[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2908":3,"related-2908":66},{"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":65},2908,"An information-driven air–ground collaborative framework for UAV-based tillage defect identification and re-tillage path optimization","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112447","An information-driven air–ground collaborative framework for UAV-based tillage defect identification and re-tillage path optimization。Computers and Electronics in Agriculture","一种信息驱动的空地协同框架，用于基于无人机耕作缺陷识别与再耕作路径优化。《农业计算机与电子》",null,"Computers and Electronics in Agriculture","2026-09-18T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,21,14,8,1,"发表于农业信息领域核心期刊，提出空地协同的无人机耕地缺陷识别与再耕路径优化框架，方法新颖且面向精准农业实际需求，具备较高参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","农业无人机","精准农业","遥感",[32,33],"无人机 耕地缺陷 识别","再耕路径 优化","无人机耕地缺陷识别-2908",0,"10.1016\u002Fj.compag.2026.112447",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":9,"card":58,"direction":62,"ingested_from":64},"W7213547466",[40,42,44,46,49,51,53,56],{"name":41,"orcid":9},"Chenshuo Xie",{"name":43,"orcid":9},"Yejun Zhu",{"name":45,"orcid":9},"Dongfang Li",{"name":47,"orcid":48},"Maohua Xiao","https:\u002F\u002Forcid.org\u002F0000-0001-5213-1035",{"name":50,"orcid":9},"Le Yang",{"name":52,"orcid":9},"Yuxuan Wan",{"name":54,"orcid":55},"Weihua Wei","https:\u002F\u002Forcid.org\u002F0000-0001-5333-4707",{"name":57,"orcid":9},"Mingfeng Wang",{"tldr":59,"method":60,"finding":61,"direction":62,"opportunity":63},"提出空地协同框架，用无人机识别耕整缺陷并优化补耕路径。","无人机遥感影像与信息驱动算法，识别缺陷并规划重耕路径。","框架能有效识别耕整缺陷并生成优化补耕路径，提升作业质量。","智慧农业 \u002F 农业物联网","可探索多机协同与实时动态重规划，结合土壤传感器提升缺陷识别精度。","openalex","2026-09-19T23:30:02.081036Z",{"total":67,"page":21,"page_size":67,"items":68},6,[69,124,181,230,259,286],{"id":70,"title":71,"url":72,"summary":73,"summary_zh":74,"content":9,"source_name":75,"source_url":72,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":76,"score_detail":77,"sources":82,"tags":84,"search_phrases":86,"slug":89,"view_count":35,"doi":90,"paper":91,"created_at":123},2947,"AI-enabled UAV-based Soil Organic Carbon Mapping in Arid Environments: A Pilot Study Protocol","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315495282260915110324","Introduction Soil organic carbon (SOC) is an important indicator of soil health, agricultural productivity, and carbon sequestration potential. However, accurate and scalable SOC mapping in arid environments is constrained by high spatial heterogeneity and the limitations of conventional soil sampling. This study aims to develop a standardized UAV-enabled framework for high-resolution SOC mapping in arid agricultural environments. Methods A pilot-study protocol integrating UAV-based hyperspectral remote sensing with artificial intelligence and machine learning was developed. The workflow encompasses study-site selection, ground-reference sampling, UAV hyperspectral data acquisition, radiometric and geometric preprocessing, spectral feature extraction and selection, machine-learning model development, validation, uncertainty assessment, and performance evaluation using R 2 , RMSE, and MAE. The protocol also incorporates assessment of environmental confounders, including