[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5400":3,"related-5400":124},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":94,"slug":97,"view_count":98,"doi":99,"paper":100,"created_at":123},5400,"Artificial intelligence in food safety: a bibliometric and empirical synthesis of emerging technologies and research frontiers","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fbfj-06-2025-0819","Purpose This study aimed to identify recent research trends in the integration of artificial intelligence (AI) with food safety and security through a comprehensive bibliometric analysis. Design\u002Fmethodology\u002Fapproach A total of 2,782 documents published between 2021 and 2025 were retrieved from the Web of Science Core Collection. The dataset was refined by removing duplicates, filtering document types and excluding irrelevant records. VOSviewer was then used to conduct co-authorship, keyword co-occurrence, citation, co-citation and bibliographic coupling analyses. Findings Machine learning and deep learning emerged as core technologies associated with food security, contamination detection and quality prediction. Keyword clustering demonstrated interdisciplinary connections with agriculture, climate change and public health. Citation and co-citation analyses identified three major intellectual foundations: traditional machine learning, computer vision-based deep learning and time-series modeling. China, India and the United States were the most influential contributors in terms of research output and technological development. Bibliographic coupling and source co-citation analyses further indicated increasing convergence between food science and computational science. Originality\u002Fvalue This study provides a comprehensive overview of the intellectual structure, collaborative networks and thematic evolution of AI-driven food safety research. Unlike previous studies focusing on specific technologies or applications, it maps global research patterns and interdisciplinary linkages. The findings provide strategic insights for researchers, policymakers and industry stakeholders. Future studies should prioritize explainable AI integration into regulatory frameworks and the development of larger, more diverse datasets to improve model generalizability across food systems.","目的 本研究旨在通过全面的文献计量分析，识别人工智能（AI）与食品安全及保障融合领域的最新研究趋势。设计与方法 从Web of Science核心合集中检索了2021年至2025年间发表的2，782篇文献。通过去除重复记录、筛选文献类型并排除不相关记录对数据集进行精炼。随后使用VOSviewer进行合著、关键词共现、引文、共被引及文献耦合分析。研究发现 机器学习和深度学习成为与食品安全、污染检测和质量预测相关的核心技术。关键词聚类显示了与农业、气候变化和公共卫生的跨学科联系。引文和共被引分析识别出三大主要知识基础：传统机器学习、基于计算机视觉的深度学习和时间序列建模。中国、印度和美国在研究产出和技术发展方面是最具影响力的贡献者。文献耦合和来源共被引分析进一步表明食品科学与计算科学之间的融合日益加深。原创性\u002F价值 本研究全面概述了AI驱动的食品安全研究的知识结构、合作网络和主题演变。与以往聚焦特定技术或应用的研究不同，本研究绘制了全球研究模式和跨学科联系。研究结果为研究人员、政策制定者和行业利益相关者提供了战略性见解。未来研究应优先关注可解释AI融入监管框架以及开发更大、更多样化的数据集，以提高模型在食品系统中的泛化能力。",null,"British Food Journal","2026-10-19T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"基于2782篇文献的AI食品安全计量分析，方法规范、结论有参考价值，但属综述性研究，产业落地影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","机器学习","食品安全","文献计量",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,62,68,74,82,89],{"code":32,"score":51,"supported":52,"title":53,"abstract":55,"tags":61},19,true,[54],"artificial intelligence",[56,28,57,58,59,54,60],"深度学习","计算机视觉","machine learning","deep learning","computer vision",[27,28],{"code":39,"score":63,"supported":52,"title":64,"abstract":66,"tags":67},15,[65],"food