[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-874":3,"related-874":65},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":35,"secondary_topics":41,"classification":42,"search_phrases":59,"slug":62,"view_count":63,"doi":8,"paper":8,"created_at":64},874,"Reinforcement Learning加Visual SLAM融合高光谱成像实现精准农业UAV作物水分胁迫自动检测:92%检测率vs固定路径61%,飞行时间减32%","https:\u002F\u002Fengrxiv.org\u002Fpreprint\u002Fview\u002F8004","Arsalan Iqbal在《engrXiv》预印本平台发布研究,提出一种基于强化学习引导的自主四旋翼无人机平台,融合机载视觉同步定位与建图(Visual SLAM)与推扫式高光谱成像载荷,在近实时构建地理参考的、冠层配准的作物水分胁迫指数(CWSI)地图。平台采用近端策略优化(PPO)训练的自适应采样策略,根据飞行中积累的证据将飞行时间和传感器驻留时间重新分配到新兴水分胁迫区域。在0.8公顷试验田模拟田间试验中,强化学习引导策略实现了92%的水分胁迫区域检测率(固定网格基线为61%),同时任务飞行时间减少约32%。研究还报告了SLAM衍生的冠层结构对CWSI精度的贡献消融研究、田间复杂性敏感性和全误差预算分析。注:本研究为预印本,未经实地验证。",null,"Preprint\u002FVersion 1\n\n## ##article.authors##\n\n## DOI:\n\n[https:\u002F\u002Fdoi.org\u002F10.31224\u002F8004](https:\u002F\u002Fdoi.org\u002F10.31224\u002F8004)\n## Keywords:\n\n VSLAM, Hyperspectral, Remote Sensing, Crop Water-Stress Index, Reinforcement Learning, Proximal Policy Optimization, Adaptive Path Planning \n## Abstract\n\nLocalized soil-moisture deficits, that is, irregular sub-field patches where crops experience water stress well before visible wilting, are a leading cause of yield variability in row-crop agriculture. These zones are difficult to detect at the spatial resolution and revisit frequency required for timely irrigation response. This paper presents a reinforcement-learningguided autonomous quadrotor unmanned aerial vehicle (UAV) platform that fuses onboard Visual Simultaneous Localization and Mapping (Visual SLAM) with a pushbroom hyperspectral imaging payload to construct georeferenced, canopy-registered maps of a Crop Water-Stress Index (CWSI) in near real time. Rather than flying a fixed lawnmower survey, the platform is guided by an adaptive-sampling policy trained with Proximal Policy Optimization (PPO) that reallocates flight time and sensor dwell toward regions of emerging water stress as evidence accumulates mid-flight. We present the complete engineering pipeline: airframe and sensor design, a keyframe-based Visual SLAM front and back end that provides centimeter-scale geolocation without continuous reliance on Real-Time Kinematic (RTK) GNSS lock, a hyperspectral preprocessing and spectralindex chain (NDVI, NDRE, NDWI\u002FNDMI) used to derive CWSI through a learned regression, the partially observable Markov Decision Process (POMDP) formulation and reward shaping used to train the sampling policy, and the fused system architecture tying these subsystems together. In simulated field trials over a 0.8-hectare test plot, the reinforcement-learning-guided policy achieved a 92% water-stress-zone detection rate versus 61% for a fixed-grid baseline, while reducing mission flight time by approximately 32%. We further report an ablation study isolating the contribution of SLAM-derived canopy structure to CWSI accuracy, a sensitivity analysis across field complexity, and a full error budget for the fused pipeline. We close with a discussion of validation limitations, broader scientific and agricultural impact, and a roadmap toward multi-UAV fleet deployment for whole-farm monitoring\n\n## Posted\n\n2026-08-19","engrXiv preprint 2026年8月19日","2026-08-19T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},18,22,20,7,1,"论文提出RL+SLAM+高光谱融合的精准农业无人机方案，检测率提升显著，方法新颖，数据详实，但为预印本且影响范围限于细分领域。",[24],{"name":10,"url":6},[26,27,28,29,30,31,32,33,34],"无人机","预印本","精准农业","强化学习","高光谱成像","Visual SLAM","作物水分胁迫","PPO","engrXiv",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":43,"locked":14,"version":44,"method":45,"evidence":46},"classified","2026-10-22.2","rules",{"candidates":47,"agricultural_evidence":50},[48,54],{"code":36,"score":49,"supported":50,"title":51,"abstract":52,"tags":53},9,true,[28],[],[28],{"code":55,"score":21,"supported":14,"title":56,"abstract":57,"tags":58},"I2",[],[],[29],[60,61],"作物水分胁迫 