soil moisture, surface roughness, and crop residues. Results The resulting framework provides a systematic and reproducible workflow for UAV-based SOC estimation, integrating field observations, hyperspectral features, predictive modelling, and uncertainty assessment. It establishes defined procedures for evaluating model robustness and transferability across varying field conditions. Discussion The framework addresses an important methodological gap in UAV-enabled SOC mapping by integrating remote sensing and AI within a standardized pilot-study design. Its emphasis on environmental confounders and uncertainty assessment can improve the reliability and comparability of SOC mapping studies. However, field validation across diverse arid environments remains necessary. Conclusion The proposed protocol provides a practical foundation for reproducible SOC mapping and subsequent field validation, supporting precision agriculture, sustainable soil management, and carbon monitoring, reporting, and verification (MRV) in arid regions.","引言 土壤有机碳（SOC）是衡量土壤健康、农业生产力及碳固存潜力的重要指标。然而，干旱环境中高空间异质性和传统土壤采样的局限性制约了准确且可扩展的SOC制图。本研究旨在开发一个标准化的无人机（UAV）框架，用于干旱农业环境中的高分辨率SOC制图。方法 开发了一套整合无人机高光谱遥感与人工智能及机器学习的试点研究方案。该工作流程涵盖研究地点选择、地面参考采样、无人机高光谱数据采集、辐射与几何预处理、光谱特征提取与选择、机器学习模型开发、验证、不确定性评估，以及使用R²、RMSE和MAE进行的性能评价。该方案还包括对环境混杂因素的评估，包括土壤水分、地表粗糙度和作物残茬。结果 所构建的框架为基于无人机的SOC估算提供了系统且可重复的工作流程，整合了野外观测、高光谱特征、预测建模和不确定性评估。它建立了明确的程序，用于评估模型在不同田间条件下的稳健性和可迁移性。讨论 该框架通过将遥感与人工智能整合于标准化的试点研究设计中，填补了无人机SOC制图领域的重要方法学空白。其对环境混杂因素和不确定性评估的重视，可提高SOC制图研究的可靠性和可比性。然而，仍需在不同干旱环境中进行田间验证。结论 所提出的方案为可重复的SOC制图及后续田间验证提供了实用基础，支持干旱地区的精准农业、可持续土壤管理以及碳监测、报告与核查（MRV）。","The Open Agriculture Journal",67,{"impact":78,"substance":17,"depth":79,"authority":80,"freshness":20,"relevant":21,"comment":81},12,16,13,"提出无人机高光谱结合AI的干旱区土壤有机碳制图标准化方案，方法框架清晰但尚属试点协议、缺乏实地验证，具备一定参考价值。",[83],{"name":75,"url":72},[26,27,29,30,85],"土壤碳汇",[87,88],"无人机 土壤有机碳 制图","AI 高光谱 干旱农业","无人机土壤有机碳制图-2947","10.2174\u002F0118743315495282260915110324",{"doi":90,"openalex_id":92,"authors":93,"venue":75,"cited_by_count":35,"oa_url":72,"card":117,"direction":121,"ingested_from":64},"W7213561504",[94,97,100,103,106,109,111,113,115],{"name":95,"orcid":96},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":98,"orcid":99},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":101,"orcid":102},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":104,"orcid":105},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":107,"orcid":108},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":110,"orcid":9},"Almaha Jamal",{"name":112,"orcid":9},"Afra Rashed",{"name":114,"orcid":9},"Hamda Yousif",{"name":116,"orcid":9},"Sarah Sadeq",{"tldr":118,"method":119,"finding":120,"direction":121,"opportunity":122},"提出一套无人机高光谱结合AI的干旱区土壤有机碳制图标准化试点方案。","无人机高光谱遥感、地面采样、光谱特征选择与机器学习建模，用R²、RMSE、MAE","构建了可复现的SOC估算流程，并纳入环境混杂因素与不确定性评估。","农业遥感与作物表型","可在多干旱区开展跨区域验证，探索模型迁移性与不确定性量化方法。","2026-09-19T23:30:33.273156Z",{"id":125,"title":126,"url":127,"summary":128,"summary_zh":129,"content":9,"source_name":130,"source_url":127,"published_at":131,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":132,"score_detail":133,"sources":137,"tags":139,"search_phrases":141,"slug":144,"view_count":35,"doi":145,"paper":146,"created_at":180},2512,"Deep