[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3680":3,"related-3680":52},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":51},3680,"AI-Driven Nutrient Optimization Systems for Precision Agriculture: A Data-Integrated Approach to Reducing Nutrient Runoff and Enhancing Soil Health in Agricultural Systems","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.85286","Nutrients play a fundamental role in plant growth, soil fertility, and overall agricultural productivity. However, inefficient fertilizer application in conventional farming systems often leads to nutrient imbalance, reduced soil quality, and increased environmental degradation. The emergence of precision agriculture and artificial intelligence (AI) has introduced new opportunities for improving nutrient management through data-driven decision-making. This study investigates an AI-driven nutrient optimization system designed to enhance fertilizer efficiency while reducing nutrient runoff and improving soil health. Findings indicate that AI-based systems significantly improve nutrient use efficiency by enabling site-specific fertilizer recommendations and adaptive management strategies. The integration of multi-source agricultural data enhances prediction accuracy for soil nutrient status and crop demand, while also supporting early identification of nutrient loss risks such as runoff and leaching. The study concludes that AI-driven nutrient optimization provides a sustainable and scalable approach for modern agriculture, supporting both productivity enhancement and environmental protection through intelligent, data-integrated farming systems.","养分在植物生长、土壤肥力和农业整体生产力中发挥着基础性作用。然而，传统农业系统中低效的施肥方式往往导致养分失衡、土壤质量下降以及环境退化加剧。精准农业与人工智能（AI）的出现，为通过数据驱动决策改善养分管理带来了新的机遇。本研究探讨了一种AI驱动的养分优化系统，旨在提高肥料利用效率，同时减少养分径流并改善土壤健康。研究结果表明，基于AI的系统通过实现特定地块的施肥建议和适应性管理策略，显著提高了养分利用效率。多源农业数据的整合提升了土壤养分状况和作物需求的预测精度，同时也有助于早期识别径流和淋溶等养分流失风险。研究得出结论，AI驱动的养分优化为现代农业提供了一种可持续且可扩展的方法，通过智能化、数据整合的农业系统，同时支持生产力提升和环境保护。",null,"International Journal For Multidisciplinary Research","2026-09-26T00:00:00Z","论文",10,false,61,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":20,"relevant":21,"comment":22},12,18,15,8,1,"论文提出AI驱动的养分优化系统，方法有新意但属学术探讨，产业落地影响有限，可作为智慧农业技术动态收录。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","精准施肥","土壤健康","养分流失",[32,33],"AI 养分优化 精准农业","养分流失 土壤健康","AI养分优化精准农业-3680",0,"10.36948\u002Fijfmr.2026.v08i05.85286",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":44,"direction":48,"ingested_from":50},"W7214553198",[40,42],{"name":41,"orcid":9},"Olufisayo Obebe",{"name":43,"orcid":9},"Ebenezer Akinola",{"tldr":45,"method":46,"finding":47,"direction":48,"opportunity":49},"研究AI驱动的养分优化系统，通过数据集成实现精准施肥，减少养分流失并改善土壤健康。","集成多源农业数据与AI算法，进行站点特异性施肥推荐和自适应管理。","AI系统显著提高养分利用效率，增强土壤养分与作物需求预测精度，并早期识别流失风险。","农业人工智能与决策模型","可探索多源数据融合的实时决策模型，以及在不同土壤气候条件下AI系统的可扩展性与长期环境效应评估。","openalex","2026-09-28T23:30:49.535963Z",{"total":53,"page":21,"page_size":53,"items":54},6,[55,95,126,170,221,257],{"id":56,"title":57,"url":58,"summary":59,"summary_zh":60,"content":9,"source_name":61,"source_url":58,"published_at":62,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":63,"score_detail":64,"sources":68,"tags":70,"search_phrases":73,"slug":76,"view_count":35,"doi":77,"paper":78,"created_at":94},3515,"Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs13198-026-03435-1","Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence。