[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-73":3,"related-73":57},{"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":18,"tags":20,"primary_topic":8,"secondary_topics":28,"classification":29,"search_phrases":51,"slug":54,"view_count":55,"doi":8,"paper":8,"created_at":56},73,"Context-Aware Concept Distillation：基于上下文感知的可解释洪涝预测框架（arXiv:2607.23237，2026-07-25提交，预印本）","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.23237","Levinkopf E、Morin E 与 Goldman CV 在 arXiv 提交预印本\"Context-Aware Concept Distillation for Trustworthy Flood Prediction\"（arXiv:2607.23237 [cs.LG]，2026-07-25提交）。研究面向公共安全高风险场景下深度学习\"黑箱\"阻碍信任与问责的难题，提出 Context-Aware Concept Distillation（CACD）框架，将 LSTM 蒸馏为可解释的水文学感知代理模型：通过无监督流程发现\"水文学语言\"，并用残差超网络（Residual Hypernetwork）根据静态流域特征动态调节这些概念。在全球 5,203 个流域上，模型中位 NSE 0.70，显著优于多层感知机等黑箱基线在未见过未来数据上的表现。",null,"[View PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2607.23237)[HTML (experimental)](https:\u002F\u002Farxiv.org\u002Fhtml\u002F2607.23237v1)\n\n> Abstract:Effective flood risk management relies on accurate forecasting, yet the \"black box\" nature of stateof-the-art Deep Learning models creates a barrier to trust and accountability in high-stakes public safety decisions. While existing Explainable AI (XAI) methods offer local attributions, they fail to provide the verifiable, operationally meaningful causal narratives required by disaster response authorities. To address this societal challenge, we propose Context-Aware Concept Distillation (CACD), a framework developed in collaboration with domain experts to distill opaque LSTMs into interpretable, hydrology-aware surrogate models. We introduce an unsupervised pipeline to discover a \"Hydrological Language\" and a Residual Hypernetwork that dynamically modulates these concepts based on static basin characteristics. Evaluated on 5,203 basins globally, our model achieves high fidelity (Median NSE 0.70), significantly outperforming black-box baselines (e.g., Multi Layer Perceptrons) on unseen future data. By demonstrating that human-interpretable concepts are sufficient to reconstruct flood dynamics, this work balances AI accuracy with the transparency required for responsible environmental decision-making.\n\nComments:to be published in IJCAI 2026 proceedings\nSubjects:Machine Learning (cs.LG); Artificial Intelligence (cs.AI)\nCite as:[arXiv:2607.23237](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.23237) [cs.LG]\n(or [arXiv:2607.23237v1](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.23237v1) [cs.LG] for this version)\n[https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2607.23237](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2607.23237)\n\narXiv-issued DOI via DataCite (pending registration)\n\n## Submission history\n\nFrom: Claudia Goldman [[view email](https:\u002F\u002Farxiv.org\u002Fshow-email\u002F51d9175a\u002F2607.23237)] \n\n**[v1]** Sat, 25 Jul 2026 14:45:26 UTC (2,120 KB)","arXiv · 2026-07-25","2026-07-25T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"论文主题为洪涝预测，不涉及三农、农业信息化、智慧农业、数字乡村或农业科技，不相关。",[19],{"name":10,"url":6},[12,21,22,23,24,25,26,27],"预印本","arXiv","洪涝预测","可解释AI","概念蒸馏","LSTM","水文学",[],{"status":30,"locked":14,"version":31,"method":32,"evidence":33},"needs_review","2026-10-22.2","rules",{"candidates":34,"agricultural_evidence":14,"reason":50},[35,43],{"code":36,"score":37,"supported":38,"title":39,"abstract":41,"tags":42},"H7",8,true,[40],"洪涝",[],[],{"code":44,"score":45,"supported":38,"title":46,"abstract":47,"tags":49},"I2",4,[],[48],"深度学习",[24],"农业相关性需要复核",[52,53],"概念蒸馏 洪涝预测 可解释AI 水文学","概念蒸馏 洪涝预测","概念蒸馏洪涝预测可解释AI水文学-73",9,"2026-07-29T23:54:14.329807Z",{"total":58,"page":59,"page_size":58,"items":60},6,1,[61,112,165,221,300,349],{"id":62,"title":63,"url":64,"summary":65,"summary_zh":8,"content":66,"source_name":67,"source_url":8,"published_at":68,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":69,"score_detail":70,"sources":77,"tags":79,"primary_topic":84,"secondary_topics":90,"classification":91,"search_phrases":107,"slug":110,"view_count":13,"doi":8,"paper":8,"created_at":111},273,"ST-LoRA: Single Trajectory LoRA Ensemble for Uncertainty Aware Agricultural Segmentation","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.01530","2026年8月2日arXiv上线cs.CV预印本ST-LoRA,提出面向农业不确定性感知语义分割的LoRA高效集成框架。