learning-based classification of wet direct seeded rice, broadcasted direct seeded rice and transplanted rice using drone imagery for precision agriculture","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1698781","Accurate estimation of crop area using classification techniques applied to drone imagery plays an important role in precision agriculture. Traditional machine learning (ML) approaches have been widely used for agricultural image classification; however, advanced deep learning (DL) models generally provide superior feature extraction and classification capability for complex crop patterns. Differentiating various rice establishment methods is essential for precise area estimation. In this study, advanced deep learning models were employed to classify three types of rice cultivation: (i) Broadcasted Direct Seeded Rice (DSR), (ii) Wet Direct Seeded Rice (Wet DSR), and (iii) Transplanted Rice (TR) using drone imagery. The drone imagery was collected from an experimental field at Praanadhaara Organised Agro Forestry Private Limited, Bapatla District, Andhra Pradesh, India. The images were captured in the visible spectrum (Red, Green, and Blue bands) on three dates, viz., 15 October 2023, 27 October 2023, and 01 December 2023, from an altitude of 40 m and were used to train and test classification models. Classification was performed using six ResNet-50 based hybrid models, namely, ResNet-50+K-Nearest Neighbor (ResNet-50+KNN), ResNet-50+Support Vector Machine (ResNet-50+SVM), ResNet-50+Decision Trees (ResNet-50+DT), ResNet-50+Random Forest (ResNet-50+RF), ResNet-50+Naïve Bayes (ResNet-50+NB), and ResNet-50+Neural Network (ResNet-50+NN), along with two additional DL architectures, namely, You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8). Model performance was evaluated using overall accuracy (OA), precision (P), recall (R), kappa coefficient (K), F1-score (F1), and mean Average Precision (mAP). Among the tested classifiers, the ResNet-50+NN model consistently achieved the highest average overall accuracies of 93.16%, 95.81%, and 93.31% at T 1 , T 2 , and T 3 , respectively, outperforming all other models, whose accuracies ranged from 78.05% to 92.07%, 86.11%–95.24%, and 77.47%–92.09% across the respective time intervals. The ResNet-50+NN model also recorded the highest precision (0.93–0.97), recall (0.92–0.97), F1-score (0.92–0.97), and kappa coefficient (0.89–0.96), demonstrating superior and consistent classification performance across all observation dates. The methodology developed in this work enables precise identification of rice establishment methods, improving crop mapping and monitoring. This identification enhances resource efficiency, optimizes input use, and supports site-specific management, contributing to sustainable precision agriculture.","利用分类技术对无人机影像进行作物面积精确估算，在精准农业中发挥着重要作用。传统机器学习（ML）方法已广泛用于农业图像分类；然而，先进的深度学习（DL）模型通常对复杂作物模式具有更优越的特征提取和分类能力。区分不同的水稻种植方式对于精确估算面积至关重要。本研究采用先进的深度学习模型，利用无人机影像对三种水稻种植类型进行分类：（i）撒播直播稻（DSR），（ii）湿润直播稻（Wet DSR），以及（iii）移栽稻（TR）。无人机影像采集自印度安得拉邦巴帕特拉县Praanadhaara Organised Agro Forestry Private Limited的试验田。图像在可见光谱（红、绿、蓝波段）下于三个日期拍摄，即2023年10月15日、2023年10月27日和2023年12月1日，飞行高度为40 m，用于训练和测试分类模型。