International Journal of Systems Assurance Engineering and Management","基于人工智能的精准农业土壤制图与肥料优化。《国际系统保障工程与管理杂志》","International Journal of Systems Assurance Engineering and Management","2026-09-24T00:00:00Z",62,{"impact":17,"substance":65,"depth":19,"authority":66,"freshness":20,"relevant":21,"comment":67},14,13,"论文探讨AI用于土壤制图与施肥优化，属智慧农业细分方向，但摘要信息有限、影响面偏窄，暂不建议进入每日精选。",[69],{"name":61,"url":58},[26,27,28,71,72],"遥感","土壤制图",[74,75],"土壤制图 人工智能 精准施肥","精准农业 肥料优化 AI","土壤制图人工智能精准施肥-3515","10.1007\u002Fs13198-026-03435-1",{"doi":77,"openalex_id":79,"authors":80,"venue":61,"cited_by_count":35,"oa_url":9,"card":89,"direction":48,"ingested_from":50},"W7214144821",[81,84,86],{"name":82,"orcid":83},"Neetu Mittal","https:\u002F\u002Forcid.org\u002F0000-0002-2012-0523",{"name":82,"orcid":85},"https:\u002F\u002Forcid.org\u002F0000-0001-6923-0013",{"name":87,"orcid":88},"Pradeepta Kumar Sarangi","https:\u002F\u002Forcid.org\u002F0000-0003-3827-6208",{"tldr":90,"method":91,"finding":92,"direction":48,"opportunity":93},"利用人工智能进行土壤制图和肥料优化，以支持精准农业。","人工智能方法，用于土壤制图与肥料优化。","AI可提升土壤制图与肥料优化的精准性，促进精准农业。","可探索多源数据融合与实时决策模型，提升肥料推荐的自适应性和可解释性。","2026-09-25T23:30:49.869002Z",{"id":96,"title":97,"url":98,"summary":99,"summary_zh":9,"content":9,"source_name":100,"source_url":9,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":102,"score_detail":103,"sources":108,"tags":110,"search_phrases":113,"slug":116,"view_count":21,"doi":9,"paper":117,"created_at":125},3326,"Research on the Application of Agricultural Big Data in Plant Growth Prediction——基于多源数据同化与混合智能（MDA-HI）框架","https:\u002F\u002Fwww.icck.org\u002Ffilebob\u002Fuploads\u002Fstorage\u002FDIA_ANrbHOHS4uBBpnVQ6.pdf","《Digital Intelligence in Agriculture》2026年第2卷第2期。Wei Yongqiang等提出Multi-source Data Assimilation and Hybrid Intelligence（MDA-HI）框架，将基于过程的作物模型与集成机器学习算法（包括基于Transformer的架构和物理信息神经网络）相结合。在2023—2025年中国多生态区主要作物（水稻、小麦、玉米、番茄）的实证验证中：MDA-HI模型实现了产量预测RMSE平均减少42.7%、关键物候期预测减少38.1%。水稻-小麦轮作系统大规模案例研究显示数据驱动处方可将氮肥使用减少22.5%、灌溉水减少18.3%，同时产量增加5.1%。","《Digital Intelligence in Agriculture》2026; 2(2):54-67","2026-09-17T00:00:00Z",85,{"impact":104,"substance":105,"depth":106,"authority":65,"freshness":53,"relevant":21,"comment":107},22,24,19,"多源数据同化与混合智能框架在四大作物上验证，减肥节水增产数据扎实，方法新颖且具产业推广价值。",[109],{"name":100,"url":98},[26,27,28,111,112],"农业大数据","作物生长预测",[114,115],"MDA-HI 多源数据同化 作物模型","水稻小麦轮作 氮肥减量 产量预测","MDA-HI多源数据同化作物模型-3326",{"doi":9,"openalex_id":9,"authors":118,"venue":9,"cited_by_count":35,"oa_url":9,"card":119,"direction":48,"ingested_from":124},[],{"tldr":120,"method":121,"finding":122,"direction":48,"opportunity":123},"提出MDA-HI框架，融合过程模型与混合智能，用于作物生长与产量预测。","多源数据同化结合Transformer与物理信息神经网络，在中国多生态区验证。","产量预测RMSE降42.7%，氮肥减22.5%、灌溉水减18.3%，产量增5.1%。","可探索轻量化MDA-HI在边缘设备部署及跨区域迁移能力，降低小农户应用门槛。","agent","2026-09-24T00:04:02.947080Z",{"id":127,"title":128,"url":129,"summary":130,"summary_zh":131,"content":9,"source_name":132,"source_url":129,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":133,"score_detail":134,"sources":138,"tags":140,"search_phrases":143,"slug":146,"view_count":21,"doi":147,"paper":148,"created_at":169},2805,"Synergistic effects of spectral preprocessing and machine learning algorithms for nitrogen estimation in tomato using hyperspectral spectral data","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71908-1","Abstract Accurate estimation of leaf nitrogen content is essential for optimizing fertilization management and improving crop productivity. This study proposes a nondestructive