高光谱成像 强化学习 精准农业","作物水分胁迫 高光谱成像","作物水分胁迫高光谱成像强化学习精准农业-874",3,"2026-08-23T00:08:13.490346Z",{"total":66,"page":21,"page_size":66,"items":67},6,[68,107,166,208,253,309],{"id":69,"title":70,"url":71,"summary":72,"summary_zh":8,"content":73,"source_name":74,"source_url":8,"published_at":75,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":76,"score_detail":77,"sources":81,"tags":83,"primary_topic":88,"secondary_topics":89,"classification":90,"search_phrases":102,"slug":105,"view_count":66,"doi":8,"paper":8,"created_at":106},415,"AgriField-40K: Adapting Vision Models to Agriculture With Efficient Continual Pretraining","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.07984","Vasileios Tzouras、Paraskevas Pegios、Lazaros Nalpantidis于arXiv(2026-08-08)发表预印本。研究引入AgriField-40K,一个源自17个公开数据集的以田野为中心的农业数据集,涵盖多样化作物、杂草和田间条件;并提出AgriMAE,一种参数高效的持续预训练基线,通过仅训练轻量适配器来适配在自然图像上预训练的掩码自动编码器。AgriMAE在下游任务上一致提升性能,使用多达9倍更少的可训练参数即可匹配甚至超越全量微调,证明AgriField-40K是农业视觉持续预训练实用的资源。为精准农业模型开发提供低成本高效率的迁移学习路径,对农业视觉应用具有显著价值。","[Skip to main content](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.07984#content)[](https:\u002F\u002Farxiv.org\u002FIgnoreMe)[![Image 1: archive](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Farxiv-logo-primary-light.svg)](https:\u002F\u002Farxiv.org\u002F)[Search](https:\u002F\u002Farxiv.org\u002Fsearch)[Submit](https:\u002F\u002Farxiv.org\u002Fuser\u002Fcreate)[Donate](https:\u002F\u002Finfo.arxiv.org\u002Fabout\u002Fdonate.html)[Log in](https:\u002F\u002Farxiv.org\u002Flogin)\n\nSearch arXiv \n\n Press Enter to search · [Advanced search](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fadvanced)\n\n# Computer Science > Computer Vision and Pattern Recognition\n\n**arXiv:2608.07984** (cs) \n\n [Submitted on 8 Aug 2026]\n\n# Title:AgriField-40K: Adapting Vision Models to Agriculture With Efficient Continual Pretraining\n\nAuthors:[Vasileios Tzouras](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Tzouras,+V), [Paraskevas Pegios](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Pegios,+P), [Lazaros Nalpantidis](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Nalpantidis,+L)\n\nView a PDF of the paper titled AgriField-40K: Adapting Vision Models to Agriculture With Efficient Continual Pretraining, by Vasileios Tzouras and 2 other authors\n\n[View PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2608.07984)[HTML (experimental)](https:\u002F\u002Farxiv.org\u002Fhtml\u002F2608.07984v1)\n> Abstract:Field-based agricultural computer vision is important for precision agriculture, yet it largely depends on expensive annotations and costly adaptation of large pretrained models. We introduce AgriField-40K, a field-centric dataset curated from 17 public resources and covering diverse crops, weeds, and field conditions. Building on this, we present AgriMAE, a parameter-efficient continual pretraining baseline that adapts a masked autoencoder pretrained on natural images by training only lightweight adapters. We further explore semantic feature reconstruction as an alternative pretraining objective and evaluate transfer across multiple tasks. AgriMAE consistently improves downstream performance and can match or even outperform full fine-tuning while using up to $9\\times$ fewer trainable parameters, showing that AgriField-40K is a practical resource for continual pretraining in agricultural vision. Project page: [this https URL](https:\u002F\u002Fdtu-pas.github.io\u002Fagrifield40k\u002F)\n\nComments:Accepted at the Computer Vision in Plant Phenotyping and Agriculture (CVPPA) Workshop at the European Conference on Computer Vision (ECCV) 2026\nSubjects:Computer Vision and Pattern Recognition (cs.CV)\nCite as:[arXiv:2608.07984](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.07984) [cs.CV]\n(or [arXiv:2608.07984v1](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.07984v1) [cs.CV] for this