safety",[29,65],[29],{"code":69,"score":63,"supported":52,"title":70,"abstract":72,"tags":73},"I7",[71],"bibliometric",[30,71],[30],{"code":75,"score":76,"supported":52,"title":77,"abstract":78,"tags":81},"H7",6,[],[79,80],"气候变化","climate change",[],{"code":83,"score":84,"supported":52,"title":85,"abstract":86,"tags":88},"A8",3,[],[87],"food security",[],{"code":90,"score":21,"supported":14,"title":91,"abstract":92,"tags":93},"I1",[],[],[26],[95,96],"AI 食品安全 文献计量","机器学习 食品质量预测","AI食品安全文献计量-5400",0,"10.1108\u002Fbfj-06-2025-0819",{"doi":99,"openalex_id":101,"authors":102,"venue":10,"cited_by_count":98,"oa_url":9,"card":116,"direction":120,"ingested_from":122},"W7222920742",[103,105,107,110,113],{"name":104,"orcid":9},"Harang Lee",{"name":106,"orcid":9},"지은서",{"name":108,"orcid":109},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":111,"orcid":112},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":114,"orcid":115},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":117,"method":118,"finding":119,"direction":120,"opportunity":121},"对2021-2025年2782篇文献做文献计量，梳理AI与食品安全交叉研究趋势。","Web of Science文献计量，VOSviewer共被引、关键词共现与耦合","机器学习与深度学习是核心，形成传统ML、计算机视觉、时序建模三大知识基础。","农业人工智能与决策模型","可解释AI嵌入监管框架、构建更大更多样数据集以提升跨食物系统泛化能力。","openalex","2026-10-20T23:30:31.567802Z",{"total":76,"page":21,"page_size":76,"items":125},[126,209,273,328,392,482],{"id":127,"title":128,"url":129,"summary":130,"summary_zh":131,"content":9,"source_name":132,"source_url":129,"published_at":133,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":134,"score_detail":135,"sources":139,"tags":141,"primary_topic":144,"secondary_topics":145,"classification":146,"search_phrases":166,"slug":169,"view_count":98,"doi":170,"paper":171,"created_at":208},5306,"A growing challenge: stakeholder perceptions of AI and ML applications in U.S. agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10459-x","Abstract Context The global population is expected to exceed ten billion by 2050 and food demand is projected to increase by up to 100% in the same timeframe. Agricultural management must adapt to meet the increasing global demands. Artificial intelligence (AI) and machine learning (ML) technologies can help the agricultural community respond to these needs. Aims An online survey and follow-up interviews were conducted to examine patterns of use, perceptions, and barriers among early adopting stakeholders regarding AI and ML. Methods The online survey with agricultural stakeholders (n=129) included questions regarding the participants’ views on incorporating AI and ML techniques into farming practices and individual follow-up interviews with agricultural stakeholders (n=10) were conducted to better understand their concerns toward technology use and adoption of AI-enabled tools and technologies. Key results Using a mixed-methods approach, four challenges to establishing AI-\u002FML-enabled technology in agriculture were identified. Namely, challenges related to (1) systems of systems (SoS), (2) sub-seasonal to seasonal weather (S2S) forecasts, (3) precision soil management, and (4) policy and economics. Each challenge was discussed in the context of recent literature. Conclusion Concerns were voiced clustered around four broad research themes. This brief communication brings the perspective of agricultural stakeholders to the discussion of the potential of AI\u002FML-enabled technology and the direction of their development in the agricultural sector. Implications