hyperspectral imaging framework combined with advanced machine learning algorithms to classify nitrogen levels in tomato leaves (Solanum lycopersicum L., Royal variety). A total of 300 hyperspectral samples were collected from plants subjected to three nitrogen treatments (N-30%, N-60%, and N-90%) under controlled conditions. Reflectance data (400–1000 nm) were calibrated and preprocessed using Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Savitzky–Golay (SG) filtering. Both unsupervised and supervised algorithms were systematically evaluated. Clustering results demonstrated that preprocessing substantially influenced class separability. The MSC + GMM combination yielded the best unsupervised results, with the lowest Davies–Bouldin index (0.443) and the highest silhouette coefficient (0.975), indicating improved cluster compactness and separation. In supervised learning, Neural Networks (NN) consistently outperformed the other models, achieving 100% accuracy and an ROC–AUC of 1.00 under SNV, MSC, and SG preprocessing. Gradient Boosting (GB) and Support Vector Machine (SVM) also demonstrated strong predictive capability, whereas conventional models, including LDA, LR, KNN, and NB, showed moderate improvements following preprocessing. Statistical analysis confirmed the significant effect of spectral preprocessing on both clustering and classification outcomes ( p \u003C 0.05). Overall, integrating hyperspectral imaging with appropriate preprocessing and nonlinear machine learning models provided strong classification performance under the controlled experimental conditions. However, the exceptionally high performance observed for some models should be interpreted cautiously because of the relatively small dataset and experimentally controlled nitrogen treatments. Moreover, the absence of an independent external validation dataset limits the assessment of generalizability across growing conditions, cultivars, and field environments. Therefore, independent validation using larger and more diverse datasets is required before broader application of the proposed framework in practical precision agriculture.","摘要 准确估算叶片氮含量对于优化施肥管理和提高作物生产力至关重要。本研究提出了一种无损高光谱成像框架，结合先进的机器学习算法对番茄叶片（Solanum lycopersicum L.，Royal品种）的氮水平进行分类。在受控条件下，从接受三种氮处理（N-30%、N-60%和N-90%）的植株中共采集了300个高光谱样本。反射率数据（400–1000 nm）经标准正态变量变换（SNV）、多元散射校正（MSC）和Savitzky–Golay（SG）滤波进行校准和预处理。系统评估了无监督和有监督算法。聚类结果表明，预处理对类别可分性产生了显著影响。MSC + GMM组合取得了最佳无监督结果，具有最低的Davies–Bouldin指数（0.443）和最高的轮廓系数（0.975），表明聚类紧密度和分离度得到改善。在有监督学习中，神经网络（NN）始终优于其他模型，在SNV、MSC和SG预处理下均达到100%的准确率和1.00的ROC–AUC。梯度提升（GB）和支持向量机（SVM）也表现出较强的预测能力，而传统模型（包括LDA、LR、KNN和NB）在预处理后表现出中等程度的改善。统计分析证实了光谱预处理对聚类和分类结果均有显著影响（p \u003C 0.05）。总体而言，将高光谱成像与适当的预处理及非线性机器学习模型相结合，在受控实验条件下提供了较强的分类性能。然而，由于数据集相对较小且氮处理为实验受控条件，某些模型所表现出的极高性能应谨慎解读。此外，缺乏独立的外部验证数据集限制了对跨生长条件、品种和田间环境泛化能力的评估。因此，在将该框架广泛应用于实际精准农业之前，需要使用更大规模、更多样化的数据集进行独立验证。","Scientific Reports",77,{"impact":19,"substance":135,"depth":136,"authority":65,"freshness":13,"relevant":21,"comment":137},21,17,"高光谱结合机器学习实现番茄叶片氮素无损估测，方法系统、结论明确，但样本量小且缺乏外部验证，属细分领域方法学进展。",[139],{"name":132,"url":129},[26,27,28,141,142],"番茄种植","高光谱遥感",[144,145],"农业人工智能 高光谱遥感 智慧农业 番茄种植","农业人工智能 高光谱遥感","农业人工智能高光谱遥感智慧农业番茄种植-2805","10.1038\u002Fs41598-026-71908-1",{"doi":147,"openalex_id":149,"authors":150,"venue":132,"cited_by_count":35,"oa_url":129,"card":163,"direction":48,"ingested_from":50},"W7213472002",[151,154,157,160],{"name":152,"orcid":153},"Mohammad Vahedi