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на приложимостта на вегетационни индекси за диагностика на азотния статус при тютюн","https:\u002F\u002Fdoi.org\u002F10.61308\u002Fpxjs7160","This study evaluates the applicability of seven vegetation indices (CI Green, CI Red, CI Red Edge, LCI, DCNI, NDCI, and CNDVI) for diagnosing nitrogen status in oriental and Virginia tobacco. Leaf level spectral measurements obtained with a CI-710s SpectraVue spectrometer were compared with laboratory determined total nitrogen content.In the oriental tobacco cultivar Krumovgrad 58, all indices exhibited strong positive correlations with leaf nitrogen concentration (r = 0.772-0.965), with LCI, CI Green, and CI Red Edge showing the highest sensitivity. Similar results were observed in the Virginia 842 cultivar (r = 0.797-0.916), where four indices (CI Green, CI Red Edge, CNDVI, and NDCI) demonstrated statistically significant relationships.Linear regression models developed for both cultivars showed high predictive performance (R2 = 0.596-0.931 for oriental tobacco and R2 = 0.635-0.839 for Virginia tobacco), confirming the potential of vegetation indices for indirect nitrogen estimation. The differences between the cultivars of the two tobacco types can be explained by specific features of leaf morphology and mesophyll structure, which influence the spectral signal.The results indicate that spectral vegetation indices are a reliable tool for monitoring nitrogen nutrition in tobacco. Further calibration and expanded studies across cultivars, growth stages, and environmental conditions are required to enhance model robustness and practical applicability.","本研究评估了七种植被指数（CI Green、CI Red、CI Red Edge、LCI、DCNI、NDCI和CNDVI）在诊断东方型烟草和弗吉尼亚型烟草氮素状况方面的适用性。利用CI-710s SpectraVue光谱仪获取的叶片尺度光谱测量数据与实验室测定的全氮含量进行了比较。在东方型烟草品种Krumovgrad 58中，所有植被指数均与叶片氮浓度呈强正相关（r = 0.772–0.965），其中LCI、CI Green和CI Red Edge表现出最高的敏感性。在弗吉尼亚型品种Virginia 842中观察到了类似的结果（r = 0.797–0.916），其中四个植被指数（CI Green、CI Red Edge、CNDVI和NDCI）表现出统计学显著的关系。为两个品种建立的线性回归模型显示出较高的预测性能（东方型烟草R² = 0.596–0.931，弗吉尼亚型烟草R² = 0.635–0.839），证实了植被指数在间接估算氮素方面的潜力。两种烟草类型品种间的差异可归因于叶片形态和叶肉结构的特异性，这些特性影响了光谱信号。结果表明，光谱植被指数是监测烟草氮素营养的可靠工具。仍需进一步校准并在品种、生育期和环境条件方面开展扩展研究，以增强模型的稳健性和实际适用性。","Bulgarian Journal of Soil Science Agrochemistry and Ecology","2026-09-28T00:00:00Z","论文",false,66,{"impact":173,"substance":508,"depth":584,"authority":585,"freshness":586,"relevant":455,"comment":587},16,13,9,"该研究验证了七种植被指数在烟草氮素诊断中的适用性，方法扎实、数据可靠，对智慧农业遥感监测有参考价值，但属细分作物基础研究，影响范围有限。",[589],{"name":578,"url":575},[591,592,593,594,595],"智慧农业","遥感","植被指数","烟草种植","氮素诊断",{"code":248,"slug":249,"name":250,"path":251,"parent_code":200},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":601},"classified","rules",{"candidates":602,"agricultural_evidence":605},[603,611,615],{"code":248,"score":604,"supported":605,"title":606,"abstract":607,"tags":610},6,true,[],[608,609],"烟草","tobacco",[],{"code":32,"score":69,"supported":581,"title":612,"abstract":613,"tags":614},[],[],[593,592],{"code":16,"score":455,"supported":581,"title":616,"abstract":617,"tags":618},[],[],[591],[620,621],"烟草 氮素 植被指数","Krumovgrad 58 烟草","烟草氮素植被指数-3850",3,"10.61308\u002Fpxjs7160",{"doi":624,"openalex_id":626,"authors":627,"venue":578,"cited_by_count":424,"oa_url":632,"card":633,"direction":637,"ingested_from":639},"W7214629574",[628,630],{"name":629,"orcid":11},"Радка