研究针对数字农业决策支持对预测精度与不确定性校准的双重需求,以及集成方法计算与内存开销大的瓶颈,提出在单训练轨迹下构建多样化集成成员:每个成员共享冻结预训练骨干网络,仅通过轻量级低秩适配器(LoRA)差异,训练参数控制在全模型的10%以内,保持集成多样性。实验采用两套农业数据集:GrowliFlower-L(花椰菜,露天)和BUP20(甜椒,温室),覆盖分布内性能、分布偏移下的校准以及分布外检测。研究发现,前馈层而非注意力层是密集预测场景下LoRA的关键靶点(颠覆语言模型仅关注注意力的惯例),ST-LoRA在分割精度与校准上匹配或超越全秩集成,且显著降低训练时间、推理延迟、内存占用与存储需求;优于快照集成、MC Dropout与深度确定性不确定性等高效基线。","[View PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2608.01530)[HTML (experimental)](https:\u002F\u002Farxiv.org\u002Fhtml\u002F2608.01530v1)\n\n> Abstract:Reliable decision-support in digital agriculture requires accurate predictions and well-calibrated uncertainty estimates, particularly for dense prediction tasks such as semantic segmentation. Ensemble methods provide strong uncertainty quantification, but their computational and memory demands limit practical use, while single-model approximations often trade off uncertainty quality for efficiency. We propose ST-LoRA, a parameter-efficient ensemble framework that builds diverse ensemble members from a single training trajectory by combining Low-Rank Adaptation (LoRA) with snapshot ensembling. Each member shares a frozen pretrained backbone and differs only in lightweight low-rank adapters, reducing trainable parameters to under 10% of the full model while preserving ensemble diversity. We evaluate across two agricultural datasets - GrowliFlower-L (cauliflower, open field) and BUP20 (sweet pepper, glasshouse) - using SegFormer and Mask2Former, covering in-distribution performance, calibration under distribution shift, and out-of-distribution detection. Ablations show feed-forward layers, not attention layers, are the critical LoRA target for dense prediction, contrary to the attention-only convention from language models. ST-LoRA matches or exceeds full-rank ensembles in segmentation accuracy and calibration across both datasets and architectures, while substantially reducing training time, inference latency, memory footprint, and storage requirements. Against efficient baselines - Snapshot Ensemble, MC Dropout, and Deep Deterministic Uncertainty - ST-LoRA consistently matches or outperforms them in image\u002Fpixel-level OoD detection, calibration stability under shift, and cross-seed variance, with far fewer parameters and lower compute. These results show LoRA-efficient ensemble adaptation is a highly effective, practical approach for uncertainty-aware agricultural vision systems.\n\nComments:Submitted to Computers and Electronics in Agriculture (Elsevier). Currently under review (first revision round)\nSubjects:Computer Vision and Pattern Recognition (cs.CV)\nCite as:[arXiv:2608.01530](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.01530) [cs.CV]\n(or [arXiv:2608.01530v1](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.01530v1) [cs.CV] for this version)\n[https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2608.01530](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2608.01530)\n\narXiv-issued DOI via DataCite\n\n## Submission history\n\nFrom: Mohamed Farag [[view email](https:\u002F\u002Farxiv.org\u002Fshow-email\u002F7540b145\u002F2608.01530)] \n\n**[v1]** Sun, 2 Aug 2026 22:49:28 UTC (22,114 KB)","arXiv 2608.01530 (cs.CV) 2026-08-02","2026-08-02T00:00:00Z",77,{"impact":71,"substance":72,"depth":73,"authority":74,"freshness":75,"relevant":59,"comment":76},18,22,20,12,5,"提出ST-LoRA方法，在农业语义分割中实现高效不确定性估计，方法新颖且实验扎实，对农业AI落地有实际价值。",[78],{"name":67,"url":64},[21,22,80,81,82,83],"ST-LoRA","农业语义分割","不确定性校准","LoRA集成",{"code":85,"slug":86,"name":87,"path":88,"parent_code":89},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[],{"status":92,"locked":14,"version":31,"method":32,"evidence":93},"classified",{"candidates":94,"agricultural_evidence":38},[95,101],{"code":85,"score":96,"supported":38,"title":97,"abstract":98,"tags":100},3,[],[99],"温室",[],{"code":102,"score":96,"supported":38,"title":103,"abstract":104,"tags":106},"I1",[],[105],"数字农业",[],[108,109],"不确定性校准 农业语义分割 预印本 LoRA集成","不确定性校准 农业语义分割","不确定性校准农业语义分割预印本LoRA集成-273","2026-08-07T00:18:31.362185Z",{"id":113,"title":114,"url":115,"summary":116,"summary_zh":8,"content":117,"source_name":118,"source_url":8,"published_at":119,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":69,"score_detail":120,"sources":122,"tags":124,"primary_topic":129,"secondary_topics":135,"classification":136,"search_phrases":160,"slug":163,"view_count":37,"doi":8,"paper":8,"created_at":164},244,"Design and Evaluation of an AI-Enabled Cloud-Edge Architecture for Connected Precision Agriculture Farms","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.03816","arXiv 2026年8月4日上线的预印本,提出一种面向互联精准农场的AI驱动云边架构。