Torshizi","https:\u002F\u002Forcid.org\u002F0000-0003-3648-1515",{"name":155,"orcid":156},"Sajad Sabzi","https:\u002F\u002Forcid.org\u002F0000-0003-2439-5329",{"name":158,"orcid":159},"Mohsen Azadbakht","https:\u002F\u002Forcid.org\u002F0000-0002-5726-9321",{"name":161,"orcid":162},"Razieh Pourdarbani","https:\u002F\u002Forcid.org\u002F0000-0003-0766-8305",{"tldr":164,"method":165,"finding":166,"direction":167,"opportunity":168},"用高光谱成像结合预处理与机器学习，对番茄叶片氮素水平进行分类。","300个高光谱样本，SNV、MSC、SG预处理，聚类与多种监督分类算法对比。","MSC+GMM聚类最优，神经网络在三种预处理下均达100%准确率。","农业遥感与作物表型","样本少且无外部验证，可扩展多品种、多环境田间数据并做独立验证以提升泛化性。","2026-09-17T23:30:59.361085Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":9,"content":9,"source_name":175,"source_url":173,"published_at":176,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":177,"score_detail":178,"sources":181,"tags":183,"search_phrases":186,"slug":189,"view_count":21,"doi":190,"paper":191,"created_at":220},2622,"Synergizing process-based modeling and data-driven learning for precision nitrogen optimization in winter wheat","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104983","Synergizing process-based modeling and data-driven learning for precision nitrogen optimization in winter wheat。Agricultural Systems","Agricultural Systems","2026-09-16T00:00:00Z",79,{"impact":18,"substance":179,"depth":136,"authority":65,"freshness":13,"relevant":21,"comment":180},20,"将过程模型与数据驱动学习融合用于冬小麦精准氮肥优化，方法新颖、发表于核心期刊且时效性强，具备进入每日精选的价值。",[182],{"name":175,"url":173},[26,27,28,184,185],"作物模型","冬小麦",[187,188],"农业人工智能 作物模型 智慧农业 精准施肥","农业人工智能 作物模型","农业人工智能作物模型智慧农业精准施肥-2622","10.1016\u002Fj.agsy.2026.104983",{"doi":190,"openalex_id":192,"authors":193,"venue":175,"cited_by_count":35,"oa_url":173,"card":9,"direction":9,"ingested_from":50},"W7213352238",[194,196,199,202,204,206,208,210,212,214,216,218],{"name":195,"orcid":9},"Yuru Ye",{"name":197,"orcid":198},"Qian Wang","https:\u002F\u002Forcid.org\u002F0000-0003-0750-7843",{"name":200,"orcid":201},"Davide Cammarano","https:\u002F\u002Forcid.org\u002F0000-0003-0918-550X",{"name":203,"orcid":9},"Kang Yu",{"name":205,"orcid":9},"Siva K. Balasundram",{"name":207,"orcid":9},"Wei Li",{"name":209,"orcid":9},"Xiuli Li",{"name":211,"orcid":9},"Xiaojun Liu",{"name":213,"orcid":9},"Yongchao Tian",{"name":215,"orcid":9},"Yan Zhu",{"name":217,"orcid":9},"Weixing Cao",{"name":219,"orcid":9},"Qiang Cao","2026-09-16T23:30:05.340754Z",{"id":222,"title":223,"url":224,"summary":225,"summary_zh":226,"content":9,"source_name":227,"source_url":224,"published_at":228,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":63,"score_detail":229,"sources":232,"tags":234,"search_phrases":237,"slug":240,"view_count":35,"doi":241,"paper":242,"created_at":256},2517,"Artificial Intelligence-Based Intelligent Fertigation Recommendation Systems for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1722972","Artificial-intelligence-enabled fertilizer and fertigation recommendation has progressed from single-task crop or fertilizer classification toward integrated decision-support architectures. However, predictive classification, agronomic dose calculation, irrigation scheduling, explainability, real-time sensing, and farmer-facing delivery are often studied separately. This paper presents a structured narrative review of these strands and an applied machine-learning case study using the publicly available Crop and Fertilizer Dataset for Western Maharashtra. The case study contains 