[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2771":3,"related-2771":89},{"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":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":60,"slug":63,"view_count":64,"doi":65,"paper":66,"created_at":88},2771,"Edge-AI Based Smart Pet Monitoring Framework: A Rabbit Case Study","https:\u002F\u002Fdoi.org\u002F10.64643\u002Fijirt.208534-459","Continuous Monitoring of animals is hard when the people who take care of them are not around.Changes in how they are eaten, how much they drink, the way they stand and what they do during the day can show if their health is changing or if their normal routine is different.This paper talks about an Edge AI-based Smart Pet Monitoring Framework.The goal is watching the pet's behaviour and the environment around them in time.The system keeps privacy in mind.It uses computer vision, IoT, sensors and computing that happens close to the data not in the cloud This reduces the need for internet access.A Raspberry Pi serves as the edge device running a Python and OpenCV image processing chain that uses a YOLOv9 detector to spot feeding, drinking, posture and activity states.Temperature, humidity, ultrasonic distance and water-level sensors give environment data while an Arduino control layer handles alerts using an LCD, buzzer, LEDs and a servo.Local inference gives real-time processing and improves privacy, data ownership and response time.We tested than 200 images in a controlled setting and found it works but the test is still early.This framework offers a flexible base for smart pet monitoring, which could help at home in shelters and, for vets once more tests are done.","当照顾动物的人不在身边时，对动物进行持续监测是很困难的。它们进食方式、饮水量、站立姿态以及白天活动的变化，可以反映其健康状况是否发生变化或日常规律是否出现异常。本文讨论了一种基于边缘人工智能的智能宠物监测框架。其目标是及时观察宠物的行为及其周围环境。该系统注重隐私保护。它使用计算机视觉、物联网、传感器以及靠近数据端而非云端进行的计算，这减少了对互联网接入的需求。树莓派作为边缘设备，运行Python和OpenCV图像处理流程，并使用YOLOv9检测器识别进食、饮水、姿态和活动状态。温度、湿度、超声波距离和水位传感器提供环境数据，而Arduino控制层通过LCD、蜂鸣器、LED和舵机处理警报。本地推理实现了实时处理，并改善了隐私、数据所有权和响应时间。我们在受控环境中测试了200多张图像，发现系统可以工作，但测试仍处于早期阶段。该框架为智能宠物监测提供了一个灵活的基础，在完成更多测试后，可能有助于家庭、收容所以及兽医使用。",null,"International Journal of Innovative Research in Technology","2026-09-16T00:00:00Z","论文",10,false,49,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,14,13,5,9,1,"边缘AI宠物监测框架，与农业信息化关联偏弱且测试样本仅200张，属早期探索性论文，不宜进入每日精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","边缘计算","物联网","动物监测",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":59},[39,46,54],{"code":40,"score":17,"supported":41,"title":42,"abstract":43,"tags":45},"I5",true,[],[30,44],"IoT",[30,29],{"code":47,"score":48,"supported":41,"title":49,"abstract":50,"tags":53},"I2",7,[],[51,52],"计算机视觉","computer vision",[28],{"code":55,"score":22,"supported":14,"title":56,"abstract":57,"tags":58},"I1",[],[],[27],"农业相关性需要复核",[61,62],"农业人工智能 动物监测 智慧农业 边缘计算","农业人工智能 动物监测","农业人工智能动物监测智慧农业边缘计算-2771",4,"10.64643\u002Fijirt.208534-459",{"doi":65,"openalex_id":67,"authors":68,"venue":10,"cited_by_count":79,"oa_url":80,"card":81,"direction":85,"ingested_from":87},"W7213273522",[69,71,73,75,77],{"name":70,"orcid":9},"Vinit Masale",{"name":72,"orcid":9},"Suyog Mamankar",{"name":74,"orcid":9},"Prathamesh Sawant",{"name":76,"orcid":9},"Mhaboob Ali",{"name":78,"orcid":9},"Prof. Jyoti Shrote",0,"https:\u002F\u002Fijirt.org\u002Fpublishedpaper\u002FIJIRT208534_PAPER.pdf",{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"提出基于边缘AI的宠物监测框架，用树莓派和YOLOv9识别兔子行为与环境。","树莓派边缘计算、YOLOv9、OpenCV、Arduino及温湿度超声波传感器。","200张图像测试验证了框架可行性，但需更多测试才能实际应用。","智慧农业 \u002F 