Data collected in 2021, establish an important baseline for the early phase of AI\u002FML use in U.S. agriculture, enabling comparison of future perceptions and developments in the sector. Impact This brief communication describes a survey and follow-up interviews conducted with agricultural stakeholders on factors influencing their willingness and reluctance to incorporate AI and ML techniques into farming practices. The findings, summarized and discussed as four key challenges for AI and ML in the agricultural sector, have the potential to streamline research activities.","摘要\n\n背景 预计到2050年全球人口将超过100亿，同期粮食需求预计将增加多达100%。农业管理必须作出调整以满足不断增长的全球需求。人工智能（AI）和机器学习（ML）技术可帮助农业界应对这些需求。\n\n目的 通过开展在线调查和后续访谈，考察早期采用相关技术的利益相关者在AI和ML方面的使用模式、认知及障碍。\n\n方法 针对农业利益相关者（n=129）的在线调查包含有关参与者对将AI和ML技术纳入农业实践看法的问题，并对农业利益相关者（n=10）进行个体后续访谈，以更好地了解他们对技术使用及采用AI赋能工具和技术的顾虑。\n\n主要结果 采用混合方法，识别出在农业中建立AI\u002FML赋能技术面临的四项挑战，即与以下方面相关的挑战：（1）系统之系统（SoS），（2）次季节至季节天气（S2S）预报，（3）精准土壤管理，以及（4）政策与经济。每项挑战均结合近期文献进行了讨论。\n\n结论 所表达的顾虑集中于四个广泛的研究主题。本简报将农业利益相关者的视角引入关于AI\u002FML赋能技术潜力及其在农业部门发展方向讨论中。\n\n意义 2021年收集的数据为美国农业中AI\u002FML应用的早期阶段建立了重要基线，有助于比较该部门未来的认知和发展。\n\n影响 本简报描述了针对农业利益相关者开展的一项调查和后续访谈，内容涉及影响其愿意和不愿将AI和ML技术纳入农业实践的因素。研究结果总结并讨论为农业部门AI和ML的四项关键挑战，有望优化研究活动。","Precision Agriculture","2026-10-18T00:00:00Z",77,{"impact":17,"substance":136,"depth":137,"authority":19,"freshness":20,"relevant":21,"comment":138},20,17,"基于129份问卷与10次访谈的混合方法研究，系统梳理美国农业AI\u002FML应用的四大挑战，为早期采用者认知提供基线数据，对智慧农业研究有参考价值。",[140],{"name":132,"url":129},[26,27,28,142,143],"精准农业","农户认知",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":147},{"candidates":148,"agricultural_evidence":52},[149,154,161],{"code":32,"score":150,"supported":52,"title":151,"abstract":152,"tags":153},11,[],[28,58,54],[27,28],{"code":155,"score":76,"supported":52,"title":156,"abstract":157,"tags":160},"H2",[],[158,159],"土壤","soil",[],{"code":90,"score":162,"supported":14,"title":163,"abstract":164,"tags":165},2,[],[],[26,142],[167,168],"美国农业 AI ML 调查","农业人工智能 应用障碍","美国农业AIML调查-5306","10.1007\u002Fs11119-026-10459-x",{"doi":170,"openalex_id":172,"authors":173,"venue":132,"cited_by_count":98,"oa_url":129,"card":203,"direction":120,"ingested_from":122},"W7222866367",[174,177,180,183,186,189,192,195,197,200],{"name":175,"orcid":176},"Jennifer Koch","https:\u002F\u002Forcid.org\u002F0000-0002-7067-2705",{"name":178,"orcid":179},"Andrew Fox","https:\u002F\u002Forcid.org\u002F0000-0001-6719-8902",{"name":181,"orcid":182},"Richa Bhattarai","https:\u002F\u002Forcid.org\u002F0000-0003-3827-7761",{"name":184,"orcid":185},"Hank C. Jenkins‐Smith","https:\u002F\u002Forcid.org\u002F0000-0002-5204-2726",{"name":187,"orcid":188},"Jonathan McFadden","https:\u002F\u002Forcid.org\u002F0000-0003-3762-1428",{"name":190,"orcid":191},"Audrey Reinert","https:\u002F\u002Forcid.org\u002F0000-0001-8951-702X",{"name":193,"orcid":194},"Juan Sesmero","https:\u002F\u002Forcid.org\u002F0000-0002-2669-4536",{"name":196,"orcid":9},"M. Z. Walkup",{"name":198,"orcid":199},"Wolfgang Jentner","https:\u002F\u002Forcid.org\u002F0000-0003-1045-6020",{"name":201,"orcid":202},"David S. Ebert","https:\u002F\u002Forcid.org\u002F0000-0001-6177-1296",{"tldr":204,"method":205,"finding":206,"direction":120,"opportunity":207},"通过问卷与访谈调查美国农业利益相关者对AI\u002FML应用的认知与障碍。","在线问卷129人加10人深度访谈的混合方法。","识别出系统之系统、次季节天气预报、精准土壤管理和政策经济四大挑战。","可针对四类障碍开展分区域、分作物类型的采纳意愿追踪与干预研究。","2026-10-19T23:30:02.345893Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":215,"source_url":212,"published_at":216,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":217,"score_detail":218,"sources":223,"tags":225,"primary_topic":228,"secondary_topics":233,"classification":235,"search_phrases":253,"slug":256,"view_count":98,"doi":257,"paper":258,"created_at":272},4759,"Machine