分类采用六种基于ResNet-50的混合模型，即ResNet-50+K近邻（ResNet-50+KNN）、ResNet-50+支持向量机（ResNet-50+SVM）、ResNet-50+决策树（ResNet-50+DT）、ResNet-50+随机森林（ResNet-50+RF）、ResNet-50+朴素贝叶斯（ResNet-50+NB）和ResNet-50+神经网络（ResNet-50+NN），以及两种额外的深度学习架构，即You Only Look Once第5版（YOLOv5）和You Only Look Once第8版（YOLOv8）。采用总体精度（OA）、精确率（P）、召回率（R）、Kappa系数（K）、F1分数（F1）和平均精度均值（mAP）评估模型性能。在测试的分类器中，ResNet-50+NN模型在T₁、T₂和T₃分别持续取得最高的平均总体精度，为93.16%、95.81%和93.31%，优于所有其他模型，后者的精度在相应时间段分别为78.05%–92.07%、86.11%–95.24%和77.47%–92.09%。ResNet-50+NN模型还记录了最高的精确率（0.93–0.97）、召回率（0.92–0.97）、F1分数（0.92–0.97）和Kappa系数（0.89–0.96），在所有观测日期均表现出优越且稳定的分类性能。本研究开发的方法能够精确识别水稻种植方式，改进作物制图和监测。这种识别增强了资源","Frontiers in Remote Sensing","2026-09-14T00:00:00Z",76,{"impact":79,"substance":18,"depth":134,"authority":80,"freshness":135,"relevant":21,"comment":136},17,9,"基于无人机可见光影像与ResNet-50混合模型区分三类水稻种植方式，方法对比系统、精度数据扎实，对精准农业作物制图有参考价值，但属细分技术进展，影响范围有限。",[138],{"name":130,"url":127},[26,27,140,29,30],"水稻",[142,143],"农业人工智能 智慧农业 精准农业 水稻","农业人工智能 智慧农业","农业人工智能智慧农业精准农业水稻-2512","10.3389\u002Ffrsen.2026.1698781",{"doi":145,"openalex_id":147,"authors":148,"venue":130,"cited_by_count":35,"oa_url":174,"card":175,"direction":62,"ingested_from":64},"W7212834743",[149,151,153,155,157,160,162,164,166,169,171],{"name":150,"orcid":9},"Amrutha Lakshmi Gubbala",{"name":152,"orcid":9},"Santosha Rathod",{"name":154,"orcid":9},"Ramesh Dasyam",{"name":156,"orcid":9},"Mahender Kumar Rapolu",{"name":158,"orcid":159},"Arun Kumar Dasari","https:\u002F\u002Forcid.org\u002F0000-0001-7398-8601",{"name":161,"orcid":9},"Pundarikakshudu Kurra",{"name":163,"orcid":9},"Hanuma Raviteja Madireddy",{"name":165,"orcid":9},"Prajwal R. Shashishekhar",{"name":167,"orcid":168},"Ravi V. Mural","https:\u002F\u002Forcid.org\u002F0000-0002-5489-9918",{"name":170,"orcid":9},"Anil Kumar",{"name":172,"orcid":173},"R. M. Sundaram","https:\u002F\u002Forcid.org\u002F0000-0002-9857-8251","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fremote-sensing\u002Farticles\u002F10.3389\u002Ffrsen.2026.1698781\u002Fpdf",{"tldr":176,"method":177,"finding":178,"direction":121,"opportunity":179},"用无人机可见光影像和深度学习模型区分直播稻、湿直播稻与移栽稻三种水稻种植方式。","无人机RGB影像三个时相，比较ResNet-50混合模型与YOLOv5\u002Fv8分类","ResNet-50+NN表现最优，三期总体精度达93.16%、95.81%、93.31%，优于其他模","可扩展到多光谱\u002F多时相与更大区域验证，并用于种植方式制图与面积精准估算。","2026-09-15T23:30:08.600535Z",{"id":182,"title":183,"url":184,"summary":185,"summary_zh":186,"content":9,"source_name":187,"source_url":184,"published_at":188,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":189,"score_detail":190,"sources":193,"tags":195,"search_phrases":197,"slug":200,"view_count":35,"doi":201,"paper":202,"created_at":229},2165,"Multi-grained image-text retrieval in farmland remote sensing via multimodal large language models","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.116113","Low-altitude farmland remote sensing (FRS) has become an important tool for precision agricultural monitoring. However, existing FRS methods are mainly designed for segmentation with predefined categories, making them insufficient for semantic understanding and unable to support flexible natural-language queries. To address this limitation, we introduce an artificial intelligence-based task, FRS cross-modal retrieval, which aims to retrieve target farmland regions from large-scale