农业物联网","可扩展至畜禽行为健康监测，解决边缘设备算力与多目标识别精度问题。","openalex","2026-09-17T23:30:10.728310Z",{"total":90,"page":22,"page_size":90,"items":91},6,[92,177,262,354,442,516],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":9,"source_name":98,"source_url":95,"published_at":99,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":101,"sources":105,"tags":107,"primary_topic":109,"secondary_topics":114,"classification":119,"search_phrases":156,"slug":159,"view_count":22,"doi":160,"paper":161,"created_at":176},4420,"AI-Enabled Sustainable Precision Farming Using Raspberry Pi and Coral Edge TPU with Cloud-Based Model Training","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85019","Precision Agriculture requires intelligent, adaptable and efficient technology that is capable of making decisions in agriculture at the right time, based on the condition of the plants and the environment. This paper proposes the development of an AI-based sustainable precision farming system based on computer vision, cloud based model training, Raspberry Pi, Coral Edge TPU and the Internet of Things (IoT) sensing. The proposed architecture's sensing layer, edge processing layer, cloud intelligence layer, and application layer enables a complete data processing pipeline from the field to real time decision support. Crop photos are collected for AI condition classification and agricultural conditions such as soil moisture, temperature, humidity and light intensity are collected by built-in sensors. After the above preprocessing, normalisation, image resizing and augmentation, the dataset is divided into training, validation and test sets. The trained deep learning models in a cloud environment are then quantized and converted to be optimized for the edge, following this step. Following this step, the trained deep learning models are quantized and converted for optimization on edge. The optimized model achieves an exemplary accuracy of 94.82%, precision of 94.47%, recall of 94.05% and F1-score of 94.26%. Deploying with the Coral Edge TPU reduces inference latency to approximately 38ms compared to Raspberry Pi-only processing, increases processing throughput to 26.3 pictures per second and reduces CPU usage. The edge-assisted architecture reduces the need for constant connection to the cloud, giving local decision making a leg up. Illustrative sustainability study estimates that condition based monitoring can save 22% of the irrigation water used every day. The findings indicated that the introduction of the edge AI and cloud intelligence in precision agricultural systems that are responsive, scalable, and resource-efficient is technically possible.","精准农业需要智能、自适应且高效的技术，能够根据植物和环境状况在恰当时机做出农业决策。本文提出开发一种基于人工智能的可持续精准农业系统，该系统依托计算机视觉、基于云端的模型训练、树莓派（Raspberry Pi）、Coral边缘TPU（Edge TPU）以及物联网（IoT）传感技术。所提出的架构包含感知层、边缘处理层、云智能层和应用层，实现了从田间到实时决策支持的完整数据处理流水线。系统采集作物照片用于AI状态分类，并通过内置传感器采集土壤湿度、温度、空气湿度和光照强度等农业环境条件。经过上述预处理、归一化、图像缩放和数据增强后，数据集被划分为训练集、验证集和测试集。随后，在云环境中训练好的深度学习模型经过量化和转换，针对边缘设备进行优化。优化后的模型达到了94.82%的准确率、94.47%的精确率、94.05%的召回率和94.26%的F1分数，表现优异。与仅使用树莓派处理相比，部署Coral边缘TPU将推理延迟降低至约38毫秒，将处理吞吐量提升至每秒26.3张图片，并降低了CPU占用率。该边缘辅助架构减少了对云端持续连接的需求，提升了本地决策能力。一项示例性可持续性研究估计，基于状态的监测可节省22%的每日灌溉用水量。研究结果表明，在精准农业系统中引入边缘AI和云智能，构建响应迅速、可扩展且资源高效的农业系统，在技术上是可行的。","International Journal for Research in Applied Science and Engineering Technology","2026-10-07T00:00:00Z",55,{"impact":17,"substance":102,"depth":103,"authority":90,"freshness":17,"relevant":22,"comment":104},18,15,"技术路线完整、指标具体，但属单篇学术论文，产业影响有限，可作为智慧农业技术参考而非每日精选头条。",[106],{"name":98,"url":95},[27,28,29,30,108],"精准灌溉",{"code":40,"slug":110,"name":111,"path":112,"parent_code":113},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[115],{"code":47,"slug":116,"name":117,"path":118,"parent_code":113},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":120,"locked":14,"version":35,"method":36,"evidence":121},"classified",{"candidates":122,"agricultural_evidence":41},[123,129,136,143,149],{"code":40,"score":124,"supported":41,"title":125,"abstract":126,"tags":128},11,[],[30,127,44],"internet