研究针对番茄早疫病、晚疫病、叶霉病等重大病害导致严重减产的问题,集成物联网传感器、无人机、深度学习、Azure IoT Hub云端分析与多平台(移动应用、Web应用、嵌入式边缘设备平台)接口,实现番茄病害实时检测。模型基于PlantVillage、Kaggle等公开数据集训练,通过TensorFlow模型部署至移动、Web与边缘设备平台。实验结果显示检测有效性达92-95%,在不同环境与设备平台表现一致,显著降低人工巡检依赖,实现及时干预,支撑可持续互联精准农场发展。","[Skip to main content](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.03816#content)\n\n![Image 1](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ficons\u002Fsmileybones-small.svg)System maintenance August 4th and 5th[Learn more](https:\u002F\u002Fstatus.arxiv.org\u002F)×\n\n[](https:\u002F\u002Farxiv.org\u002FIgnoreMe)[![Image 2: 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 > Robotics\n\n**arXiv:2608.03816** (cs) \n\n [Submitted on 4 Aug 2026]\n\n# Title:Design and Evaluation of an AI-Enabled Cloud-Edge Architecture for Connected Precision Agriculture Farms\n\nAuthors:[Deckshitha Angadi](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Angadi,+D), [Koteshwar Goud Surga](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Surga,+K+G), [Nagaraju Lakkaraju](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Lakkaraju,+N), [Naveena Budda](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Budda,+N), [Vikas Agarwal](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Agarwal,+V), [Giridhara Venkata Ram Raj Mulasa](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Mulasa,+G+V+R+R), [Ravi Killamsetty](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Killamsetty,+R), [Chandrasekhara Sarma Mallubhotla](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Mallubhotla,+C+S), [Narsimlu Kemsaram](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Kemsaram,+N)\n\nView a PDF of the paper titled Design and Evaluation of an AI-Enabled Cloud-Edge Architecture for Connected Precision Agriculture Farms, by Deckshitha Angadi and 8 other authors\n\n[View PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2608.03816)\n> Abstract:Plant diseases cause significant yield losses worldwide, with tomato crops particularly susceptible to early blight, late blight, and leaf mold. Manual monitoring is practical only for small-scale farms and becomes unmanageable at larger scales. To tackle this limitation, an artificial intelligence (AI) enabled cloud-edge architecture is proposed for autonomous crop monitoring. This proposed architecture integrates Internet of Things (IoT) sensors, unmanned aerial vehicles (UAVs), deep learning, Azure IoT Hub-based cloud analytics, and multi-platform (mobile app, web app, and embedded edge device platform) interfaces to enable real-time detection of tomato diseases. For training and validation, we used publicly available datasets, such as PlantVillage and Kaggle. A TensorFlow model trained on a collected dataset is deployed across mobile, web, and edge-device platforms. Experimental results show detection effectiveness around 92-95%, with consistent performance over diverse environments and device platforms. The proposed system improves disease detection effectiveness, lowers dependence on manual inspection, and enables prompt interventions, thereby supporting sustainable, connected precision agriculture farms.\n\nComments:Accepted for presentation at the 2026 IEEE International Conference on Sustainable AI for Social Impact and Global Development (SASIGD 2026), Hyderabad, India, 13-14 August 2026\nSubjects:Robotics (cs.RO)\nCite as:[arXiv:2608.03816](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.03816) [cs.RO]\n(or [arXiv:2608.03816v1](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.03816v1) [cs.RO] for this version)\n[https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2608.03816](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2608.03816)\n\nFocus to learn more\n\n arXiv-issued DOI via DataCite (pending registration)\n\n## Submission history\n\n From: Narsimlu Kemsaram Dr [[view email](https:\u002F\u002Farxiv.org\u002Fshow-email\u002Fccb32526\u002F2608.03816)] \n\n**[v1]** Tue, 4 Aug 2026 15:25:02 UTC (16,022 KB)\n\n[](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.03816)Full-text links:\n## Access Paper:\n\n View a PDF of the paper titled Design and Evaluation of an AI-Enabled Cloud-Edge Architecture for Connected Precision Agriculture Farms, by Deckshitha Angadi and 8 other authors\n\n*   [View PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2608.03816)\n*   [TeX Source](https:\u002F\u002Farxiv.org\u002Fsrc\u002F2608.03816)\n\n[view license](http:\u002F\u002Farxiv.org\u002Flicenses\u002Fnonexclusive-distrib\u002F1.0\u002F \"Rights to this article\")\n\n### Current browse context:\n\ncs.RO\n\n[\u003Cprev](https:\u002F\u002Farxiv.org\u002Fprevnext?id=2608.03816&function=prev&context=cs.RO \"previous in cs.RO (accesskey p)\") | [next>](https:\u002F\u002Farxiv.org\u002Fprevnext?id=2608.03816&function=next&context=cs.RO \"next in cs.RO (accesskey n)\")\n\n[new](https:\u002F\u002Farxiv.org\u002Flist\u002Fcs.RO\u002Fnew) | 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[**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\u002F2608.03816) | [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 2608.03816 (cs.RO) 2026-08-04","2026-08-04T00:00:00Z",{"impact":71,"substance":73,"depth":71,"authority":74,"freshness":55,"relevant":59,"comment":121},"论文提出AI云边架构用于精准农业番茄病害检测，方法新颖，数据可靠，具有产业应用价值。",[123],{"name":118,"url":115},[21,125,22,48,126,127,128],"物联网","精准农业","云边协同","番茄病害",{"code":130,"slug":131,"name":132,"path":133,"parent_code":134},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":92,"locked":14,"version":31,"method":32,"evidence":137},{"candidates":138,"agricultural_evidence":38},[139,144,150,156],{"code":102,"score":55,"supported":38,"title":140,"abstract":142,"tags":143},[141],"precision agriculture",[],[126],{"code":130,"score":145,"supported":38,"title":146,"abstract":147,"tags":149},7,[],[125,148],"IoT",[125],{"code":151,"score":45,"supported":38,"title":152,"abstract":153,"tags":155},"B1",[],[154],"番茄",[128],{"code":44,"score":45,"supported":38,"title":157,"abstract":158,"tags":159},[],[48],[48],[161,162],"云边协同 深度学习 番茄病害 精准农业","云边协同 深度学习","云边协同深度学习番茄病害精准农业-244","2026-08-05T23:53:02.401550Z",{"id":166,"title":167,"url":168,"summary":169,"summary_zh":8,"content":170,"source_name":171,"source_url":8,"published_at":172,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":173,"score_detail":174,"sources":177,"tags":179,"primary_topic":185,"secondary_topics":191,"classification":198,"search_phrases":216,"slug":219,"view_count":202,"doi":8,"paper":8,"created_at":220},72,"CascadeCropNet：基于安全级联推理的农作物损伤评估——肯尼亚小农户玉米保险图像应用（arXiv:2607.25468，2026-07-28提交，预印本）","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.25468","Hagbe JTM、Gounou GK 与 Zimé SK 在 arXiv 提交预印本\"Safety-Aware Cascaded Inference for Crop Damage Assessment with Controlled Error Trade-offs\"（arXiv:2607.25468 [cs.CV]，2026-07-28提交）。针对基于图像的农业保险中漏检成本远高于误报的非对称代价结构，提出 CascadeCropNet 两阶段级联架构：通过阈值选择满足目标召回约束（Rec-Damaged ≥ 0.95），轻量 Sentinel 模型先做二分类健康分诊，超过损伤概率阈值 τ 的样本升级到专家 Expert 模型做精细诊断。在 Eyes on the Ground 数据集（肯尼亚小农户玉米田 23,804 张图像）上，τ=0.5 时 Rec-Damaged 达 0.974，相对单模型基线漏检减少最多54%。注：该方法可靠性取决于阈值与部署条件，并非任意分布漂移下的绝对保证。","[View PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2607.25468)[HTML (experimental)](https:\u002F\u002Farxiv.org\u002Fhtml\u002F2607.25468v1)\n\n> Abstract:In picture-based agricultural insurance for smallholder farmers, missed damage detections carry substantially higher cost than false alarms: a farmer who sustained real losses receives no payout, while unnecessary expert review is operationally costly but reversible. Standard multi-class classifiers optimize global accuracy but provide no mechanism to operationalize or control this asymmetric cost structure at inference time. We propose CascadeCropNet, a two-stage cascade architecture calibrated to satisfy a target recall constraint (Rec-Damaged >= 0.95) through threshold selection. A lightweight Sentinel model performs binary health triage; samples exceeding a calibrated damage probability threshold tau are escalated to a specialist Expert model for fine-grained diagnosis. This