remote sensing images using textual descriptions. To support this task, we construct the first Farmland Remote Sensing Cross-Modal Retrieval (FRS-CMR) dataset, containing 67,806 image-text pairs generated through multimodal large language models and expert refinement. Based on FRS-CMR, we propose a Farmland-oriented Multi-Grained Retrieval (FarmMGR) framework for image-text alignment. FarmMGR jointly exploits global scene semantics and local region-level cues to capture both large-scale landscape patterns and fine-grained farmland characteristics. To further improve semantic alignment, we design Prototype-Based Semantic Alignment (PBSA) to alleviate the soft-positive sample problem, Global Semantics Hierarchical Feature Integration (GSHFI) to adaptively fuse global and local representations, and Instance-Level Semantic Relation Modeling (ISRM) to capture relational dependencies among farmland instances. In addition, Memory-Enhanced Cross-Modal Contrastive Learning (MECCL) enlarges the negative sample pool and improves representation discrimination. Extensive experiments on FRS-CMR show that FarmMGR achieves a mean recall of 28.57%, outperforming existing image-text retrieval methods and demonstrating its effectiveness for language-driven farmland remote sensing retrieval.","低空农田遥感(FRS)已成为精准农业监测的重要工具。然而，现有农田遥感方法主要针对预定义类别的分割任务设计，难以进行语义理解，也无法支持灵活的自然语言查询。为解决这一局限，我们引入了一项基于人工智能的任务——农田遥感跨模态检索，旨在利用文本描述从大规模遥感图像中检索目标农田区域。为支撑该任务，我们构建了首个农田遥感跨模态检索(FRS-CMR)数据集，包含67,806个通过多模态大语言模型生成并经专家精炼的图像-文本对。基于FRS-CMR，我们提出了面向农田的多粒度检索(FarmMGR)框架以实现图像-文本对齐。FarmMGR联合利用全局场景语义和局部区域级线索，以同时捕获大尺度景观格局和细粒度农田特征。为进一步提升语义对齐效果，我们设计了基于原型的语义对齐(PBSA)以缓解软正样本问题，全局语义层次特征融合(GSHFI)以自适应融合全局与局部表示，以及实例级语义关系建模(ISRM)以捕获农田实例间的关系依赖。此外，记忆增强跨模态对比学习(MECCL)扩大了负样本池并提升了表示判别能力。在FRS-CMR上的大量实验表明，FarmMGR取得了28.57%的平均召回率，优于现有图像-文本检索方法，证明了其在语言驱动农田遥感检索中的有效性。","Engineering Applications of Artificial Intelligence","2026-09-10T00:00:00Z",81,{"impact":17,"substance":191,"depth":17,"authority":19,"freshness":135,"relevant":21,"comment":192},22,"构建首个农田遥感跨模态检索数据集并提出多粒度对齐框架，方法新颖、数据规模可观，对语言驱动的精准农业监测有实质推动。",[194],{"name":187,"url":184},[26,27,29,30,196],"多模态大模型",[198,199],"农业人工智能 多模态大模型 智慧农业 精准农业","农业人工智能 多模态大模型","农业人工智能多模态大模型智慧农业精准农业-2165","10.1016\u002Fj.engappai.2026.116113",{"doi":201,"openalex_id":203,"authors":204,"venue":187,"cited_by_count":35,"oa_url":184,"card":224,"direction":121,"ingested_from":64},"W7212180335",[205,207,210,212,215,217,219,221],{"name":206,"orcid":9},"KeJian Yu",{"name":208,"orcid":209},"Wentao Ma","https:\u002F\u002Forcid.org\u002F0000-0002-2781-1693",{"name":211,"orcid":9},"Shichao Jin",{"name":213,"orcid":214},"Lu Liu","https:\u002F\u002Forcid.org\u002F0000-0002-3170-9376",{"name":216,"orcid":9},"Yuwei Wang",{"name":218,"orcid":9},"Weiwei Wang",{"name":220,"orcid":9},"Longzhe Quan",{"name":222,"orcid":223},"Lichuan Gu","https:\u002F\u002Forcid.org\u002F0000-0002-3768-8203",{"tldr":225,"method":226,"finding":227,"direction":121,"opportunity":228},"提出农田遥感图文跨模态检索任务，构建数据集并提出FarmMGR框架实现语言驱动检索。","构建67,806图文对FRS-CMR数据集，用多粒度对齐与记忆增强对比学习。","FarmMGR平均召回率28.57%，优于现有图文检索方法，验证语言驱动农田遥感检索可行。","可探索多模态大模型生成数据的噪声校正、跨区域泛化及细粒度农田语义检索基准。","2026-09-11T23:30:30.005240Z",{"id":231,"title":232,"url":233,"summary":234,"summary_zh":9,"content":9,"source_name":235,"source_url":9,"published_at":236,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":237,"score_detail":238,"sources":241,"tags":243,"search_phrases":246,"slug":249,"view_count":35,"doi":9,"paper":250,"created_at":258},3001,"UAV多光谱不同空间分辨率匹配春小麦多性状监测","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1811","天津师范大学张程程等联合天津市农科院农业资源与环境研究所，从原生0.07 m四波段UAV多光谱影像通过像素聚合重采样生成14种空间分辨率（0.07-3.03 m），耦合PROSAIL辐射传输模型与随机森林评估尺度依赖反演性能。