Божинова",{"name":631,"orcid":11},"Yovko Dyulgerski","https:\u002F\u002Fagriacad.eu\u002Fojs\u002Findex.php\u002Fbjssae\u002Farticle\u002Fdownload\u002F6863\u002F5937",{"tldr":634,"method":635,"finding":636,"direction":637,"opportunity":638},"评估七种植被指数诊断两种烟草氮素状况的适用性并建立回归模型。","CI-710s光谱仪叶片光谱与实验室全氮含量对比，线性回归建模。","多指数与叶片氮浓度强相关，LCI、CI Green、CI Red Edge最敏感，模型R²达0.93","农业遥感与作物表型","可扩展至冠层尺度、多品种多生育期及环境条件，提升模型鲁棒性与田间实用性。","openalex","2026-09-30T23:30:31.090531Z",{"id":642,"title":643,"url":644,"summary":645,"summary_zh":646,"content":11,"source_name":647,"source_url":644,"published_at":648,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":93,"score_detail":649,"sources":651,"tags":653,"primary_topic":657,"secondary_topics":658,"classification":660,"search_phrases":678,"slug":681,"view_count":623,"doi":682,"paper":683,"created_at":693},3838,"Forecasting Cotton Productivity in the Mil-Mugan Economic Region using Climate Variables and Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.52171\u002Fherald.342","Satellite imagery is considered as one of the most valuable and functional data sources for evaluating and forecasting production in agricultural systems. This study aims to forecast productivity in cotton fields within the Mil-Mugan economic region by incorporating vegetation indices and climatic variables. NDVI indicators were derived from Sentinel-2 data, temperature (T) was obtained from ERA5-Land data, and precipitation (R) was collected from the CHIRPS database. The data were analyzed using Google Earth Engine, ArcGIS Pro, and Python environments, consolidating them into an integrated temporal-spatial dimension. The correlation and regression analysis assessed the link among NDVI, temperature, precipitation, and productivity, leading to the development of a multivariate model. The findings derived from the model indicate that NDVI is the primary factor influencing productivity. The model developed for one region was implemented in the other three locations, and the correlation between the actual and predicted indicators was assessed using R² and RMSE metrics. The results indicate that the model's forecast accuracy fluctuates within the 90% range, considered appropriate for predicting regional cotton yield. The study's findings are significant for enhancing operational monitoring and planning of agrotechnical decisions with satellite data.","卫星影像被认为是评估和预测农业系统产量最有价值且最实用的数据来源之一。本研究旨在通过整合植被指数与气候变量，预测米尔-穆甘经济区棉田的生产力。NDVI指标源自Sentinel-2数据，温度（T）取自ERA5-Land数据，降水（R）来自CHIRPS数据库。数据在Google Earth Engine、ArcGIS Pro和Python环境中进行分析，并将其整合为统一的时空维度。通过相关性与回归分析评估了NDVI、温度、降水与生产力之间的关系，进而构建了多变量模型。模型结果表明，NDVI是影响生产力的主要因素。将为某一区域开发的模型应用于其他三个地点，并使用R²和RMSE指标评估实际值与预测值之间的相关性。结果表明，模型的预测精度在90%范围内波动，被认为适用于预测区域棉花产量。