Learning-based Prediction of SVI Parameter in Maize: A Comparative Study and Feature Importance Analysis","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i10.2870-2879.9043","Background: Accurate estimation of seedling vigor indices (SVI) is critical for monitoring plant health and predicting yields in precision agriculture. This study comparatively evaluates the performance of different machine learning algorithms in estimating the SVI parameter. Methods: Germination data were collected from 39 maize genotypes under three different salinity conditions (0 mM, 100 mM, and 200 mM NaCl), including the parameters Gmax, MGT, GI, SL, RL, SdL, SMR, RMR, and SVI. The dataset was preprocessed through normalization to the [0–1] range. The SVI parameter was estimated using four different machine learning methods: Random Forest, CatBoost, LightGBM, and Support Vector Regression (SVR). To evaluate the performance of the models, both 10-fold cross-validation and 80%–20% hold-out validation techniques were employed. The model performance was examined using RMSE, MSE, MAE, and R² metrics. Results: The findings show that all models have a high explanatory power in the training dataset. Specifically, the CatBoost model showed the highest success in the training phase (R²=0.9962), whereas the SVR model achieved the highest generalization performance on the test dataset (R²=0.9712, RMSE=0.0582). Feature importance analysis results revealed that the 'App' parameter had the highest impact on the target variable in most models, but different structural interactions were prominent in the LightGBM model. The results demonstrate that machine learning techniques provide high accuracy in SVI estimation and that the tendency towards overfitting is limited. Conclusions: In conclusion, this study confirms that machine learning techniques are powerful and reliable tools for estimating spectral indices in precision agriculture applications. The integration of SVR and ensemble-based models into agricultural decision support systems is expected to significantly contribute to optimizing resource use and improving yield predictions.","背景：准确估计幼苗活力指数（SVI）对于精准农业中监测植物健康和预测产量至关重要。本研究比较评估了不同机器学习算法在估计SVI参数方面的性能。方法：从39个玉米基因型在三种不同盐度条件（0 mM、100 mM和200 mM NaCl）下收集发芽数据，包括Gmax、MGT、GI、SL、RL、SdL、SMR、RMR和SVI等参数。数据集通过归一化至[0–1]范围进行预处理。采用四种不同的机器学习方法估计SVI参数：随机森林、CatBoost、LightGBM和支持向量回归（SVR）。为评估模型性能，采用了10折交叉验证和80%–20%留出验证技术。使用RMSE、MSE、MAE和R²指标检验模型性能。结果：研究结果表明，所有模型在训练数据集中均具有较高的解释力。具体而言，CatBoost模型在训练阶段表现出最高的成功率（R²=0.9962），而SVR模型在测试数据集上取得了最高的泛化性能（R²=0.9712，RMSE=0.0582）。特征重要性分析结果显示，在大多数模型中，“App”参数对目标变量的影响最大，但在LightGBM模型中不同的结构交互作用更为突出。结果表明，机器学习技术在SVI估计中提供了高精度，且过拟合倾向有限。结论：总之，本研究证实机器学习技术是精准农业应用中估计光谱指数的强大而可靠的工具。