design provides explicit, deployment-time control over the safety-efficiency trade-off without retraining. Evaluated on the Eyes on the Ground dataset (23,804 images from Kenyan smallholder maize farms), the cascade achieves Rec-Damaged = 0.974 at tau = 0.5, reducing missed damage cases by up to 54% relative to a flat baseline. Under evaluation alignment, the representational gap reduces to +0.008 F1-macro, confirming the contribution is architectural rather than representational. Under input degradation, the system prioritizes escalation over confident misclassification, reflecting error containment through architectural isolation rather than intrinsic model robustness. These results demonstrate that cascade architectures can operationalize safety-oriented decision constraints through calibrated routing in settings where reliability matters more than aggregate accuracy. These properties depend on threshold calibration and deployment conditions and do not constitute guarantees under arbitrary distribution shift.\n\nComments:44 pages, 7 figures, 12 tables. Submitted to Precision Agriculture, July 2026\nSubjects:Computer Vision and Pattern Recognition (cs.CV)\nCite as:[arXiv:2607.25468](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.25468) [cs.CV]\n(or [arXiv:2607.25468v1](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.25468v1) [cs.CV] for this version)\n[https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2607.25468](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2607.25468)\n\narXiv-issued DOI via DataCite (pending registration)\n\n## Submission history\n\nFrom: Jose Thiery Messigbede Hagbe [[view email](https:\u002F\u002Farxiv.org\u002Fshow-email\u002F242aacb2\u002F2607.25468)] \n\n**[v1]** Tue, 28 Jul 2026 09:01:27 UTC (507 KB)","arXiv · 2026-07-28","2026-07-28T00:00:00Z",80,{"impact":71,"substance":72,"depth":71,"authority":175,"freshness":55,"relevant":59,"comment":176},13,"提出CascadeCropNet级联架构，针对小农户玉米保险图像损伤评估，显著降低漏检率，方法新颖且数据规模大，对农业保险精准理赔有重要参考价值。",[178],{"name":171,"url":168},[12,21,180,181,182,183,184,22],"CascadeCropNet","安全级联推理","农作物损伤","农业保险","肯尼亚玉米",{"code":186,"slug":187,"name":188,"path":189,"parent_code":190},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[192],{"code":193,"slug":194,"name":195,"path":196,"parent_code":197},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",{"status":92,"locked":14,"version":31,"method":32,"evidence":199},{"candidates":200,"agricultural_evidence":38},[201,206,211],{"code":186,"score":202,"supported":38,"title":203,"abstract":204,"tags":205},11,[188],[188],[],{"code":193,"score":202,"supported":38,"title":207,"abstract":209,"tags":210},[208],"小农户",[208],[],{"code":212,"score":45,"supported":38,"title":213,"abstract":214,"tags":215},"G3",[],[183],[183],[217,218],"安全级联推理 农作物损伤 肯尼亚玉米 农业保险","安全级联推理 农作物损伤","安全级联推理农作物损伤肯尼亚玉米农业保险-72","2026-07-29T23:54:14.318832Z",{"id":222,"title":223,"url":224,"summary":225,"summary_zh":226,"content":8,"source_name":227,"source_url":224,"published_at":228,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":229,"score_detail":230,"sources":234,"tags":236,"primary_topic":241,"secondary_topics":245,"classification":251,"search_phrases":274,"slug":277,"view_count":15,"doi":278,"paper":279,"created_at":299},5320,"Machine Learning for Drought Analysis and Forecasting: A Critical Review of Data Fusion, Extreme-Event Sensitivity, Explainability and Generalization","https:\u002F\u002Fdoi.org\u002F10.17485\u002Fijst\u002Fv19i36.1263","Background: Machine learning is increasingly applied to drought monitoring and forecasting, but studies differ widely in how drought is defined, what data are used, and how models are validated, which makes reported performance difficult to compare directly. Objectives: To explain why reported machine-learning drought-model performance varies across definitions, data sources and validation schemes, and to assess whether recent studies adequately address rare severe events and cross-domain transferability. Method: Journal articles published January 2018 to August 2026 were reviewed, starting from a 70-record high-relevance subset of a 359-record discovery export and extended through citation chaining. Publisher and DOI verification produced a final evidence