本研究结果对于利用卫星数据加强农艺决策的业务化监测与规划具有重要意义。","Herald of the Azerbaijan Engineering Academy","2026-09-29T00:00:00Z",{"impact":173,"substance":508,"depth":584,"authority":385,"freshness":586,"relevant":455,"comment":650},"基于Sentinel-2与气候变量的棉花产量预测模型，方法扎实、精度达90%，但属区域案例研究，公共影响有限。",[652],{"name":647,"url":644},[591,654,655,592,656],"产量预测","棉花","NDVI",{"code":248,"slug":249,"name":250,"path":251,"parent_code":200},[659],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":661},{"candidates":662,"agricultural_evidence":605},[663,669,674],{"code":248,"score":664,"supported":605,"title":665,"abstract":667,"tags":668},15,[666],"cotton",[655,666],[655],{"code":32,"score":524,"supported":605,"title":670,"abstract":672,"tags":673},[671],"remote sensing",[],[656,654,592],{"code":16,"score":455,"supported":581,"title":675,"abstract":676,"tags":677},[],[],[591],[679,680],"Mil-Mugan 棉花 遥感","Sentinel-2 NDVI 棉花产量","Mil-Mugan棉花遥感-3838","10.52171\u002Fherald.342",{"doi":682,"openalex_id":684,"authors":685,"venue":647,"cited_by_count":424,"oa_url":644,"card":688,"direction":637,"ingested_from":639},"W7214748696",[686],{"name":687,"orcid":11},"B.H. Babashli",{"tldr":689,"method":690,"finding":691,"direction":637,"opportunity":692},"结合Sentinel-2 NDVI与气候变量，构建多元模型预测米尔-穆甘地区棉花产量。","Sentinel-2 NDVI、ERA5-Land温度、CHIRPS降水，GEE","NDVI是产量主控因子，模型跨区预测精度约90%，可用于区域估产。","可引入多时相NDVI与机器学习，提升跨区域迁移性和极端气候下的估产鲁棒性。","2026-09-30T23:30:27.786044Z",{"id":695,"title":696,"url":697,"summary":698,"summary_zh":699,"content":11,"source_name":700,"source_url":697,"published_at":701,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":702,"score_detail":703,"sources":705,"tags":707,"primary_topic":710,"secondary_topics":711,"classification":713,"search_phrases":728,"slug":731,"view_count":408,"doi":732,"paper":733,"created_at":749},3179,"Multi-features and Fuzzy with Residual Lenet for Cotton Classification Using Remote Sensing Image","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02541-8","Multi-features and Fuzzy with Residual Lenet for Cotton Classification Using Remote Sensing Image。Journal of the Indian Society of Remote Sensing","多特征与模糊残差LeNet用于基于遥感图像的棉花分类。《印度遥感学会杂志》","Journal of the Indian Society of Remote Sensing","2026-09-21T00:00:00Z",58,{"impact":173,"substance":345,"depth":664,"authority":416,"freshness":586,"relevant":455,"comment":704},"方法类论文，提出多特征与模糊结合残差LeNet用于遥感棉花分类，专业价值尚可但影响面窄，适合作为技术参考而非每日精选头条。",[706],{"name":700,"url":697},[708,655,592,709],"农业人工智能","作物分类",{"code":248,"slug":249,"name":250,"path":251,"parent_code":200},[712],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":714},{"candidates":715,"agricultural_evidence":605},[716,720,724],{"code":32,"score":584,"supported":605,"title":717,"abstract":718,"tags":719},[671],[592,671],[709,592],{"code":248,"score":664,"supported":605,"title":721,"abstract":722,"tags":723},[666],[655,666],[655],{"code":24,"score":455,"supported":581,"title":725,"abstract":726,"tags":727},[],[],[708],[729,730],"Residual LeNet 棉花 遥感分类","农业人工智能 作物分类 棉花 遥感","ResidualLeNet棉花遥感分类-3179","10.1007\u002Fs12524-026-02541-8",{"doi":732,"openalex_id":734,"authors":735,"venue":700,"cited_by_count":424,"oa_url":11,"card":744,"direction":637,"ingested_from":639},"W7213944040",[736,738,740,742],{"name":737,"orcid":11},"G. Suresh",{"name":739,"orcid":11},"G. Bhuvaneswari",{"name":741,"orcid":11},"G. Manikandan",{"name":743,"orcid":11},"M. Robinson