of things",[30,29],{"code":47,"score":13,"supported":41,"title":130,"abstract":131,"tags":135},[],[132,51,133,52,134],"深度学习","deep learning","decision support",[28],{"code":137,"score":48,"supported":41,"title":138,"abstract":139,"tags":142},"H3",[],[140,141],"灌溉","irrigation",[108],{"code":55,"score":48,"supported":41,"title":144,"abstract":145,"tags":148},[],[146,147],"精准农业","precision agriculture",[27],{"code":150,"score":90,"supported":41,"title":151,"abstract":152,"tags":155},"H2",[],[153,154],"土壤","soil",[],[157,158],"Raspberry Pi Coral Edge TPU 精准农业","边缘AI 作物分类 灌溉节水","RaspberryPiCoralEdgeTPU精准农业-4420","10.22214\u002Fijraset.2026.85019",{"doi":160,"openalex_id":162,"authors":163,"venue":98,"cited_by_count":79,"oa_url":95,"card":171,"direction":85,"ingested_from":87},"W7220855887",[164,167,169],{"name":165,"orcid":166},"Prakash Kanade","https:\u002F\u002Forcid.org\u002F0000-0001-6296-9368",{"name":168,"orcid":9},"Shetty Chirag",{"name":170,"orcid":9},"Sunay Kanade",{"tldr":172,"method":173,"finding":174,"direction":85,"opportunity":175},"提出基于树莓派与Coral Edge TPU的边缘AI精准农业系统，实现作物状态分类与实时决策。","计算机视觉、云端模型训练与量化、边缘TPU部署、IoT多传感器融合。","模型准确率94.82%，推理延迟约38ms，条件监测可节水22%。","可探索多作物泛化、边缘模型自适应更新及水肥药协同优化，并量化长期可持续效益。","2026-10-08T23:30:09.663393Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":9,"content":9,"source_name":182,"source_url":180,"published_at":183,"category":12,"cover_url":9,"hotness":100,"is_selected":14,"score":184,"score_detail":185,"sources":189,"tags":199,"primary_topic":201,"secondary_topics":202,"classification":208,"search_phrases":241,"slug":244,"view_count":64,"doi":245,"paper":246,"created_at":261},1249,"Enhancing water management with intelligent irrigation systems using edge computing, machine learning and IoT","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1827416","Agriculture is responsible for about 70% of freshwater withdrawals, and water scarcity and inefficient methods of water use remain threats to food security. The traditional way of irrigation causes more water loss and requires innovative solutions to optimize water distribution. This research provides an overview of an intelligent irrigation system, which combines the Internet of Things (IoT), machine learning (ML), and edge computing to optimize water usage and improve crop production. The system uses IoT sensors and edge devices to collect data on the environment and estimate the need for irrigation in real-time. It combines machine learning with system components like the Soil Monitoring Unit (SMU) and Water Management Unit (WMU) to provide scalable, real-time and efficient irrigation management. It is deployed locally at the edge, without the need for cloud computing, has the potential to integrate multi-sensor fusion for storm detection, and offers multiple ML models in a modular, low cost system that can be adapted to various agricultural settings. To optimize irrigation schedules, six supervised machine-learning models were used, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD) and Multi-Layer Perceptron (MLP). DT and RF had the highest classification accuracy of 93% and R 2 regression scores of 0.98 respectively, indicating high prediction reliability. However, Support Vector Machine (SVM) did not do that well, with an accuracy percentage of 58% and an R 2 score of 0.18. Although more complex, RF’s accuracy is a great option for larger applications and DT balances efficiency and scalability. The findings corroborate the utility of water efficient irrigation system and its prospect in augmenting productivity of agriculture. Future work will include expanding the field