base of 55 articles: 48 primary studies and 7 reviews. Target definition, input data, model family, validation design, metrics, class-imbalance treatment, explainability and external testing were compared across studies. Findings: Reported performance varied with the modelling task rather than with any single algorithm: Random Forest reached 97.7% AUC-ROC for agricultural-drought mapping in Queensland, SVM reached average R²=0.90 for hydrological drought in Algeria, and a CatBoost stacking model reached R²=0.9065 for remote-sensing-based drought monitoring. These scores are not directly comparable because targets, data and validation protocols differ. Recent studies increasingly test cross-product and cross-region transfer, use SHAP\u002FLIME explainability, and adopt extreme-aware losses, yet class-wise reporting of severe and extreme drought, ordinal-aware evaluation, and leakage-resistant temporal validation remain inconsistently applied across the 48 primary studies. Novelty: The review jointly evaluates predictive accuracy alongside rare-event sensitivity, ordinal drought severity, leakage-resistant validation, explainability, uncertainty and cross-domain generalization, criteria largely absent from earlier machine-learning drought reviews. Keywords: Drought Forecasting, Machine Learning, Remote Sensing, Explainable AI, Extreme Drought","背景：机器学习正日益应用于干旱监测与预报，但各研究在干旱定义、所用数据和模型验证方式上差异很大，导致所报告的模型性能难以直接比较。目标：解释为何已报告的机器学习干旱模型性能会因定义、数据来源和验证方案的不同而变化，并评估近期研究是否充分处理了罕见的严重事件和跨域可迁移性问题。方法：综述了2018年1月至2026年8月发表的期刊论文，起始于359条发现导出记录中70条高相关性记录的子集，并通过引文追溯加以扩展。经出版商和DOI核验，最终证据基础为55篇文章：48篇原始研究和7篇综述。比较了各研究的目标定义、输入数据、模型族、验证设计、指标、类别不平衡处理、可解释性和外部测试。发现：所报告的性能随建模任务而非任何单一算法而变化：随机森林在昆士兰农业干旱制图中达到97.7%的AUC-ROC，SVM在阿尔及利亚水文干旱中达到平均R²=0.90，CatBoost堆叠模型在基于遥感的干旱监测中达到R²=0.9065。这些分数不能直接比较，因为目标、数据和验证方案不同。近期研究越来越多地测试跨产品和跨区域迁移，使用SHAP\u002FLIME可解释性，并采用极端事件感知损失函数，然而在48篇原始研究中，严重和极端干旱的分类别报告、序数感知评估以及防泄漏的时间验证仍应用不一致。新颖性：本综述将预测准确性与罕见事件敏感性、序数干旱严重程度、防泄漏验证、可解释性、不确定性和跨域泛化等标准共同评估，这些标准在以往的机器学习干旱综述中大多缺失。关键词：干旱预报，机器学习，遥感，可解释人工智能，极端干旱","Indian Journal of Science and Technology","2026-10-18T00:00:00Z",71,{"impact":74,"substance":231,"depth":232,"authority":74,"freshness":55,"relevant":59,"comment":233},21,17,"系统综述机器学习干旱分析与预测，指出评估口径不一、极端事件敏感性与可解释性不足，对农业旱情监测信息化有参考价值，但属学术综述、影响面偏细分领域。",[235],{"name":227,"url":224},[237,24,238,239,240],"农业人工智能","极端天气","遥感","干旱监测",{"code":44,"slug":242,"name":243,"path":244,"parent_code":134},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[246],{"code":36,"slug":247,"name":248,"path":249,"parent_code":250},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":92,"locked":14,"version":31,"method":32,"evidence":252},{"candidates":253,"agricultural_evidence":38},[254,261,268],{"code":44,"score":255,"supported":38,"title":256,"abstract":258,"tags":260},16,[257],"machine learning",[259,257],"机器学习",[237,24],{"code":36,"score":262,"supported":38,"title":263,"abstract":265,"tags":267},15,[264],"drought",[266,264],"干旱",[240],{"code":269,"score":145,"supported":38,"title":270,"abstract":271,"tags":273},"I3",[],[239,272],"remote sensing",[239],[275,276],"机器学习 干旱预测","遥感 干旱监测","机器学习干旱预测-5320","10.17485\u002Fijst\u002Fv19i36.1263",{"doi":278,"openalex_id":280,"authors":281,"venue":227,"cited_by_count":15,"oa_url":224,"card":291,"direction":297,"ingested_from":298},"W7222882764",[282,285,288],{"name":283,"orcid":284},"Ritik Mallik","https:\u002F\u002Forcid.org\u002F0009-0000-2785-0265",{"name":286,"orcid":287},"Dayal Kumar Behera","https:\u002F\u002Forcid.org\u002F0000-0002-9740-8066",{"name":289,"orcid":290},"Satarupa Mohanty","https:\u002F\u002Forcid.org\u002F0000-0002-1434-7158",{"tldr":292,"method":293,"finding":294,"direction":295,"opportunity":296},"综述55篇文献，比较机器学习干旱监测预报在定义、数据与验证上的差异。","系统综述2018-2026年55篇文献，比较目标定义、数据、模型、验证与可解释性","模型性能差异主要源于任务与验证设计而非算法，极端事件与泄漏防护评估仍不足。","农业人工智能与决策模型","可构建面向极端干旱的序数感知评估与抗泄漏时序验证框架，提升跨区域泛化。","农业遥感与作物表型","openalex","2026-10-19T23:30:22.840943Z",{"id":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":8,"source_name":306,"source_url":303,"published_at":307,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":308,"sources":310,"tags":312,"primary_topic":8,"secondary_topics":314,"classification":315,"search_phrases":325,"slug":328,"view_count":59,"doi":329,"paper":330,"created_at":348},5151,"XAI