将SVR和基于集成的模型整合到农业决策支持系统中，预计将显著有助于优化资源利用和改善产量预测。","Turkish Journal of Agriculture - Food Science and Technology","2026-10-11T00:00:00Z",63,{"impact":20,"substance":17,"depth":219,"authority":220,"freshness":221,"relevant":21,"comment":222},16,12,9,"方法对比扎实、结论可靠，但属细分领域论文，产业影响有限，适合主题聚合而非头条精选。",[224],{"name":215,"url":212},[26,27,28,226,227],"玉米","耐盐育种",{"code":229,"slug":230,"name":226,"path":231,"parent_code":232},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[234],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":236},{"candidates":237,"agricultural_evidence":52},[238,243,247],{"code":32,"score":51,"supported":52,"title":239,"abstract":240,"tags":242},[58],[28,58,241],"decision support",[27,28],{"code":229,"score":63,"supported":52,"title":244,"abstract":245,"tags":246},[230],[226,230],[226],{"code":90,"score":248,"supported":52,"title":249,"abstract":250,"tags":252},7,[],[142,251],"precision agriculture",[26],[254,255],"玉米 SVI 机器学习","玉米 耐盐 幼苗活力指数","玉米SVI机器学习-4759","10.24925\u002Fturjaf.v14i10.2870-2879.9043",{"doi":257,"openalex_id":259,"authors":260,"venue":215,"cited_by_count":98,"oa_url":212,"card":267,"direction":120,"ingested_from":122},"W7221462188",[261,264],{"name":262,"orcid":263},"Mustafa Turan Arslan","https:\u002F\u002Forcid.org\u002F0000-0001-5498-6571",{"name":265,"orcid":266},"Gülay Zülkadir","https:\u002F\u002Forcid.org\u002F0000-0003-3488-4011",{"tldr":268,"method":269,"finding":270,"direction":120,"opportunity":271},"用四种机器学习模型基于39个玉米基因型盐胁迫发芽数据预测幼苗活力指数SVI。","39个玉米基因型在三种盐浓度下的发芽数据，归一化后用RF、CatBoost、Li","SVR泛化最优（R²=0.9712），CatBoost训练拟合最高；App参数对SVI影响最大。","可扩展到多作物多胁迫环境，结合光谱或图像表型数据提升SVI预测的田间适用性。","2026-10-12T23:30:44.762497Z",{"id":274,"title":275,"url":276,"summary":277,"summary_zh":9,"content":9,"source_name":278,"source_url":9,"published_at":279,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":280,"score_detail":281,"sources":283,"tags":285,"primary_topic":288,"secondary_topics":289,"classification":294,"search_phrases":315,"slug":318,"view_count":307,"doi":9,"paper":319,"created_at":327},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":17,"substance":18,"depth":17,"authority":19,"freshness":76,"relevant":21,"comment":282},"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。",[284],{"name":278,"url":276},[26,27,28,286,287],"遥感","土壤监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[290],{"code":155,"slug":159,"name":291,"path":292,"parent_code":293},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":45,"locked":14,"version":46,"method":47,"evidence":295},{"candidates":296,"agricultural_evidence":52},[297,301,305,311],{"code":32,"score":219,"supported":52,"title":298,"abstract":299,"tags":300},[54],[56,28],[27,28],{"code":155,"score":150,"supported":52,"title":302,"abstract":303,"tags":304},[159],[158],[],{"code":306,"score":307,"supported":52,"title":308,"abstract":309,"tags":310},"I3",4,[],[286],[286],{"code":90,"score":21,"supported":14,"title":312,"abstract":313,"tags":314},[],[],[26],[316,317],"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":320,"venue":9,"cited_by_count":98,"oa_url":9,"card":321,"direction":120,"ingested_from":326},[],{"tldr":322,"method":323,"finding":324,"direction":120,"opportunity":325},"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。","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":329,"title":330,"url":331,"summary":332,"summary_zh":333,"content":9,"source_name":334,"source_url":331,"published_at":279,"category":12,"cover_url":9,"hotness":335,"is_selected":14,"score":336,"score_detail":337,"sources":339,"tags":343,"primary_topic":345,"secondary_topics":346,"classification":347,"search_phrases":375,"slug":378,"view_count":162,"doi":379,"paper":380,"created_at":391},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":20,"substance":136,"depth":219,"authority":20,"freshness":221,"relevant":21,"comment":338},"