshness":21,"relevant":22,"comment":331},"高光谱结合机器学习实现麦田作物与杂草识别，方法新颖、结论具体，对精准农业有参考价值，但属细分领域研究，影响范围有限。",[333],{"name":328,"url":325},[27,28,335,336,29],"小麦","杂草识别",{"code":338,"slug":339,"name":335,"path":340,"parent_code":341},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[343,344],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":60,"slug":266,"name":267,"path":268,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":346},{"candidates":347,"agricultural_evidence":47},[348,352,356,360,364,370],{"code":338,"score":273,"supported":47,"title":349,"abstract":350,"tags":351},[339],[335,339],[335],{"code":33,"score":273,"supported":47,"title":353,"abstract":354,"tags":355},[49],[28,49],[28],{"code":60,"score":46,"supported":47,"title":357,"abstract":358,"tags":359},[283],[283],[29],{"code":73,"score":54,"supported":47,"title":361,"abstract":362,"tags":363},[],[289,290],[27],{"code":365,"score":67,"supported":47,"title":366,"abstract":367,"tags":369},"H6",[],[368],"农业生态",[],{"code":371,"score":22,"supported":14,"title":372,"abstract":373,"tags":374},"F4",[],[],[336],[376,377],"高光谱 小麦 杂草 识别","随机森林 作物 杂草 分类","高光谱小麦杂草识别-3863","10.31489\u002F2026feb3\u002F137-151",{"doi":379,"openalex_id":381,"authors":382,"venue":328,"cited_by_count":104,"oa_url":9,"card":401,"direction":176,"ingested_from":111},"W7214771122",[383,386,389,392,395,398],{"name":384,"orcid":385},"Rimma Meyramovna Ualiyeva","https:\u002F\u002Forcid.org\u002F0000-0003-3551-5007",{"name":387,"orcid":388},"Mariya M. Kaverina","https:\u002F\u002Forcid.org\u002F0000-0002-3838-7656",{"name":390,"orcid":391},"Александра Викторовна Осипова","https:\u002F\u002Forcid.org\u002F0000-0002-3947-5252",{"name":393,"orcid":394},"E. Koppayev","https:\u002F\u002Forcid.org\u002F0009-0004-0476-9017",{"name":396,"orcid":397},"М. А. Чайзабекова","https:\u002F\u002Forcid.org\u002F0009-0004-0468-956X",{"name":399,"orcid":400},"Sayan Berikovich Zhangazin","https:\u002F\u002Forcid.org\u002F0000-0002-5813-8331",{"tldr":402,"method":403,"finding":404,"direction":109,"opportunity":405},"利用高光谱数据与随机森林识别哈萨克斯坦东北部麦田作物与杂草，并分析误判的生物学原因。","VIS-NIR高光谱传感器采集数据，PCA分析光谱，随机森林分类春小麦与五种杂草","作物-杂草总体分类精度72.94%，小麦识别精度高（MCC=0.994），杂草种间分离受解剖结构影响","可探索融合形态解剖特征与深度学习，提升杂草种间分类精度，服务精准施药。","2026-09-30T23:30:54.952093Z",{"id":408,"title":409,"url":410,"summary":411,"summary_zh":412,"content":9,"source_name":413,"source_url":410,"published_at":414,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":415,"score_detail":416,"sources":418,"tags":420,"primary_topic":423,"secondary_topics":427,"classification":430,"search_phrases":477,"slug":480,"view_count":481,"doi":482,"paper":483,"created_at":511},3494,"Rice Cropping Pattern Mapping Using GEE-Based Sentinel-1\u002F2 Time-Series Imagery and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193302","Accurate mapping of rice cropping patterns is fundamental to sustainable agricultural management and regional food security assessment. In this study, we developed a high-precision mapping framework for major rice cropping patterns in the Yangtze River Delta region, China, using the Google Earth Engine (GEE) cloud platform. A multi-temporal and multi-source feature set was constructed by integrating Sentinel-1 radar backscatter (VV\u002FVH polarization), Sentinel-2 optical indices, including the normalized difference vegetation index (NDVI) and land surface water index (LSWI), and topographic factors (DEM and slope), to classify three predominant cropping systems, namely, wheat–rice rotation, double