4,513 records spanning five districts, 16 crops, and 19 fertilizer classes. Seven classifiers were compared using an 80:20 stratified train-test split with five-fold stratified cross-validation on the training set. XGBoost achieved 97.34% test accuracy and 94.21% ± 1.15% cross-validated accuracy, while Random Forest achieved 93.58% and 90.89% ± 0.80%, respectively. TreeSHAP analysis of Random Forest identified crop identity, potassium, and nitrogen as the leading predictors of the historical fertilizer class. These results are interpreted as a computational baseline rather than proof of agronomic optimality because the target label represents recorded fertilizer choices. The review also incorporates evidence on IoT\u002Fedge-cloud sensing, multilingual agricultural advisory, and federated learning. It concludes that RF\u002FSHAP, IoT sensing, and multilingual interfaces are established capabilities; a more defensible research direction is an auditable pipeline that separates fertilizer identity, nutrient dose, and application timing, connects explainable prediction to sequential scheduling, and independently benchmarks outputs against authoritative agronomic guidance. The proposed Intelligent Fertigation Recommendation System (IFRS) is therefore presented as a research framework requiring multi-season and field validation before claims of yield, water, nutrient-use-efficiency, or adoption benefits.","人工智能驱动的肥料与灌溉施肥推荐已从单一任务的作物或肥料分类，发展为集成式决策支持架构。然而，预测性分类、农艺用量计算、灌溉调度、可解释性、实时感知以及面向农户的交付往往被分别研究。本文对这些研究脉络进行了结构化叙述性综述，并基于公开的“西马哈拉施特拉邦作物与肥料数据集”开展了一项应用机器学习案例研究。该案例研究包含4，513条记录，涵盖五个地区、16种作物和19个肥料类别。采用80：20分层训练-测试划分，并在训练集上进行五折分层交叉验证，比较了七种分类器。XGBoost取得了97.34%的测试准确率和94.21% ± 1.15%的交叉验证准确率，而随机森林分别取得了93.58%和90.89% ± 0.80%。对随机森林的TreeSHAP分析表明，作物身份、钾和氮是历史肥料类别的主要预测因子。这些结果被解释为计算基线，而非农艺最优性的证明，因为目标标签代表的是有记录的肥料选择。该综述还纳入了关于物联网\u002F边缘-云感知、多语言农业咨询和联邦学习的证据。结论认为，随机森林\u002FSHAP、物联网感知和多语言界面已是成熟能力；更具可辩护性的研究方向是构建一条可审计的流水线，将肥料身份、养分用量和施用时机分离，将可解释预测与序贯调度相连接，并依据权威农艺指南对输出进行独立基准测试。因此，所提出的智能灌溉施肥推荐系统（IFRS）被作为一个研究框架提出，在声称产量、水分、养分利用效率或采用效益之前，仍需进行多季和田间验证。","Iconic Research and Engineering Journals","2026-09-14T00:00:00Z",{"impact":17,"substance":18,"depth":136,"authority":53,"freshness":230,"relevant":21,"comment":231},9,"对AI水肥推荐研究进行系统综述并给出可复现的机器学习基线，方法透明、结论审慎，但来源为普通工程类期刊且属综述性论文，产业影响有限，适合作为技术参考而非每日精选头条。",[233],{"name":227,"url":224},[26,27,28,235,236],"可解释AI","水肥一体化",[238,239],"农业人工智能 水肥一体化 智慧农业 精准施肥","农业人工智能 水肥一体化","农业人工智能水肥一体化智慧农业精准施肥-2517","10.64388\u002Firev10i3-1722972",{"doi":241,"openalex_id":243,"authors":244,"venue":227,"cited_by_count":35,"oa_url":249,"card":250,"direction":255,"ingested_from":50},"W7212934364",[245,247],{"name":246,"orcid":9},"Shraddha S. Tayade",{"name":248,"orcid":9},"Yogesh V. Chimate","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1722972.pdf",{"tldr":251,"method":252,"finding":253,"direction":48,"opportunity":254},"综述AI施肥推荐研究，并用马哈拉施特拉数据集比较七种分类器，提出可审计智能施肥推荐框架。","结构化综述加机器学习案例，4513条记录，七分类器对比，XGBoost与Tree","XGBoost测试准确率97.34%，但标签仅为历史施肥选择，不能证明农艺最优。","可研究分离肥料种类、养分剂量与施用时序的可审计推荐流水线，并进行多季田间验证。","智慧农业 \u002F 农业物联网","2026-09-15T23:30:12.675690Z",{"id":258,"title":259,"url":260,"summary":261,"summary_zh":262,"content":9,"source_name":263,"source_url":260,"published_at":264,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":133,"score_detail":265,"sources":268,"tags":270,"search_phrases":272,"slug":275,"view_count":35,"doi":276,"paper":277,"created_at":289},2036,"Smart crop recommendation: fusing nutrient and climate data with Krill Herd Optimization and explainable AI","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrai.2026.1910196","Crop recommendation is a vital part of precision agriculture as it helps farmers choose appropriate crops according to the nutrient profile and environmental conditions. This paper presents a crop recommendation framework in which Multi-Layer Perceptron (MLP), XGBoost, and Tab Transformer are first evaluated as baseline prediction models, followed by the proposed Krill Herd Optimization (KHO)-based explainable framework integrated with Explainable Artificial Intelligence (XAI). These eleven parameters are created based on agronomic and environmental aspects, namely: Nitrogen, Phosphorus, Potassium, Copper, Iron, Magnesium, Sulphur, Temperature, Rainfall, pH and Humidity. For better model transparency and to aid informed decision-making, model explanations with SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) are used to identify feature contributions to crop predictions both locally and globally. The experimental results showed that Tab Transformer significantly outperformed the other models, with an accuracy of 0.99, precision of 0.98, recall of 0.99 and F1-score of 0.98. The proposed framework further incorporates Krill Herd Optimization (KHO) to generate optimized nutrient and climate profiles, while Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) are used for comparative evaluation of optimization performance. By combining explainable AI with optimization methods, the framework improves crop suitability prediction and provides transparent insights into the factors influencing crop recommendations, ensuring reliable decision support for practical farming applications. The proposed framework supports precision agriculture by enabling data-driven crop selection, reducing unnecessary fertilizer usage, optimizing crop productivity, and promoting sustainable farming practices.","作物推荐是精准农业的重要组成部分，有助于农民根据养分状况和环境条件选择适宜的作物。本文提出了一个作物推荐框架，首先评估多层感知机（MLP）、XGBoost和Tab Transformer作为基线预测模型，随后提出基于磷虾群优化（KHO）的可解释框架，并集成可解释人工智能（XAI）。基于农艺和环境因素构建了十一个参数，即：氮、磷、钾、铜、铁、镁、硫、温度、降雨量、pH值和湿度。为提高模型透明度并辅助知情决策，采用SHAP（SHapley加性解释）和LIME（局部可解释模型无关解释）进行模型解释，以在局部和全局层面识别特征对作物预测的贡献。实验结果表明，Tab Transformer显著优于其他模型，准确率为0.99，精确率为0.98，召回率为0.99，F1分数为0.98。所提框架进一步引入磷虾群优化（KHO）以生成优化的养分和气候方案，同时使用遗传算法（GA）、粒子群优化（PSO）和模拟退火（SA）对优化性能进行比较评估。通过将可解释人工智能与优化方法相结合，该框架提高了作物适宜性预测能力，并提供了影响作物推荐因素的透明洞察，确保为实际农业应用提供可靠的决策支持。所提框架通过实现数据驱动的作物选择、减少不必要的肥料使用、优化作物生产力并促进可持续农业实践，为精准农业提供支持。","Frontiers in Artificial Intelligence","2026-09-09T00:00:00Z",{"impact":266,"substance":135,"depth":18,"authority":66,"freshness":230,"relevant":21,"comment":267},16,"融合营养与气候数据、Krill Herd优化与可解释AI的作物推荐框架，方法新颖、结论可靠，对精准农业决策有参考价值。",[269],{"name":263,"url":260},[26,27,28,235,271],"作物推荐",[273,274],"农业人工智能 作物推荐 智慧农业 精准施肥","农业人工智能 作物推荐","农业人工智能作物推荐智慧农业精准施肥-2036","10.3389\u002Ffrai.2026.1910196",{"doi":276,"openalex_id":278,"authors":279,"venue":263,"cited_by_count":35,"oa_url":260,"card":284,"direction":255,"ingested_from":50},"W7211994206",[280,282],{"name":281,"orcid":9},"P. Latha",{"name":283,"orcid":9},"P. Kumaresan",{"tldr":285,"method":286,"finding":287,"direction":48,"opportunity":288},"提出融合养分与气候数据、KHO优化和可解释AI的作物推荐框架，Tab Transformer精度达0","用MLP、XGBoost、Tab Transformer建模，结合KHO优化与S","Tab Transformer预测最优（准确率0.99），KHO优化养分气候方案，XAI提升推荐透明","可探索将可解释AI与优化算法嵌入真实农田物联网实时数据流，验证跨区域泛化与农户采纳效果。","2026-09-10T23:30:09.054372Z"]