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。",[340,341],{"name":334,"url":331},{"name":334,"url":342},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23106954",[26,27,28,286,344],"农地制图",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":348},{"candidates":349,"agricultural_evidence":52},[350,355,360,365,371],{"code":32,"score":351,"supported":52,"title":352,"abstract":353,"tags":354},27,[58,59],[56,28,58,59],[27,28],{"code":306,"score":248,"supported":52,"title":356,"abstract":357,"tags":359},[],[286,358],"remote sensing",[286],{"code":83,"score":84,"supported":52,"title":361,"abstract":362,"tags":364},[],[363],"粮食安全",[],{"code":366,"score":84,"supported":52,"title":367,"abstract":368,"tags":370},"J1",[],[369],"农业规划",[],{"code":90,"score":21,"supported":14,"title":372,"abstract":373,"tags":374},[],[],[26],[376,377],"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","10.5281\u002Fzenodo.23106955",{"doi":379,"openalex_id":381,"authors":382,"venue":334,"cited_by_count":98,"oa_url":331,"card":385,"direction":389,"ingested_from":122},"W7219591984",[383],{"name":384,"orcid":9},"J.I. Igbokwe, C.E Ejikeme, J.O and Obi",{"tldr":386,"method":387,"finding":388,"direction":389,"opportunity":390},"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","农业遥感与作物表型","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":393,"title":394,"url":395,"summary":396,"summary_zh":397,"content":9,"source_name":398,"source_url":395,"published_at":399,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":400,"score_detail":401,"sources":404,"tags":406,"primary_topic":408,"secondary_topics":413,"classification":415,"search_phrases":460,"slug":463,"view_count":162,"doi":464,"paper":465,"created_at":481},3776,"Smart Agriculture with Machine Learning and Internet of Things Techniques: Literature Review and Bibliometric Analysis","https:\u002F\u002Fdoi.org\u002F10.7160\u002Faol.2026.180301","A booming field known as \"smart agriculture\" has emerged because of the rapid revolution and transformation of agriculture. It makes use of cutting-edge agricultural technologies, like the internet of things and machine learning (ML) algorithms, to enhance farm management tasks, boost agricultural productivity, and lessen environmental impact by empowering farmers to react swiftly and efficiently to climate change. A key component of the shift to smart farming is the Internet of Things (IoT), which makes it easier for sensors and devices to connect and share data. It also gives farmers access to actionable information and knowledge, enabling them to make well-informed decisions using machine learning algorithms to enhance crop management and boost yields. Numerous tasks, including weed identification, fertilization requirement analysis, irrigation adjustment, and pest and soil management, are made possible by the automation made possible by these technologies. They allow farmers to make targeted interventions by continuously monitoring crop health, which lowers labor costs and the environmental impact. The effects of technological advancements, such as machine learning and the Internet of Things, on the agriculture industry are thoroughly examined in this paper. It draws attention to the useful applications of these instruments, which help farmers better, more automatically, and more precisely manage their resources and output.","由于农业的快速变革与转型，一个被称为“智慧农业”的蓬勃发展的领域应运而生。它利用物联网和机器学习（ML）算法等前沿农业技术，通过赋能农民快速高效地应对气候变化，来增强农场管理任务、提高农业生产力并减少环境影响。