version)\n[https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2608.07984](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2608.07984)\n\nFocus to learn more\n\n arXiv-issued DOI via DataCite (pending registration)\n\n## Submission history\n\n From: Vasileios Tzouras [[view email](https:\u002F\u002Farxiv.org\u002Fshow-email\u002F09ea92b8\u002F2608.07984)] \n\n**[v1]** Sat, 8 Aug 2026 07:38:01 UTC (24,651 KB)\n\n[](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.07984)Full-text links:\n## Access 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[Venue](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.07984)\n*   [Institution](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.07984)\n*   [Topic](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.07984)\n\n About arXivLabs  \n\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https:\u002F\u002Finfo.arxiv.org\u002Flabs\u002Findex.html).\n\n[Which authors of this paper are endorsers?](https:\u002F\u002Farxiv.org\u002Fauth\u002Fshow-endorsers\u002F2608.07984) | [Disable MathJax](javascript:setMathjaxCookie()) ([What is MathJax?](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fmathjax.html)) \n\n We gratefully acknowledge support from our **major funders**, [**member institutions**](https:\u002F\u002Finfo.arxiv.org\u002Fabout\u002Fourmembers.html), , and all contributors. \n\n[About](https:\u002F\u002Finfo.arxiv.org\u002Fabout)·[Help](https:\u002F\u002Finfo.arxiv.org\u002Fhelp)·[Contact](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fcontact.html)·[Subscribe](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fsubscribe)·[Copyright](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Flicense\u002Findex.html)·[Privacy](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fpolicies\u002Fprivacy_policy.html)·[Accessibility](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fweb_accessibility.html)·[Operational Status (opens in new tab)](https:\u002F\u002Fstatus.arxiv.org\u002F)\n\nMajor funding support from\n\n[![Image 5: Simons Foundation](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ffunders\u002Fsimons-foundation.png)](https:\u002F\u002Fwww.simonsfoundation.org\u002F)[![Image 6: Simons Foundation International](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ffunders\u002Fsimons-foundation-international.png)](https:\u002F\u002Fwww.sfi.org.bm\u002F)[![Image 7: Schmidt Sciences](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ffunders\u002Fschmidt-sciences.png)](https:\u002F\u002Fwww.schmidtsciences.org\u002F)","arXiv 2608.07984 2026-08-08","2026-08-07T23:00:00Z",80,{"impact":17,"substance":18,"depth":19,"authority":78,"freshness":79,"relevant":21,"comment":80},12,8,"农业视觉大模型持续预训练新数据集与方法，发表于ECCV研讨会，对智慧农业有实质推动。",[82],{"name":74,"url":71},[84,27,28,85,86,87],"计算机视觉","持续预训练","数据集","AgriField-40K",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":91},{"candidates":92,"agricultural_evidence":50},[93,98],{"code":36,"score":94,"supported":50,"title":95,"abstract":96,"tags":97},4,[],[28],[28],{"code":55,"score":21,"supported":14,"title":99,"abstract":100,"tags":101},[],[],[84],[103,104],"持续预训练 计算机视觉 精准农业 数据集","持续预训练 计算机视觉","持续预训练计算机视觉精准农业数据集-415","2026-08-11T23:57:03.188478Z",{"id":108,"title":109,"url":110,"summary":111,"summary_zh":8,"content":8,"source_name":112,"source_url":110,"published_at":113,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":114,"score_detail":115,"sources":120,"tags":122,"primary_topic":127,"secondary_topics":128,"classification":129,"search_phrases":143,"slug":146,"view_count":147,"doi":148,"paper":149,"created_at":165},5539,"Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10473-z","Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems。Precision Agriculture","Precision Agriculture","2026-10-22T00:00:00Z",79,{"impact":116,"substance":117,"depth":17,"authority":118,"freshness":13,"relevant":21,"comment":119},16,21,14,"核心期刊新论文，提出鲁棒且可解释的RGB作物行检测方法，对视觉导航农机具参考价值明确，但属细分技术进展，未达重大突破层级。",[121],{"name":112,"url":110},[123,124,28,125,126],"智慧农业","农业人工智能","机器视觉","作物行检测",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":130},{"candidates":131,"agricultural_evidence":50},[132,138],{"code":36,"score":133,"supported":50,"title":134,"abstract":135,"tags":137},5,[],[136],"precision agriculture",[123,28],{"code":55,"score":139,"supported":14,"title":140,"abstract":141,"tags":142},2,[],[],[124,125],[144,145],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 机器视觉","RGB作物行检测-5539",0,"10.1007\u002Fs11119-026-10473-z",{"doi":148,"openalex_id":150,"authors":151,"venue":112,"cited_by_count":147,"oa_url":8,"card":8,"direction":8,"ingested_from":164},"W7223117828",[152,155,158,161],{"name":153,"orcid":154},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":156,"orcid":157},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":159,"orcid":160},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":162,"orcid":163},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","openalex","2026-10-22T11:20:09.678587Z",{"id":167,"title":168,"url":169,"summary":170,"summary_zh":8,"content":8,"source_name":171,"source_url":169,"published_at":172,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":173,"score_detail":174,"sources":176,"tags":178,"primary_topic":181,"secondary_topics":182,"classification":183,"search_phrases":196,"slug":199,"view_count":147,"doi":200,"paper":201,"created_at":207},5380,"Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.17987.98089","Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems。OpenAlex","OpenAlex","2026-10-19T00:00:00Z",56,{"impact":17,"substance":79,"depth":13,"authority":78,"freshness":79,"relevant":21,"comment":175},"主题契合智慧农业，但摘要仅为标题重复、缺乏方法与数据细节，信息增量有限，暂不建议进入每日精选。",[177],{"name":171,"url":169},[123,179,28,180],"农业机器人","智能采收",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":184},{"candidates":185,"agricultural_evidence":50},[186,191],{"code":36,"score":187,"supported":50,"title":188,"abstract":189,"tags":190},13,[136],[136],[123,28],{"code":192,"score":21,"supported":14,"title":193,"abstract":194,"tags":195},"F6",[],[],[179],[197,198],"农业机器人 智慧农业 智能采收 精准农业","农业机器人 智慧农业","农业机器人智慧农业智能采收精准农业-5380","10.13140\u002Frg.2.2.17987.98089",{"doi":200,"openalex_id":202,"authors":203,"venue":8,"cited_by_count":147,"oa_url":169,"card":8,"direction":206,"ingested_from":164},"W7222971426",[204],{"name":205,"orcid":8},"Joydeep Dey","智慧农业 \u002F 农业物联网","2026-10-20T23:30:08.600540Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":213,"content":8,"source_name":214,"source_url":211,"published_at":172,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":114,"score_detail":215,"sources":217,"tags":219,"primary_topic":222,"secondary_topics":223,"classification":224,"search_phrases":236,"slug":239,"view_count":147,"doi":240,"paper":241,"created_at":252},5316,"PLANT-GPK v2.7: Spatial Heterogeneity and Predictive Digital Twin for Plant Phenotyping","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23460273","Traditional image-based plant phenotyping assumes spatial homogeneity, estimating a single set of physiological parameters for the entire plant. This paper introduces a novel extension to the PLANT-GPK framework: Spatial Physiological Heatmaps and Predictive Digital Twins. By dividing the plant image into a 3x3 grid and solving the inverse ODE problem for each spatial patch, we reveal the hidden spatial heterogeneity of resource allocation. Furthermore, the recovered parameters are extrapolated to predict the exact day of harvest using a continuous smooth-physics model, providing a powerful prescriptive tool for precision agriculture.","传统的基于图像的植物表型分析假设空间同质性，即对整个植株估计单一组生理参数。本文提出了对PLANT-GPK框架的一种新颖扩展：空间生理热图与预测性数字孪生。通过将植物图像划分为3×3网格并针对每个空间区块求解逆常微分方程（ODE）问题，我们揭示了资源分配中隐藏的空间异质性。