研究揭示了叶面积指数（LAI）、叶绿素含量（Cab）和冠层水分含量（Cw）反演精度对空间分辨率的非单调响应，提出物候阶段自适应分辨率策略并开发Heterogeneity-Scale Game Model（HSGM）刻画最优聚合尺度形成机制。","MDPI Agronomy 16(18):1811","2026-09-15T00:00:00Z",74,{"impact":239,"substance":191,"depth":17,"authority":80,"freshness":67,"relevant":21,"comment":240},15,"方法新颖、数据扎实的作物遥感反演研究，对精准农业变量施药与无人机监测有参考价值，但属细分领域学术进展，公共影响有限。",[242],{"name":235,"url":233},[26,29,30,244,245],"作物表型","春小麦",[247,248],"天津师范大学 春小麦 多光谱","UAV 多光谱 空间分辨率","天津师范大学春小麦多光谱-3001",{"doi":9,"openalex_id":9,"authors":251,"venue":9,"cited_by_count":35,"oa_url":9,"card":252,"direction":121,"ingested_from":257},[],{"tldr":253,"method":254,"finding":255,"direction":121,"opportunity":256},"用无人机多光谱重采样14种分辨率，结合PROSAIL与随机森林，研究春小麦多性状反演的空间尺度效应。","UAV四波段多光谱像素聚合重采样，耦合PROSAIL模型与随机森林反演LAI、C","反演精度对空间分辨率呈非单调响应，提出物候自适应分辨率策略与HSGM模型。","可探索不同作物与物候下最优分辨率普适规律，并将尺度自适应策略嵌入实时无人机监测系统。","agent","2026-09-20T00:03:08.168753Z",{"id":260,"title":261,"url":262,"summary":263,"summary_zh":9,"content":9,"source_name":264,"source_url":9,"published_at":265,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":266,"score_detail":267,"sources":269,"tags":271,"search_phrases":274,"slug":277,"view_count":35,"doi":9,"paper":278,"created_at":285},2999,"基于改进DeepLabv3+的高标准农田田间道路提取与结构指标量化框架","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1986","沈阳农业大学刘永生等开发了基于MobileNetV2改进DeepLabv3+的高标准农田田间道路提取轻量化框架，集成Normalization-based Attention Module与Content-Aware ReAssembly of FEatures。三次随机种子训练平均mIoU 93.34%、mPA 96.75%、精度98.90%，模型参数6.14M、推理速度17.04 FPS；沥青、混凝土、砾石道路宽度预测R²分别为0.650、0.486、0.662，宽度MAE 0.130\u002F0.140\u002F0.100 m。第二验证区域连通性指数从0.4682提升至0.4795，支持高标准农田田间道路高效、可量化、可追溯的验收检查。","MDPI Agriculture 16(18):1986","2026-09-16T00:00:00Z",77,{"impact":79,"substance":191,"depth":17,"authority":80,"freshness":20,"relevant":21,"comment":268},"方法有创新、指标详实，对高标准农田道路验收有实用价值，但属细分技术论文，影响面有限。",[270],{"name":264,"url":262},[26,27,272,30,273],"高标准农田","田间道路",[275,276],"沈阳农业大学 高标准农田 道路提取","DeepLabv3 田间道路 遥感","沈阳农业大学高标准农田道路提取-2999",{"doi":9,"openalex_id":9,"authors":279,"venue":9,"cited_by_count":35,"oa_url":9,"card":280,"direction":121,"ingested_from":257},[],{"tldr":281,"method":282,"finding":283,"direction":121,"opportunity":284},"提出改进DeepLabv3+轻量框架，提取高标准农田田间道路并量化结构指标。","MobileNetV2+NAM+CARAFE改进DeepLabv3+，多区域遥感","mIoU 93.34%，道路宽度预测R²最高0.662，连通性指数提升至0.4795。","可拓展至多作物、多地形道路提取，并结合时序遥感实现道路损毁动态监测。","2026-09-20T00:03:08.023498Z",{"id":287,"title":288,"url":289,"summary":290,"summary_zh":291,"content":9,"source_name":292,"source_url":289,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":76,"score_detail":293,"sources":295,"tags":297,"search_phrases":300,"slug":303,"view_count":35,"doi":304,"paper":305,"created_at":322},2963,"A comprehensive review of deep learning methods for weed classification in precision agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-01916-7","Weeds today are among the factors contributing to low agricultural productivity. As the world’s population continues to grow, there is an urgent need to meet global food demand. Nigeria currently lacks sufficient crop production to feed its growing population, and weeds are among the core contributors to poor agricultural yield. This study conducts a comprehensive review of Deep Learning (DL) approaches to weed classification in precision agriculture, covering literature from 2018 to 2025, was carried out. We employed a mix of quantitative and qualitative methods in the course of this review paper. Our data source is centred on Scopus-indexed papers, published with Sensors, Electronics, and Agriculture in MDPI as well as IEEE, Thomson Reuters, and Springer. The study systematically reviewed and analysed machine learning (ML), DL, and instance segmentation techniques to identify the key technological and environmental barriers, such as data limitations, class imbalance, environmental variability, and model scalability issues that affect the effectiveness and efficiency of these models when deployed in real time. These findings show that while weed management models like the YOLO variants, ResNet, and Vision Transformers achieved high accuracy in training and testing, they are associated with several challenges in their real world-deployment, such as occlusion, small object detection, and environmental adaptability. Overall, this research provides recommended solutions to enhance model robustness, scalability, and efficiency. It further provides a summary of the current state and future directions for AI-driven weed management.","杂草是当前导致农业生产力低下的因素之一。随着世界人口持续增长，满足全球粮食需求已成为迫切任务。尼日利亚目前的作物产量不足以养活其不断增长的人口，而杂草是导致农业产量低下的核心因素之一。本研究对精准农业中基于深度学习（Deep Learning，DL）的杂草分类方法进行了全面综述，涵盖2018年至2025年的文献。在综述过程中，我们采用了定量与定性相结合的方法。数据来源集中于Scopus索引论文，这些论文发表于MDPI旗下的Sensors、Electronics和Agriculture，以及IEEE、Thomson Reuters和Springer。本研究系统综述并分析了机器学习（Machine Learning，ML）、深度学习及实例分割技术，以识别影响这些模型实时部署效果与效率的关键技术和环境障碍，如数据局限性、类别不平衡、环境变异性及模型可扩展性问题。研究结果表明，尽管YOLO系列、ResNet和视觉Transformer（Vision Transformer）等杂草管理模型在训练和测试中达到了较高精度，但在实际部署中仍面临诸多挑战，如遮挡、小目标检测和环境适应性等问题。总体而言，本研究提出了增强模型鲁棒性、可扩展性和效率的推荐解决方案，并进一步总结了人工智能驱动杂草管理的现状与未来方向。","Discover Artificial Intelligence",{"impact":78,"substance":17,"depth":79,"authority":80,"freshness":20,"relevant":21,"comment":294},"系统综述2018—2025年深度学习杂草分类方法，指出遮挡、小目标与环境适应性等落地瓶颈，对农业AI研究有参考价值，但属综述类论文、非突破性成果。",[296],{"name":292,"url":289},[26,27,298,299,29],"深度学习","杂草识别",[301,302],"深度学习 杂草分类 精准农业","YOLO 杂草识别 模型部署","深度学习杂草分类精准农业-2963","10.1007\u002Fs44163-026-01916-7",{"doi":304,"openalex_id":306,"authors":307,"venue":292,"cited_by_count":35,"oa_url":289,"card":316,"direction":320,"ingested_from":64},"W7213558223",[308,310,312,314],{"name":309,"orcid":9},"Njoku Camillus Ekene",{"name":311,"orcid":9},"Francis A. Okoye",{"name":313,"orcid":9},"Ebere Uzoka Chidi",{"name":315,"orcid":9},"OGBU MARY NNENNA",{"tldr":317,"method":318,"finding":319,"direction":320,"opportunity":321},"综述2018-2025年深度学习杂草分类方法，分析技术瓶颈并给出改进建议。","混合定量定性法，基于Scopus及MDPI、IEEE等文献，分析ML、DL与实例","YOLO、ResNet、ViT等精度高，但实际部署受遮挡、小目标与环境适应性限制。","农业人工智能与决策模型","可研究轻量化、跨域自适应模型，解决小目标与遮挡下的实时杂草识别难题。","2026-09-19T23:30:56.875661Z"]