物联网（IoT）是向智慧农业转型的关键组成部分，它使传感器和设备之间的连接与数据共享变得更加便捷。它还为农民提供可操作的信息和知识，使他们能够利用机器学习算法做出明智决策，以改善作物管理并提高产量。这些技术所实现的自动化使许多任务成为可能，包括杂草识别、施肥需求分析、灌溉调整以及病虫害和土壤管理。它们使农民能够通过持续监测作物健康来进行有针对性的干预，从而降低劳动力成本和环境影响。本文深入探讨了机器学习与物联网等技术进步对农业产业的影响。它重点关注这些工具的实际应用，这些应用帮助农民更好、更自动、更精准地管理其资源和产出。","Agris on-line Papers in Economics and Informatics","2026-09-28T00:00:00Z",65,{"impact":220,"substance":219,"depth":63,"authority":402,"freshness":221,"relevant":21,"comment":403},13,"核心期刊发表的智慧农业文献综述与文献计量分析，方法规范、信息量足，但属综述性研究，产业影响有限，可作为主题页聚合素材。",[405],{"name":398,"url":395},[26,27,407,30],"农业物联网",{"code":409,"slug":410,"name":411,"path":412,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[414],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":416},{"candidates":417,"agricultural_evidence":52},[418,425,430,434,438,445,449,456],{"code":409,"score":17,"supported":52,"title":419,"abstract":421,"tags":424},[420],"internet of things",[422,420,423],"物联网","IoT",[407],{"code":90,"score":63,"supported":52,"title":426,"abstract":428,"tags":429},[427],"smart agriculture",[26,427],[26],{"code":32,"score":63,"supported":52,"title":431,"abstract":432,"tags":433},[58],[28,58],[27],{"code":69,"score":221,"supported":52,"title":435,"abstract":436,"tags":437},[71],[],[30],{"code":439,"score":76,"supported":52,"title":440,"abstract":441,"tags":444},"F3",[],[442,443],"施肥","fertilization",[],{"code":155,"score":76,"supported":52,"title":446,"abstract":447,"tags":448},[],[158,159],[],{"code":450,"score":76,"supported":52,"title":451,"abstract":452,"tags":455},"H3",[],[453,454],"灌溉","irrigation",[],{"code":75,"score":76,"supported":52,"title":457,"abstract":458,"tags":459},[],[79,80],[],[461,462],"农业人工智能 农业物联网 文献计量 智慧农业","农业人工智能 农业物联网","农业人工智能农业物联网文献计量智慧农业-3776","10.7160\u002Faol.2026.180301",{"doi":464,"openalex_id":466,"authors":467,"venue":398,"cited_by_count":98,"oa_url":395,"card":475,"direction":479,"ingested_from":122},"W7214682427",[468,470,472],{"name":469,"orcid":9},"Karima El Majdouby",{"name":471,"orcid":9},"Mohamed El Mohadab",{"name":473,"orcid":474},"Karim Abouelmehdi","https:\u002F\u002Forcid.org\u002F0009-0007-8904-8762",{"tldr":476,"method":477,"finding":478,"direction":479,"opportunity":480},"综述机器学习与物联网在智慧农业中的应用并做文献计量分析。","文献综述与文献计量分析，梳理ML与IoT农业应用。","ML与IoT可实现杂草识别、灌溉施肥等自动化，提升产量并降低环境与人力成本。","智慧农业 \u002F 农业物联网","可针对具体作物构建ML+IoT闭环决策系统，并量化其经济与环境效益。","2026-09-29T23:30:10.681055Z",{"id":483,"title":484,"url":485,"summary":486,"summary_zh":487,"content":9,"source_name":488,"source_url":485,"published_at":489,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":490,"score_detail":491,"sources":494,"tags":496,"primary_topic":499,"secondary_topics":500,"classification":501,"search_phrases":534,"slug":537,"view_count":538,"doi":539,"paper":540,"created_at":553},3626,"A Hybrid Model for Crop Yield Prediction Using Recurrent Neural Networks and Explainable Artificial Intelligence","https:\u002F\u002Fdoi.org\u002F10.22194\u002Fjgias\u002F27.2061","Accurate maize yield prediction is essential for ensuring food security and supporting agricultural planning in Kenya. However, the changes in climate and severe weather are posing more challenges to the stability of yield and food security. While advanced machine learning models, such as Long Short–Term Memory (LSTM) networks, offer strong predictive performance, their inherent \"black–box\" nature limits their practical adoption by farmers and policymakers who require interpretable