trials and developing hybrid models to further improve adaptability of the system in different agricultural environments. The findings of this research can offer understanding for sustainable farming practices, that are essential when it comes to water scarcity and food security in the global context.","Frontiers in Sustainable Food Systems","2026-08-25T00:00:00Z",74,{"impact":102,"substance":186,"depth":102,"authority":19,"freshness":187,"relevant":22,"comment":188},22,3,"研究提出结合IoT、ML与边缘计算的智能灌溉系统，DT和RF模型精度高，对节水农业有实质参考价值。",[190,191,194,196],{"name":182,"url":180},{"name":192,"url":193},"Zenodo (CERN European Organization for Nuclear Research)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790399",{"name":192,"url":195},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790398",{"name":197,"url":198},"Journal of Animal Science","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjas\u002Fskag311",[27,28,29,30,200],"智能灌溉",{"code":40,"slug":110,"name":111,"path":112,"parent_code":113},[203,207],{"code":137,"slug":141,"name":204,"path":205,"parent_code":206},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"code":47,"slug":116,"name":117,"path":118,"parent_code":113},{"status":120,"locked":14,"version":35,"method":36,"evidence":209},{"candidates":210,"agricultural_evidence":41},[211,217,222,227,233,237],{"code":40,"score":212,"supported":41,"title":213,"abstract":215,"tags":216},27,[44,214],"edge computing",[127,44,214],[30,29],{"code":137,"score":218,"supported":41,"title":219,"abstract":220,"tags":221},12,[141],[141],[200],{"code":47,"score":218,"supported":41,"title":223,"abstract":225,"tags":226},[224],"machine learning",[224],[28],{"code":228,"score":187,"supported":41,"title":229,"abstract":230,"tags":232},"A8",[],[231],"food security",[],{"code":150,"score":187,"supported":41,"title":234,"abstract":235,"tags":236},[],[154],[],{"code":55,"score":22,"supported":14,"title":238,"abstract":239,"tags":240},[],[],[27],[242,243],"农业人工智能 智慧农业 智能灌溉 边缘计算","农业人工智能 智慧农业","农业人工智能智慧农业智能灌溉边缘计算-1249","10.3389\u002Ffsufs.2026.1827416",{"doi":245,"openalex_id":247,"authors":248,"venue":182,"cited_by_count":79,"oa_url":255,"card":256,"direction":85,"ingested_from":87},"W7204189894",[249,252],{"name":250,"orcid":251},"Mohammed Sulaiman Mustafa","https:\u002F\u002Forcid.org\u002F0000-0002-2920-9428",{"name":253,"orcid":254},"Hakan Kutucu","https:\u002F\u002Forcid.org\u002F0000-0001-7144-7246","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Fpdf",{"tldr":257,"method":258,"finding":259,"direction":85,"opportunity":260},"提出结合IoT、机器学习和边缘计算的智能灌溉系统，优化用水并提高作物产量。","使用IoT传感器和边缘设备，结合六种监督学习模型（如DT、RF）进行实时灌溉管理","决策树和随机森林表现最佳，准确率93%，R²为0.98，优于其他模型。","可探索混合模型和扩展田间试验，提高系统在不同农业环境中的适应性。","2026-09-01T04:03:09.639788Z",{"id":263,"title":264,"url":265,"summary":266,"summary_zh":267,"content":9,"source_name":268,"source_url":265,"published_at":269,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":270,"score_detail":271,"sources":274,"tags":276,"primary_topic":279,"secondary_topics":283,"classification":284,"search_phrases":340,"slug":343,"view_count":79,"doi":344,"paper":345,"created_at":353},5556,"AI Powered IoT-Based System for Weed and Pest Control in Agriculture Using Deep Learning","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.76s.32","Weeds, pest attacks, labour shortage and over-dependence on chemical pesticides are the serious challenges faced by the agricultural sector in the wake of ever growing weed infestation.Manual weed and pest control has potential negative environmental impacts, increased production costs and reduced yields.The application of Artificial Intelligence (AI), Internet of Things (IoT), Deep Learning, robotics, and laser technologies are a promising