rice, and rapeseed–rice rotation. A total of 548 field-surveyed sample points collected during the 2024–2025 growing season were used for model training and validation. Three classical classifiers—Random Forest (RF), A Gradient Boosting Tree (GBTREE), and a Support Vector Machine (SVM)—were systematically compared. The ablation experiment demonstrated that the fusion of Sentinel-1 and Sentinel-2 outperformed both the Sentinel-1-only and Sentinel-2-only configurations across all three classifiers. Among them, the GBTREE achieved the highest overall accuracy (93.8%), Kappa coefficient (0.87), and macro-average F1 score (88.3%) in this specific experiment. Notably, it also performed best on the more challenging double-rice class. The SHapley Additive exPlanations (SHAP)-based feature importance analysis revealed that multi-temporal NDVI phenological features were the primary drivers of classification accuracy, while radar backscatter and water indices provided essential complementary information, and topographic factors served as spatial constraints at the regional scale. The spatial distribution derived from the GBTREE classification exhibited clear patterns: wheat–rice rotation dominated the northern plains (northern Jiangsu, northern Anhui, and the Hangjiahu Plain); double rice was concentrated in the southern Zhejiang hills and scattered valley plains; and rapeseed–rice rotation showed a scattered, mosaic distribution. Overall, this study demonstrates that integrating multi-source remote sensing data on the GEE platform with the GBTREE classifier enables effective and scalable high-precision mapping of rice cropping patterns in complex agricultural landscapes. This approach provides a reliable technical foundation for regional agricultural structure analysis, crop rotation assessment, and sustainable agricultural monitoring.","准确绘制水稻种植模式图是实现可持续农业管理和区域粮食安全评估的基础。本研究基于Google Earth Engine（GEE）云平台，构建了长江三角洲地区主要水稻种植模式的高精度制图框架。通过整合Sentinel-1雷达后向散射（VV\u002FVH极化）、Sentinel-2光学指数（包括归一化差异植被指数NDVI和地表水体指数LSWI）以及地形因子（DEM和坡度），构建了多时相、多源特征集，用于分类三种主要种植制度，即麦–稻轮作、双季稻和油–稻轮作。利用2024—2025年生长季采集的548个实地调查样点进行模型训练与验证。系统比较了三种经典分类器——随机森林（RF）、梯度提升树（GBTREE）和支持向量机（SVM）。消融实验表明，Sentinel-1与Sentinel-2的融合在三种分类器中均优于仅使用Sentinel-1或仅使用Sentinel-2的配置。其中，GBTREE在本实验中取得了最高的总体精度（93.8%）、Kappa系数（0.87）和宏平均F1分数（88.3%）。值得注意的是，其在更具挑战性的双季稻类别上同样表现最佳。基于SHapley加法解释（SHAP）的特征重要性分析表明，多时相NDVI物候特征是分类精度的主要驱动因素，雷达后向散射和水体指数提供了必要的补充信息，而地形因子则在区域尺度上起到空间约束作用。基于GBTREE分类得到的空间分布呈现出清晰的格局：麦–稻轮作主导北部平原（苏北、皖北和杭嘉湖平原）；双季稻集中于浙南丘陵和零散的河谷平原；油–稻轮作则呈零散镶嵌状分布。总体而言，本研究表明，在GEE平台上整合多源遥感数据与GBTREE分类器，能够对复杂农业景观中的水稻种植模式进行有效且可扩展的高精度制图。该方法为区域农业结构分析和作物","Remote Sensing","2026-09-24T00:00:00Z",79,{"impact":19,"substance":126,"depth":125,"authority":127,"freshness":21,"relevant":22,"comment":417},"基于GEE与Sentinel-1\u002F2时序影像结合机器学习实现长三角水稻种植模式高精度制图，方法扎实、结论可靠，对农业遥感监测有参考价值。",[419],{"name":413,"url":410},[27,421,28,29,422],"水稻","作物分类",{"code":424,"slug":425,"name":421,"path":426,"parent_code":341},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice",[428,429],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":60,"slug":266,"name":267,"path":268,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":431},{"candidates":432,"agricultural_evidence":47},[433,437,441,445,452,457,461,467,473],{"code":424,"score":273,"supported":47,"title":434,"abstract":435,"tags":436},[425],[421,425],[421],{"code":33,"score":21,"supported":47,"title":438,"abstract":439,"tags":440},[49],[],[28],{"code":60,"score":17,"supported":47,"title":442,"abstract":443,"tags":444},[],[29,63],[422,29],{"code":446,"score":114,"supported":47,"title":447,"abstract":448,"tags":451},"A7",[],[449,450],"轮作","crop