and trustworthy insights. This study developed a transparent predictive model by integrating LSTM with Explainable Artificial Intelligence (XAI). Using county–level maize yield data (2012–2023), the hybrid model was compared against Random Forest and Gradient Boosting baselines. The LSTM model achieved superior performance (R² = 0.8551, RMSE = 0.3715, MAE = 0.2705), compared to Random Forest (R2 = 0.8441, RMSE= 0.3851 and MAE= 0.2856) and Gradient Boosting (R2 = 0.7925, RMSE = 0.4443 and MAE = 0.3234) baselines. XAI analysis, using SHapley Additive explanations (SHAP) and Local Interpretable Model–agnostic Explanations (LIME), identified longitude, latitude, and annual rainfall as key predictors. The model maintained high accuracy while improving interpretability, increasing transparency and trust. This provides actionable insights for farmers and policymakers, supporting evidence–based planning and enhancing resilience in smallholder systems. Keywords: Crop yield prediction, long short–term memory (LSTM), explainable artificial intelligence, precision agriculture, machine learning.","准确的玉米产量预测对于保障肯尼亚粮食安全和支持农业规划至关重要。然而，气候变化和极端天气对产量稳定性和粮食安全构成了更多挑战。尽管长短期记忆（LSTM）网络等先进机器学习模型具有强大的预测性能，但其固有的“黑箱”特性限制了农民和政策制定者的实际采用，因为他们需要可解释且可信的洞见。本研究通过将LSTM与可解释人工智能（XAI）相结合，开发了一个透明的预测模型。利用县级玉米产量数据（2012—2023年），将该混合模型与随机森林和梯度提升基线模型进行了比较。LSTM模型取得了更优的性能（R² = 0.8551，RMSE = 0.3715，MAE = 0.2705），优于随机森林（R2 = 0.8441，RMSE= 0.3851，MAE= 0.2856）和梯度提升（R2 = 0.7925，RMSE = 0.4443，MAE = 0.3234）基线模型。使用SHapley加法解释（SHAP）和局部可解释模型无关解释（LIME）进行的XAI分析，确定了经度、纬度和年降雨量是关键预测因子。该模型在保持高精度的同时提高了可解释性，增强了透明度和信任。这为农民和政策制定者提供了可操作的洞见，支持基于证据的规划，并增强小农系统的韧性。关键词：作物产量预测，长短期记忆（LSTM），可解释人工智能，精准农业，机器学习。","Journal of Global Innovations in Agricultural Sciences","2026-09-25T00:00:00Z",71,{"impact":220,"substance":492,"depth":137,"authority":402,"freshness":20,"relevant":21,"comment":493},21,"将LSTM与可解释AI结合用于肯尼亚县级玉米产量预测，方法新颖、指标详实，对智慧农业有参考价值，但属境外区域研究，公共影响有限。",[495],{"name":488,"url":485},[26,27,28,497,498],"可解释AI","玉米产量预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":502},{"candidates":503,"agricultural_evidence":52},[504,508,512,516,520,526,530],{"code":32,"score":136,"supported":52,"title":505,"abstract":506,"tags":507},[54],[28,58,54],[27,497,28],{"code":90,"score":248,"supported":52,"title":509,"abstract":510,"tags":511},[],[142,251],[26],{"code":229,"score":76,"supported":52,"title":513,"abstract":514,"tags":515},[],[226,230],[],{"code":83,"score":76,"supported":52,"title":517,"abstract":518,"tags":519},[],[363,87],[],{"code":521,"score":84,"supported":52,"title":522,"abstract":523,"tags":525},"G2",[],[524],"smallholder",[],{"code":75,"score":84,"supported":52,"title":527,"abstract":528,"tags":529},[],[79],[],{"code":366,"score":84,"supported":52,"title":531,"abstract":532,"tags":533},[],[369],[],[535,536],"LSTM 玉米产量预测","SHAP LIME 农业模型","LSTM玉米产量预测-3626",5,"10.22194\u002Fjgias\u002F27.2061",{"doi":539,"openalex_id":541,"authors":542,"venue":488,"cited_by_count":98,"oa_url":485,"card":548,"direction":120,"ingested_from":122},"W7214446524",[543,545],{"name":544,"orcid":9},"Stephen Gitau Ndung’u",{"name":546,"orcid":547},"Consolata Gakii","https:\u002F\u002Forcid.org\u002F0000-0003-2783-9992",{"tldr":549,"method":550,"finding":551,"direction":120,"opportunity":552},"用LSTM结合可解释AI预测肯尼亚玉米产量，兼顾精度与透明度。","基于2012-2023县级玉米产量数据，LSTM融合SHAP与LIME，对比随机","LSTM精度最高（R²=0.8551），经度、纬度和年降雨量是主要预测因子。","可探索将可解释AI与遥感、气象多源数据融合，提升小农户区域产量预测的可信度与推广性。","2026-09-27T23:31:22.771605Z"]