此外，利用连续光滑物理模型对所恢复的参数进行外推，以预测确切的收获日期，为精准农业提供了强有力的规范性工具。","Zenodo (CERN European Organization for Nuclear Research)",{"impact":17,"substance":18,"depth":17,"authority":78,"freshness":49,"relevant":21,"comment":216},"该论文提出植物表型空间异质性建模与预测性数字孪生方法，方法新颖、结论有实用价值，但属细分领域预印本成果，未达重大突破层级。",[218],{"name":214,"url":211},[123,124,220,28,221],"数字孪生","植物表型",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":225},{"candidates":226,"agricultural_evidence":50},[227,232],{"code":36,"score":19,"supported":50,"title":228,"abstract":230,"tags":231},[229],"digital twin",[28,220,136],[220,123,28],{"code":55,"score":21,"supported":14,"title":233,"abstract":234,"tags":235},[],[],[124],[237,238],"PLANT-GPK 植物表型","空间生理热图 数字孪生","PLANT-GPK植物表型-5316","10.5281\u002Fzenodo.23460273",{"doi":240,"openalex_id":242,"authors":243,"venue":214,"cited_by_count":147,"oa_url":211,"card":246,"direction":250,"ingested_from":164},"W7222885652",[244],{"name":245,"orcid":8},"MOHAMMED TAHAR SAIM",{"tldr":247,"method":248,"finding":249,"direction":250,"opportunity":251},"将植物图像分3x3网格反演ODE参数，构建空间生理热图与预测数字孪生。","基于PLANT-GPK框架，分块求解逆ODE问题，并用连续平滑物理模型外推。","揭示了植物资源分配的空间异质性，可预测精确收获日。","农业遥感与作物表型","可探索更细粒度网格与多模态表型融合，提升数字孪生预测精度与田间适用性。","2026-10-19T23:30:19.414490Z",{"id":254,"title":255,"url":256,"summary":257,"summary_zh":258,"content":8,"source_name":259,"source_url":256,"published_at":260,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":261,"score_detail":262,"sources":264,"tags":266,"primary_topic":271,"secondary_topics":272,"classification":273,"search_phrases":285,"slug":288,"view_count":21,"doi":289,"paper":290,"created_at":308},5103,"Assessment of the efficiency of resource potential utilization in the agricultural sector of the economy under investment and innovation development","https:\u002F\u002Fdoi.org\u002F10.32782\u002Fnvlvet-e10701","Ukraine’s agricultural sector is undergoing transformation under the influence of wartime challenges, economic restructuring, European integration, digitalization, and the growing need for sustainable resource management. Improving the efficiency of resource potential utilization has become a prerequisite for strengthening the competitiveness and sustainability of agricultural enterprises. The purpose of this study is to provide a comprehensive assessment of the efficiency of resource potential utilization in the agricultural sector of the economy under investment and innovation development and to substantiate organizational and economic measures for its improvement. The research is based on a systems approach using economic-statistical, comparative, analytical, economic-mathematical, graphical, forecasting, and composite assessment methods. The empirical analysis covers agricultural enterprises in the Poltava region during 2015–2025. Resource potential utilization efficiency was comprehensively assessed by integrating economic, investment, innovation, environmental, and managerial indicators. The results show that technological modernization, digital transformation, investment activity, and strategic management are the principal drivers of efficient resource utilization and sustainable enterprise development. An integrated organizational and economic model for resource potential development is proposed, combining precision agriculture, digital monitoring, resource-saving technologies, and human capital development. Forecasting confirms the positive impact of the proposed measures on labour productivity, capital productivity, profitability, investment activity, digitalization, and the integrated indicator of resource utilization efficiency. The scientific novelty lies in improving a comprehensive methodological approach to assessing the efficiency of resource potential utilization in the agricultural sector of the economy through integrating economic, investment, innovation, environmental, and managerial components. The practical significance lies in applying the proposed approach to strategic planning for agricultural enterprise development, investment decision-making, and improving resource management efficiency.","乌克兰农业部门正处在战时挑战、经济结构调整、欧洲一体化、数字化以及可持续资源管理需求日益增长等多重因素影响下的转型进程之中。