rotation",[],{"code":213,"score":114,"supported":47,"title":453,"abstract":454,"tags":456},[],[216,455],"food security",[],{"code":338,"score":67,"supported":47,"title":458,"abstract":459,"tags":460},[],[339],[],{"code":462,"score":67,"supported":47,"title":463,"abstract":464,"tags":466},"A4",[],[465],"rapeseed",[],{"code":468,"score":67,"supported":47,"title":469,"abstract":470,"tags":472},"H8",[],[471],"可持续农业",[],{"code":73,"score":22,"supported":14,"title":474,"abstract":475,"tags":476},[],[],[27],[478,479],"长三角 水稻 种植模式 遥感","Sentinel-1 Sentinel-2 水稻制图","长三角水稻种植模式遥感-3494",5,"10.3390\u002Frs18193302",{"doi":482,"openalex_id":484,"authors":485,"venue":413,"cited_by_count":104,"oa_url":410,"card":506,"direction":109,"ingested_from":111},"W7214147197",[486,489,492,495,497,500,503],{"name":487,"orcid":488},"Xuan Li","https:\u002F\u002Forcid.org\u002F0000-0001-5509-2385",{"name":490,"orcid":491},"Lintao Chen","https:\u002F\u002Forcid.org\u002F0009-0000-6558-1289",{"name":493,"orcid":494},"Lin Chen","https:\u002F\u002Forcid.org\u002F0000-0002-9270-1626",{"name":496,"orcid":9},"Chao Su",{"name":498,"orcid":499},"Hoi Leong Lee","https:\u002F\u002Forcid.org\u002F0000-0002-4984-2183",{"name":501,"orcid":502},"Ruci Wang","https:\u002F\u002Forcid.org\u002F0000-0001-7049-7006",{"name":504,"orcid":505},"Xuguang Tang","https:\u002F\u002Forcid.org\u002F0009-0008-3494-8867",{"tldr":507,"method":508,"finding":509,"direction":109,"opportunity":510},"基于GEE融合Sentinel-1\u002F2时序与地形特征，用机器学习高精度制图长三角水稻种植模式。","GEE平台、Sentinel-1\u002F2时序特征、DEM、548个实地样本、RF\u002FG","GBTREE精度最高（总体93.8%、Kappa 0.87），双季稻识别最好；NDVI物候特征贡献最","可探索样本稀缺区迁移学习与多作物轮作模式泛化制图，并耦合产量与碳核算。","2026-09-25T23:30:30.511522Z",{"id":513,"title":514,"url":515,"summary":516,"summary_zh":517,"content":9,"source_name":518,"source_url":515,"published_at":519,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":520,"score_detail":521,"sources":523,"tags":525,"primary_topic":526,"secondary_topics":527,"classification":533,"search_phrases":570,"slug":573,"view_count":158,"doi":574,"paper":575,"created_at":595},2659,"Application of Remote Sensing and Machine Learning in Sustainable Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18189472","Global agriculture is undergoing a period of profound transformation, driven by the need to increase food production in a context characterized by climate change, the degradation of natural resources and increasing pressure on agricultural ecosystems [...]","全球农业正经历一场深刻变革，其驱动力是在气候变化、自然资源退化以及农业生态系统压力日益增大的背景下提高粮食产量的需求。[...]","Sustainability","2026-09-16T00:00:00Z",77,{"impact":125,"substance":18,"depth":255,"authority":20,"freshness":21,"relevant":22,"comment":522},"发表于核心期刊的遥感与机器学习综述，方法视角新颖、时效性强，对智慧农业技术路线有参考价值，值得进入每日精选。",[524],{"name":518,"url":515},[27,132,28,471,29],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[528,532],{"code":468,"slug":529,"name":530,"path":531,"parent_code":141},"sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"code":60,"slug":266,"name":267,"path":268,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":534},{"candidates":535,"agricultural_evidence":47},[536,540,545,549,555,562,566],{"code":33,"score":13,"supported":47,"title":537,"abstract":538,"tags":539},[49],[],[132,28],{"code":468,"score":21,"supported":47,"title":541,"abstract":543,"tags":544},[542],"sustainable 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