Joel",{"tldr":745,"method":746,"finding":747,"direction":637,"opportunity":748},"提出多特征与模糊结合残差LeNet的遥感影像棉花分类方法。","多特征提取、模糊逻辑与残差LeNet网络，用于遥感图像分类。","该方法能有效提升棉花分类精度，验证了深度残差网络结合模糊特征的可行性。","可探索多特征与模糊深度学习在其他作物遥感分类中的泛化能力及轻量化部署。","2026-09-22T23:30:23.811658Z",{"id":751,"title":752,"url":753,"summary":754,"summary_zh":755,"content":11,"source_name":756,"source_url":753,"published_at":757,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":758,"score_detail":759,"sources":762,"tags":764,"primary_topic":768,"secondary_topics":769,"classification":770,"search_phrases":814,"slug":817,"view_count":455,"doi":818,"paper":819,"created_at":834},2180,"Crop Yield Estimation with MODIS Derived Normalized Difference Vegetation Index and Comparative Study on Crop Yield Prediction Among Linear Regression, Random Forest and Gradient Boosting as Well as CatBoost","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183107","This paper presents the design, development, and evaluation of a machine-learning system built to forecast agricultural crop yields across Indian states between 2000 and 2026, together with a complementary, national-scale verification of predicted crop yield using a MODIS-derived NDVI time series (MOD13A3.061 Vegetation Indices Monthly L3 Global 1 km SIN Grid). Although many prior studies address crop-yield prediction with linear regression, random forest, gradient boosting, and related methods, a complementary, aggregate-level verification method for predicted crop yield has rarely been proposed. This article contributes such a method, together with a complementary NDVI-based estimation approach for total foodgrain output. Crop yield and MODIS-derived NDVI are strongly correlated (r = 0.84 for annual maximum NDVI; r = 0.78 for annual mean NDVI), and a simple regression of total foodgrains on annual maximum NDVI alone reaches R2 = 0.70. Four modeling approaches—linear regression, random forest, gradient boosting, and CatBoost—were built and compared using a chronology-preserving, expanding-window walk-forward validation procedure with a final, untouched 2024–2026 holdout, rather than a random split; a companion leakage check confirmed that reported production is almost algebraically identical to reported yield and therefore had to be excluded from the feature set. Random forest produced the most reliable and consistent forecasts, reaching a mean absolute percentage error (MAPE) of 11.4% and R2 = 0.982 on the final holdout, ahead of CatBoost (MAPE = 11.6%, R2 = 0.969) and gradient boosting (MAPE = 13.0%, R2 = 0.908), and substantially ahead of linear regression, which failed to generalize to the holdout period (R2 = −10.67); across the walk-forward folds preceding this holdout, however, the three tree ensembles were statistically indistinguishable. A four-configuration ablation study confirms that most of this performance gain is attributable to the inclusion of MODIS-derived NDVI rather than to model choice alone. Prediction error varies considerably by crop, from under 10% MAPE for major staples (rice, wheat, maize, sugarcane, moong) to well over 80% MAPE for several lower-volume crops (soyabean, garlic, Sunn hemp, tobacco, potato). The paper also documents two consequential data-quality findings—a near-perfect algebraic relationship between production and yield, and a structural administrative reporting gap in 2020—and closes with directions for future work, including higher-resolution satellite inputs, temporal deep-learning architectures, additional environmental covariates, and explainable-AI analysis of feature contributions.","本文介绍了一个机器学习系统的设计、开发与评估，该系统用于预测2000年至2026年间印度各邦的农作物产量，并辅以一项基于MODIS衍生的NDVI时间序列（MOD13A3.061植被指数月度L3全球1 km SIN网格）在全国尺度上对预测作物产量的验证。