提高资源潜力利用效率已成为增强农业企业竞争力和可持续性的先决条件。本研究旨在对投资与创新发展背景下经济中农业部门的资源潜力利用效率进行综合评估，并为其改进提供组织与经济措施的依据。研究基于系统方法，运用了经济统计、比较、分析、经济数学、图形、预测及综合评估等方法。实证分析涵盖2015—2025年波尔塔瓦地区的农业企业。通过整合经济、投资、创新、环境和管理指标，对资源潜力利用效率进行了综合评估。结果表明，技术现代化、数字化转型、投资活动和战略管理是高效资源利用与企业可持续发展的主要驱动力。研究提出了一个资源潜力发展的综合组织与经济模型，将精准农业、数字监测、资源节约技术和人力资本开发相结合。预测结果证实了所提措施对劳动生产率、资本生产率、盈利能力、投资活动、数字化以及资源利用效率综合指标的积极影响。本研究的科学新颖性在于，通过整合经济、投资、创新、环境和管理等组成部分，改进了评估经济中农业部门资源潜力利用效率的综合方法论。其实践意义在于将所提方法应用于农业企业发展的战略规划、投资决策以及提高资源管理效率。","Scientific Messenger of LNU of Veterinary Medicine and Biotechnologies","2026-10-15T00:00:00Z",63,{"impact":79,"substance":17,"depth":116,"authority":78,"freshness":49,"relevant":21,"comment":263},"区域实证研究，方法综合但影响限于乌克兰波尔塔瓦地区，对国内三农信息化参考价值有限。",[265],{"name":259,"url":256},[267,28,268,269,270],"数字农业","农业投资","乌克兰农业","农业资源利用",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":274},{"candidates":275,"agricultural_evidence":50},[276,280],{"code":36,"score":79,"supported":50,"title":277,"abstract":278,"tags":279},[],[28,136],[267,28],{"code":281,"score":21,"supported":14,"title":282,"abstract":283,"tags":284},"G3",[],[],[268],[286,287],"波尔塔瓦 农业企业 资源潜力","乌克兰 数字化 投资","波尔塔瓦农业企业资源潜力-5103","10.32782\u002Fnvlvet-e10701",{"doi":289,"openalex_id":291,"authors":292,"venue":259,"cited_by_count":147,"oa_url":256,"card":302,"direction":306,"ingested_from":164},"W7221969015",[293,296,299],{"name":294,"orcid":295},"Olena Bukhalo","https:\u002F\u002Forcid.org\u002F0000-0001-7776-6715",{"name":297,"orcid":298},"Андрій Петрович Дорошенко","https:\u002F\u002Forcid.org\u002F0000-0002-6314-1586",{"name":300,"orcid":301},"Yuri Nesterenko","https:\u002F\u002Forcid.org\u002F0009-0000-5831-4465",{"tldr":303,"method":304,"finding":305,"direction":306,"opportunity":307},"评估乌克兰波尔塔瓦地区农业企业资源潜力利用效率并提出改进措施。","系统方法结合经济统计、比较、预测与复合评估，分析2015-2025年农业企业数据","技术现代化、数字化、投资与战略管理是资源高效利用和可持续发展的主要驱动力。","数字乡村与农业信息化","可延伸至战后或转型期农业数字化投资对资源效率的因果机制与动态监测研究。","2026-10-16T23:30:33.662938Z",{"id":310,"title":311,"url":312,"summary":313,"summary_zh":8,"content":8,"source_name":314,"source_url":312,"published_at":315,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":316,"score_detail":317,"sources":320,"tags":322,"primary_topic":324,"secondary_topics":325,"classification":326,"search_phrases":342,"slug":345,"view_count":139,"doi":346,"paper":347,"created_at":361},5082,"Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jip.2026.108765","Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives。Journal of Invertebrate Pathology","Journal of Invertebrate Pathology","2026-10-14T00:00:00Z",67,{"impact":78,"substance":116,"depth":318,"authority":118,"freshness":79,"relevant":21,"comment":319},17,"核心期刊综述，聚焦昆虫病原线虫在精准农业中的应用方法与挑战，专业性强但属细分领域，产业影响有限。",[321],{"name":314,"url":312},[123,124,28,323],"生物防治",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":327},{"candidates":328,"agricultural_evidence":50},[329,333,338],{"code":36,"score":187,"supported":50,"title":330,"abstract":331,"tags":332},[136],[136],[123,28],{"code":334,"score":21,"supported":14,"title":335,"abstract":336,"tags":337},"F4",[],[],[323],{"code":55,"score":21,"supported":14,"title":339,"abstract":340,"tags":341},[],[],[124],[343,344],"昆虫病原线虫 精准农业","Journal of Invertebrate Pathology 线虫","昆虫病原线虫精准农业-5082","10.1016\u002Fj.jip.2026.108765",{"doi":346,"openalex_id":348,"authors":349,"venue":314,"cited_by_count":147,"oa_url":8,"card":8,"direction":206,"ingested_from":164},"W7221746877",[350,353,356,358],{"name":351,"orcid":352},"Alperen Kaan Bütüner","https:\u002F\u002Forcid.org\u002F0000-0002-2121-3529",{"name":354,"orcid":355},"Hilal Erdoğan","https:\u002F\u002Forcid.org\u002F0000-0002-0387-2600",{"name":357,"orcid":8},"İsmail Alper Susurluk",{"name":359,"orcid":360},"Edwin E. Lewis","https:\u002F\u002Forcid.org\u002F0000-0002-8825-1562","2026-10-16T23:30:09.556034Z"]