way of developing sustainable and autonomous agricultural systems.This study introduces an intelligent agricultural system based on Artificial Intelligence (AI) and Internet of Things (IoT) for weed identification, harmful insect identification, crop condition monitoring and automatic weed removal using a laser beam.The system will be based on Internet of Things (IoT) sensors, Computer vision modules, Convolutional neural networks (CNN), Unmanned robotic platforms, Environmental sensing systems, and Electronic Swarm Suppression (ESS) mechanisms to enhance agricultural productivity and reduce crop losses.The system uses deep learning algorithms for real time weed, crop, beneficial and harmful insect classification.High-resolution cameras and sensing devices collect environmental and crop data continuously and feed the data into an IoT system for processing in a cloud-edge computing system.High precision weed elimination with a robotic platform containing laser emitters which do not harm adjacent crops.Further, computer vision and environmental monitoring are employed to identify aerial insect swarms and to enable automatic attacks of insect swarms by means of a laser beam and ESS technology.Multiple agricultural blocks were used for experimental evaluation of the maize, wheat, soybean and vegetable crops.The results show substantial increases in the accuracy of weed detection, pest identification, crop protection and cost savings for operations over current practices.The proposed system achieved a 97.6% accuracy in the weed classification, 96.8% accuracy in the detection of the insect and a 42.3% reduction in pesticide use.In addition, the crop yield rose by 18.7% and the operational costs dropped by 28.4%.Overall, the results suggest that AI-powered IoT can significantly enhance precision agriculture and promote sustainable food production.","在杂草侵扰日益加剧的背景下，杂草、病虫害侵袭、劳动力短缺以及过度依赖化学农药是农业部门面临的严峻挑战。人工除草和病虫害防治具有潜在的环境负面影响，会增加生产成本并降低产量。人工智能（AI）、物联网（IoT）、深度学习、机器人技术及激光技术的应用，是发展可持续自主农业系统的有前景的途径。本研究提出一种基于人工智能（AI）和物联网（IoT）的智能农业系统，用于杂草识别、有害昆虫识别、作物状况监测以及利用激光束自动除草。该系统将基于物联网（IoT）传感器、计算机视觉模块、卷积神经网络（CNN）、无人机器人平台、环境感知系统及电子蜂群抑制（ESS）机制，以提高农业生产力并减少作物损失。该系统采用深度学习算法对杂草、作物、有益昆虫和有害昆虫进行实时分类。高分辨率摄像头和传感设备持续采集环境与作物数据，并将数据输入物联网系统，在云边计算系统中进行处理。通过配备激光发射器的机器人平台实现高精度除草，且不伤害邻近作物。此外，利用计算机视觉和环境监测来识别空中昆虫蜂群，并通过激光束和ESS技术实现对昆虫蜂群的自动攻击。研究采用多个农业区块对玉米、小麦、大豆和蔬菜作物进行了实验评估。结果表明，与当前做法相比，杂草检测、病虫害识别、作物保护的准确性显著提高，作业成本得以节省。所提出的系统在杂草分类中达到97.6%的准确率，昆虫检测准确率为96.8%，农药使用量减少42.3%。此外，作物产量提高18.7%，作业成本下降28.4%。总体而言，结果表明人工智能驱动的物联网能够显著增强精准农业并促进可持续粮食生产。","International Journal of Drug Delivery Technology","2026-10-21T00:00:00Z",75,{"impact":102,"substance":186,"depth":272,"authority":17,"freshness":13,"relevant":22,"comment":273},17,"论文给出97.6%杂草识别率、42.3%农药减量等量化结论，方法新颖且数据完整，但期刊权威性一般，属细分领域技术进展。",[275],{"name":268,"url":265},[27,28,30,277,278],"杂草识别","病虫害监测",{"code":113,"slug":280,"name":281,"path":282,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":285,"locked":14,"version":35,"method":36,"evidence":286},"partial",{"candidates":287,"agricultural_evidence":41,"reason":339},[288,294,298,307,311,318,325,332],{"code":47,"score":102,"supported":41,"title":289,"abstract":290,"tags":293},[133],[132,51,291,133,292,52],"病虫害识别","artificial intelligence",[28],{"code":40,"score":102,"supported":41,"title":295,"abstract":296,"tags":297},[44],[30,127,44,214],[30],{"code":299,"score":13,"supported":41,"title":300,"abstract":301,"tags":306},"F4",[],[302,303,304,305],"农药","除草","pesticide","crop