尽管此前已有许多研究采用线性回归、随机森林、梯度提升及相关方法进行作物产量预测，但针对预测作物产量的补充性、聚合层面的验证方法却鲜有提出。本文提出了这样一种方法，并辅以一种基于NDVI的粮食总产量估算方法。作物产量与MODIS衍生的NDVI高度相关（年最大NDVI的r = 0.84；年均NDVI的r = 0.78），仅以年最大NDVI对粮食总产量进行简单回归即可达到R² = 0.70。本文构建了四种建模方法——线性回归、随机森林、梯度提升和CatBoost——并采用保持时间顺序的扩展窗口前向验证程序进行比较，最终以2024—2026年作为未触碰的留出集，而非随机划分；一项配套的泄漏检查证实，报告产量与报告单产在代数上几乎完全相同，因此必须将其从特征集中排除。随机森林产生了最可靠且一致的预测，在最终留出集上达到平均绝对百分比误差（MAPE）为11.4%、R² = 0.982，优于CatBoost（MAPE = 11.6%，R² = 0.969）和梯度提升（MAPE = 13.0%，R² = 0.908），并大幅优于线性回归，后者未能泛化至留出期（R² = −10.67）；然而在此留出集之前的各前向验证折中，三种树集成方法在统计上无法区分。一项四配置消融研究证实，大部分性能提升归因于纳入MODIS衍生的NDVI，而非仅归因于模型选择。预测误差因作物而异，主要粮食作物（水稻、小麦、玉米、甘蔗、绿豆）的MAPE低于10%，而若干低产量作物（大豆、大蒜、菽麻、烟草、马铃薯）的MAPE则远超80%。本文还记录了两项重要的数据质量发现——产量与单产之间近乎完美的代数关系，以及2020年结构性的行政报告缺口——并在结尾提出了未来研究方向。","Remote Sensing","2026-09-10T00:00:00Z",81,{"impact":361,"substance":760,"depth":361,"authority":345,"freshness":173,"relevant":455,"comment":761},23,"基于MODIS NDVI与多种机器学习模型的作物产量预测研究，方法严谨、结论可靠，对农业遥感估产有实质参考价值。",[763],{"name":756,"url":753},[708,765,766,592,767],"粮食安全","机器学习","作物估产",{"code":248,"slug":249,"name":250,"path":251,"parent_code":200},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":771},{"candidates":772,"agricultural_evidence":605},[773,779,783,787,791,797,801,806,810],{"code":248,"score":586,"supported":605,"title":774,"abstract":775,"tags":778},[],[776,608,777,609],"甘蔗","sugarcane",[],{"code":208,"score":604,"supported":605,"title":780,"abstract":781,"tags":782},[],[210,209],[],{"code":216,"score":604,"supported":605,"title":784,"abstract":785,"tags":786},[],[218,217],[],{"code":224,"score":604,"supported":605,"title":788,"abstract":789,"tags":790},[],[226,225],[],{"code":240,"score":604,"supported":605,"title":792,"abstract":793,"tags":796},[],[794,795],"马铃薯","potato",[],{"code":24,"score":408,"supported":605,"title":798,"abstract":799,"tags":800},[],[766],[708,766],{"code":232,"score":623,"supported":605,"title":802,"abstract":803,"tags":805},[],[804],"大豆",[],{"code":32,"score":69,"supported":581,"title":807,"abstract":808,"tags":809},[],[],[767,592],{"code":263,"score":455,"supported":581,"title":811,"abstract":812,"tags":813},[],[],[765],[815,816],"农业人工智能 作物估产 机器学习 粮食安全","农业人工智能 作物估产","农业人工智能作物估产机器学习粮食安全-2180","10.3390\u002Frs18183107",{"doi":818,"openalex_id":820,"authors":821,"venue":756,"cited_by_count":424,"oa_url":753,"card":828,"direction":833,"ingested_from":639},"W7212191753",[822,825],{"name":823,"orcid":824},"Kohei Arai","https:\u002F\u002Forcid.org\u002F0009-0001-6433-1592",{"name":826,"orcid":827},"Sara Sanwal","https:\u002F\u002Forcid.org\u002F0009-0003-7944-0948",{"tldr":829,"method":830,"finding":831,"direction":637,"opportunity":832},"用MODIS NDVI与四种回归模型预测印度各邦作物产量，并做全国尺度验证。","MODIS NDVI时序、线性回归、随机森林、梯度提升、CatBoost及前向验","随机森林最优（MAPE 11.4%），NDVI贡献主要增益，主粮误差低而小作物误差高。","可探索多源遥感与深度模型融合，并针对小作物和行政数据缺口改进预测。","农业人工智能与决策模型","2026-09-11T23:30:52.943993Z",{"id":836,"title":837,"url":838,"summary":839,"summary_zh":11,"content":11,"source_name":840,"source_url":838,"published_at":841,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":842,"score_detail":843,"sources":845,"tags":847,"primary_topic":849,"secondary_topics":850,"classification":851,"search_phrases":870,"slug":873,"view_count":623,"doi":874,"paper":875,"created_at":903},1208,"Restoration of occlusion in residual film images and curling degree prediction based on UAV in pre-sowing cotton fields","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112335","Restoration of occlusion in residual film images and curling degree prediction based on UAV in pre-sowing cotton fields。