and its Applications in Decision Support System","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85188","Artificial Intelligence (AI) has transformed decision-making across various industries by enabling systems to analyse large volumes of data and provide intelligent recommendations. However, many modern AI models, particularly deep learning algorithms, function as \"black boxes,\" making it difficult for users to understand how decisions are generated. This lack of transparency limits the adoption of AI in critical domains such as healthcare, finance, law, and business. Explainable Artificial Intelligence (XAI) has emerged as a promising solution that improves transparency, interpretability, and trust in AI systems. Decision Support Systems (DSS) assist decision-makers by combining data, analytical models, and domain knowledge. Integrating XAI into DSS enables users to understand AI-generated recommendations, thereby improving confidence and accountability. This review paper explores the concepts of XAI, different explanation techniques, and their applications in DSS. The paper also discusses recent research, challenges, limitations, and future research directions","人工智能（Artificial Intelligence, AI）通过使系统能够分析大量数据并提供智能建议，已经改变了各行各业的决策方式。然而，许多现代AI模型，尤其是深度学习算法，作为“黑箱”运作，使用户难以理解决策是如何生成的。这种透明度的缺乏限制了AI在医疗、金融、法律和商业等关键领域的应用。可解释人工智能（Explainable Artificial Intelligence, XAI）作为一种有前景的解决方案应运而生，旨在提高AI系统的透明度、可解释性和信任度。决策支持系统（Decision Support Systems, DSS）通过结合数据、分析模型和领域知识来辅助决策者。将XAI集成到DSS中，使用户能够理解AI生成的建议，从而提高信心和问责性。本文综述探讨了XAI的概念、不同的解释技术及其在DSS中的应用。本文还讨论了近期研究、挑战、局限性以及未来研究方向。","International Journal for Research in Applied Science and Engineering Technology","2026-10-16T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":309},"该文为通用XAI与决策支持系统综述，未涉及农业场景，与三农及农业信息化无直接关联，不建议进入每日精选。",[311],{"name":306,"url":303},[237,24,313],"决策支持系统",[],{"status":30,"locked":14,"version":31,"method":32,"evidence":316},{"candidates":317,"agricultural_evidence":14,"reason":50},[318],{"code":44,"score":73,"supported":38,"title":319,"abstract":321,"tags":324},[320],"decision support",[48,322,323,320],"deep learning","artificial intelligence",[237,313,24],[326,327],"农业人工智能 决策支持系统 可解释AI","农业人工智能 决策支持系统","农业人工智能决策支持系统可解释AI-5151","10.22214\u002Fijraset.2026.85188",{"doi":329,"openalex_id":331,"authors":332,"venue":306,"cited_by_count":15,"oa_url":303,"card":342,"direction":347,"ingested_from":298},"W7222727160",[333,335,337,340],{"name":334,"orcid":8},"Gitin Garg",{"name":336,"orcid":8},"Kiya Jain",{"name":338,"orcid":339},"Vishal Shrivastava","https:\u002F\u002Forcid.org\u002F0000-0002-8353-2752",{"name":341,"orcid":8},"Akhil Pandey",{"tldr":343,"method":344,"finding":345,"direction":295,"opportunity":346},"综述可解释AI技术及其在决策支持系统中的应用、挑战与未来方向。","文献综述，梳理XAI解释技术与DSS集成应用。","XAI能提升DSS透明度与信任，但应用仍面临挑战与局限。","可将XAI引入农业决策支持系统，研究面向农户的可解释推荐与信任机制。","智慧农业 \u002F 农业物联网","2026-10-17T23:30:08.767848Z",{"id":350,"title":351,"url":352,"summary":353,"summary_zh":354,"content":8,"source_name":355,"source_url":352,"published_at":356,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":357,"score_detail":358,"sources":360,"tags":362,"primary_topic":365,"secondary_topics":366,"classification":367,"search_phrases":391,"slug":394,"view_count":15,"doi":395,"paper":396,"created_at":424},5080,"Explainable deep learning for Mango leaf disease recognition using AlexNet and grad-CAM","https:\u002F\u002Fdoi.org\u002F10.1177\u002F1088467x261495708","Early and accurate detection of plant diseases is essential for improving crop productivity and supporting sustainable agricultural practices. Mango (Mangifera indica), one of the most economically important tropical fruit crops worldwide, is highly susceptible to several leaf diseases that can significantly reduce yield and fruit quality. This study presents an explainable deep learning framework for automated ten-class mango leaf disease classification using a publicly available dataset containing approximately 5000 images distributed across Anthracnose, Bacterial Canker, Cutting Weevil, Dag Disease, Die Back, Galls, Healthy, Powdery Mildew, Red Rust, and Sooty Mould classes. Unlike conventional AlexNet-based approaches, the proposed framework integrates Bayesian hyperparameter optimization, two-stage