protection",[277],{"code":55,"score":48,"supported":41,"title":308,"abstract":309,"tags":310},[],[146,147],[27],{"code":312,"score":90,"supported":41,"title":313,"abstract":314,"tags":317},"A2",[],[315,316],"小麦","wheat",[],{"code":319,"score":90,"supported":41,"title":320,"abstract":321,"tags":324},"A3",[],[322,323],"玉米","maize",[],{"code":326,"score":90,"supported":41,"title":327,"abstract":328,"tags":331},"A4",[],[329,330],"大豆","soybean",[],{"code":333,"score":90,"supported":41,"title":334,"abstract":335,"tags":338},"B1",[],[336,337],"蔬菜","vegetable",[],"一级领域明确，二级方向需要细分",[341,342],"AI IoT 激光除草 病虫害识别","深度学习 精准农业 农药减量","AIIoT激光除草病虫害识别-5556","10.25258\u002Fijddt.16.76s.32",{"doi":344,"openalex_id":346,"authors":347,"venue":268,"cited_by_count":79,"oa_url":265,"card":348,"direction":85,"ingested_from":87},"W7223145687",[],{"tldr":349,"method":350,"finding":351,"direction":85,"opportunity":352},"提出AI+IoT系统，用深度学习和激光机器人实现杂草与害虫识别及自动清除。","IoT传感器、CNN视觉分类、无人机\u002F机器人平台与激光除草，云边协同处理。","杂草分类准确率97.6%、害虫识别96.8%，农药减42.3%，增产18.7%。","可探索激光除草对非靶标生物与土壤的长期生态影响，及多作物跨域模型泛化。","2026-10-22T11:20:18.540359Z",{"id":355,"title":356,"url":357,"summary":358,"summary_zh":359,"content":9,"source_name":360,"source_url":357,"published_at":269,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":361,"score_detail":362,"sources":364,"tags":366,"primary_topic":368,"secondary_topics":369,"classification":372,"search_phrases":417,"slug":420,"view_count":79,"doi":421,"paper":422,"created_at":441},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)",80,{"impact":102,"substance":186,"depth":102,"authority":19,"freshness":21,"relevant":22,"comment":363},"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[365],{"name":360,"url":357},[27,28,30,146,367],"气候韧性",{"code":47,"slug":116,"name":117,"path":118,"parent_code":113},[370,371],{"code":40,"slug":110,"name":111,"path":112,"parent_code":113},{"code":137,"slug":141,"name":204,"path":205,"parent_code":206},{"status":120,"locked":14,"version":35,"method":36,"evidence":373},{"candidates":374,"agricultural_evidence":41},[375,380,385,389,394,401,405,411],{"code":47,"score":102,"supported":41,"title":376,"abstract":377,"tags":379},[292],[132,378,51,224,133,52],"机器学习",[28],{"code":55,"score":381,"supported":41,"title":382,"abstract":383,"tags":384},16,[147],[146,147],[27,146],{"code":40,"score":13,"supported":41,"title":386,"abstract":387,"tags":388},[],[30,29,44,214],[30],{"code":137,"score":21,"supported":41,"title":390,"abstract":391,"tags":393},[],[140,392,141],"水资源",[],{"code":395,"score":48,"supported":41,"title":396,"abstract":397,"tags":400},"H7",[],[398,399],"气候变化","climate change",[367],{"code":150,"score":90,"supported":41,"title":402,"abstract":403,"tags":404},[],[153,154],[],{"code":406,"score":187,"supported":41,"title":407,"abstract":408,"tags":410},"A7",[],[409],"间作",[],{"code":412,"score":187,"supported":41,"title":413,"abstract":414,"tags":416},"F3",[],[415],"养分管理",[],[418,419],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":421,"openalex_id":423,"authors":424,"venue":360,"cited_by_count":79,"oa_url":357,"card":435,"direction":85,"ingested_from":87},"W7223121456",[425,428,430,432],{"name":426,"orcid":427},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":429,"orcid":9},"Oluwadare OYEBODE",{"name":431,"orcid":9},"Iretiayo Adelaiye",{"name":433,"orcid":434},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":436,"method":437,"finding":438,"direction":439,"opportunity":440},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","农业人工智能与决策模型","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":443,"title":444,"url":445,"summary":446,"summary_zh":447,"content":9,"source_name":448,"source_url":445,"published_at":449,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":450,"score_detail":451,"sources":453,"tags":455,"primary_topic":9,"secondary_topics":458,"classification":459,"search_phrases":487,"slug":490,"view_count":79,"doi":491,"paper":492,"created_at":515},5402,"AI‐Enabled