Computers and Electronics in Agriculture","Computers and Electronics in Agriculture","2026-08-25T00:00:00Z",62,{"impact":416,"substance":361,"depth":584,"authority":345,"freshness":69,"relevant":455,"comment":844},"基于无人机影像的残膜污染检测与卷曲度预测研究，方法新颖，对农业环保有实际价值，但时效性较低。",[846],{"name":840,"url":838},[591,708,655,592,848],"残膜污染",{"code":248,"slug":249,"name":250,"path":251,"parent_code":200},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":852},{"candidates":853,"agricultural_evidence":605},[854,858,862,866],{"code":248,"score":416,"supported":605,"title":855,"abstract":856,"tags":857},[666],[666],[655],{"code":16,"score":455,"supported":581,"title":859,"abstract":860,"tags":861},[],[],[591],{"code":24,"score":455,"supported":581,"title":863,"abstract":864,"tags":865},[],[],[708],{"code":32,"score":455,"supported":581,"title":867,"abstract":868,"tags":869},[],[],[592],[871,872],"农业人工智能 智慧农业 残膜污染 棉花","农业人工智能 智慧农业","农业人工智能智慧农业残膜污染棉花-1208","10.1016\u002Fj.compag.2026.112335",{"doi":874,"openalex_id":876,"authors":877,"venue":840,"cited_by_count":424,"oa_url":11,"card":898,"direction":637,"ingested_from":639},"W7204210197",[878,881,883,886,889,892,895],{"name":879,"orcid":880},"Zhiqiang Zhai","https:\u002F\u002Forcid.org\u002F0000-0002-5242-9779",{"name":882,"orcid":11},"Lupeng Miao",{"name":884,"orcid":885},"Huting Wang","https:\u002F\u002Forcid.org\u002F0009-0008-3605-2685",{"name":887,"orcid":888},"Jiangbo Li","https:\u002F\u002Forcid.org\u002F0000-0001-7856-9131",{"name":890,"orcid":891},"Yue Chen","https:\u002F\u002Forcid.org\u002F0009-0004-5110-6400",{"name":893,"orcid":894},"Songxin Ye","https:\u002F\u002Forcid.org\u002F0000-0002-8895-8525",{"name":896,"orcid":897},"Ruoyu Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0370-3249",{"tldr":899,"method":900,"finding":901,"direction":637,"opportunity":902},"用无人机图像修复残膜遮挡并预测卷曲程度，评估棉田残膜污染。","无人机图像、遮挡修复、卷曲度预测模型。","成功修复遮挡并预测卷曲度，支持残膜污染评估。","可扩展至不同作物、不同残膜类型，结合深度学习提升修复精度和预测鲁棒性。","2026-09-01T04:03:02.336224Z",{"technologies":905,"keywords":927},[906,907,908,910,912,914,916,918,919,921,923,925],{"name":708,"count":361},{"name":592,"count":408},{"name":909,"count":400},"深度学习",{"name":911,"count":623},"农业遥感",{"name":913,"count":623},"精准农业",{"name":915,"count":69},"分子育种",{"name":917,"count":69},"无人机遥感",{"name":766,"count":69},{"name":920,"count":69},"目标检测",{"name":922,"count":455},"GIS",{"name":924,"count":455},"物联网",{"name":926,"count":455},"计算机视觉",[928,930,931,932,933,935,937,938,939,940,942,944],{"name":591,"count":929},19,{"name":655,"count":586},{"name":776,"count":400},{"name":656,"count":69},{"name":934,"count":69},"甘蔗育种",{"name":936,"count":69},"轻量化模型",{"name":654,"count":455},{"name":767,"count":455},{"name":709,"count":455},{"name":941,"count":455},"农业气象",{"name":943,"count":455},"农药减量",{"name":945,"count":455},"卫星影像"]