fine-tuning, test-time augmentation (TTA), and Grad-CAM-based explainability to improve both predictive performance and interpretability. Experimental results demonstrate that the proposed framework achieves a final test accuracy of 99.2% using eight-view test-time augmentation (TTA), with corresponding precision, recall, and F1-score of approximately 99.1%. The model also achieves micro-average and macro-average ROC–AUC scores of 1.00 on the curated test set, indicating strong discriminative capability across the considered disease categories. Comparative evaluations against MobileNetV2, DenseNet201, ConvNeXt, and LeafNet demonstrate competitive or superior classification performance while maintaining moderate computational complexity and architectural simplicity. Furthermore, Grad-CAM visualizations qualitatively highlight biologically relevant disease symptoms, including lesions, necrotic regions, rust spots, fungal growth, and tissue discoloration, thereby enhancing model interpretability. Although field-scale validation and quantitative explainability assessment remain subjects for future investigation, the proposed framework provides an accurate, computationally efficient, and interpretable approach for automated mango leaf disease diagnosis with strong potential for smart agriculture and precision farming applications.","植物病害的早期准确检测对于提高作物生产力和支持可持续农业实践至关重要。芒果（Mangifera indica）是全球最具经济重要性的热带水果作物之一，极易感染多种叶部病害，这些病害可显著降低产量和果实品质。本研究提出了一种可解释的深度学习框架，用于自动化十类芒果叶部病害分类，所使用的公开数据集包含约5000张图像，分布于炭疽病（Anthracnose）、细菌性溃疡病（Bacterial Canker）、切叶象甲（Cutting Weevil）、枯枝病（Dag Disease）、回枯病（Die Back）、虫瘿（Galls）、健康（Healthy）、白粉病（Powdery Mildew）、红锈病（Red Rust）和煤烟病（Sooty Mould）十个类别。与传统的基于AlexNet的方法不同，所提出的框架集成了贝叶斯超参数优化、两阶段微调、测试时增强（TTA）以及基于Grad-CAM的可解释性，以同时提升预测性能和可解释性。实验结果表明，所提出的框架在使用八视图测试时增强（TTA）时达到了99.2%的最终测试准确率，相应的精确率、召回率和F1分数约为99.1%。该模型在精选测试集上还取得了1.00的微平均和宏平均ROC–AUC分数，表明在所考虑的病害类别间具有较强的判别能力。与MobileNetV2、DenseNet201、ConvNeXt和LeafNet的对比评估表明，该框架在保持适中计算复杂度和架构简洁性的同时，具有竞争性或更优的分类性能。此外，Grad-CAM可视化定性地突出了具有生物学相关性的病害症状，包括病斑、坏死区域、锈斑、真菌生长和组织变色，从而增强了模型的可解释性。尽管田间规模验证和定量可解释性评估仍有待未来研究，但所提出的框架为自动化芒果叶部病害诊断提供了一种准确、计算高效且可解释的方法，在智慧农业和精准农业应用中具有强大潜力。","Intelligent Data Analysis","2026-10-15T00:00:00Z",76,{"impact":255,"substance":231,"depth":232,"authority":175,"freshness":55,"relevant":59,"comment":359},"将贝叶斯调参、两阶段微调与Grad-CAM结合用于十类芒果叶病识别，准确率99.2%，方法新颖且可解释性强，对智慧农业植保应用有参考价值。",[361],{"name":355,"url":352},[363,237,24,126,364],"智慧农业","芒果病害",{"code":44,"slug":242,"name":243,"path":244,"parent_code":134},[],{"status":92,"locked":14,"version":31,"method":32,"evidence":368},{"candidates":369,"agricultural_evidence":38},[370,374,379,385],{"code":44,"score":255,"supported":38,"title":371,"abstract":372,"tags":373},[322],[48,322],[237,24],{"code":102,"score":202,"supported":38,"title":375,"abstract":376,"tags":378},[],[363,126,377],"smart agriculture",[363,126],{"code":380,"score":96,"supported":38,"title":381,"abstract":382,"tags":384},"B2",[],[383],"芒果",[],{"code":386,"score":96,"supported":38,"title":387,"abstract":388,"tags":390},"H8",[],[389],"可持续农业",[],[392,393],"芒果叶片病害 AlexNet","Grad-CAM 芒果病害识别","芒果叶片病害AlexNet-5080","10.1177\u002F1088467x261495708",{"doi":395,"openalex_id":397,"authors":398,"venue":355,"cited_by_count":15,"oa_url":8,"card":419,"direction":347,"ingested_from":298},"W7222328160",[399,402,405,407,410,413,416],{"name":400,"orcid":401},"Anay Ghosh","https:\u002F\u002Forcid.org\u002F0009-0006-6237-7905",{"name":403,"orcid":404},"Tawfikur Rahman","https:\u002F\u002Forcid.org\u002F0000-0002-0456-5143",{"name":406,"orcid":8},"Md Moniruzzaman",{"name":408,"orcid":409},"Nibedita Deb","https:\u002F\u002Forcid.org\u002F0000-0002-1579-1138",{"name":411,"orcid":412},"Hanan Abdullah Mengash","https:\u002F\u002Forcid.org\u002F0000-0002-4103-2434",{"name":414,"orcid":415},"Noorlindawaty Md Jizat","https:\u002F\u002Forcid.org\u002F0000-0001-7618-2283",{"name":417,"orcid":418},"Samir Salem Al‐Bawri","https:\u002F\u002Forcid.org\u002F0000-0003-2852-575X",{"tldr":420,"method":421,"finding":422,"direction":295,"opportunity":423},"提出可解释深度学习框架，用AlexNet结合贝叶斯调参、微调和Grad-CAM实现芒果叶病十分类。","AlexNet+贝叶斯超参优化、两阶段微调、八视图TTA、Grad-CAM，约5","测试准确率达99.2%，ROC-AUC为1.00，Grad-CAM能定位病斑等生物相关症状。","可延伸至田间复杂背景验证、可解释性定量评估及轻量化边缘部署研究。","2026-10-16T23:30:08.303796Z"]