RGB Light‐to‐Frequency Sensor for Nondestructive Salinity Stress Monitoring in Medicinal Plants","https:\u002F\u002Fdoi.org\u002F10.1002\u002Faisy.70561","Salinity stress constrains medicinal plant productivity, particularly in coastal and salt affected agroecosystems where rapid, deployable diagnostics are essential. Here, we develop an AI‐enabled RGB light‐to‐frequency proximal sensing platform integrated with Tiny Machine Learning (TinyML) for nondestructive, real‐time salinity discrimination. Plants are exposed to three salinity levels (0, 100, and 200 mM NaCl). RGB‐derived and CIELAB chromatic descriptors are engineered, normalized, and partitioned (75:25) into training and test datasets. ReliefF‐based feature ranking and systematic subset optimization are combined with supervised learning using fivefold cross‐validation, and interpretability is achieved through SHAP analysis. Biomass reductions under saline treatments confirm physiological perturbations underlying spectral variability. Subspace KNN achieved the highest predictive performance (96.28% validation; 95.55% independent test accuracy). Framing model selection as a multiobjective optimization problem identified an optimal 10‐feature subset. Under this constraint, a Wide Neural Network attained 0.904 accuracy with an ultracompact 0.017 MB model size and >246 000 observations s −1 inference speed, demonstrating full TinyML compatibility. SHAP attribution consistently highlighted green intensity, blue channel metrics, and chromatic ratios as dominant predictors. This work establishes a lightweight, interpretable Edge‐AI framework for scalable, real‐time salinity diagnostics in precision agriculture.","盐胁迫制约药用植物的生产力，尤其是在沿海及受盐影响的农业生态系统中，亟需可快速部署的诊断手段。本研究开发了一种基于人工智能的RGB光-频率近端传感平台，并集成微型机器学习（TinyML），用于无损、实时的盐胁迫判别。植物暴露于三个盐浓度水平（0、100和200 mM NaCl）。研究构建了RGB衍生描述符与CIELAB色度描述符，经归一化后按75:25划分为训练集与测试集。将基于ReliefF的特征排序与系统性特征子集优化相结合，采用五折交叉验证进行监督学习，并通过SHAP分析实现模型可解释性。盐处理下的生物量下降证实了光谱变异性背后的生理扰动。子空间KNN取得了最高的预测性能（验证集准确率96.28%；独立测试集准确率95.55%）。将模型选择构建为多目标优化问题，确定了最优的10特征子集。在该约束下，宽神经网络（Wide Neural Network）达到0.904的准确率，模型体积仅0.017 MB，推理速度超过246 000次观测\u002F秒，展现出完全的TinyML兼容性。SHAP归因一致表明，绿色强度、蓝色通道指标及色度比是主要预测因子。本研究为精准农业中可扩展的实时盐胁迫诊断建立了一个轻量、可解释的边缘人工智能框架。","Advanced Intelligent Systems","2026-10-19T00:00:00Z",81,{"impact":102,"substance":186,"depth":102,"authority":18,"freshness":21,"relevant":22,"comment":452},"该研究提出一种轻量、可解释的边缘AI盐胁迫诊断框架，方法新颖、数据扎实，对精准农业具有参考价值，但属于实验室阶段成果，产业影响有限。",[454],{"name":448,"url":445},[27,28,29,456,457],"盐胁迫监测","药用植物",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":460},{"candidates":461,"agricultural_evidence":41,"reason":486},[462,468,472,476,482],{"code":463,"score":48,"supported":41,"title":464,"abstract":465,"tags":467},"B6",[],[457,466],"medicinal plant",[457],{"code":55,"score":48,"supported":41,"title":469,"abstract":470,"tags":471},[],[146,147],[27],{"code":47,"score":48,"supported":41,"title":473,"abstract":474,"tags":475},[],[378,224],[28],{"code":477,"score":187,"supported":41,"title":478,"abstract":479,"tags":481},"H6",[],[480],"农业生态",[],{"code":40,"score":22,"supported":14,"title":483,"abstract":484,"tags":485},[],[],[29],"多个产业证据相当，需要确认主归属",[488,489],"RGB 光频传感器 盐胁迫","TinyML 药用植物 无损监测","RGB光频传感器盐胁迫-5402","10.1002\u002Faisy.70561",{"doi":491,"openalex_id":493,"authors":494,"venue":448,"cited_by_count":79,"oa_url":445,"card":510,"direction":439,"ingested_from":87},"W7222914402",[495,498,501,504,507],{"name":496,"orcid":497},"Ahmad Ali","https:\u002F\u002Forcid.org\u002F0000-0001-5530-7374",{"name":499,"orcid":500},"Vinie Lee Silva-Alvarado","https:\u002F\u002Forcid.org\u002F0009-0000-5857-3248",{"name":502,"orcid":503},"Arman Heydari","https:\u002F\u002Forcid.org\u002F0009-0002-4705-0729",{"name":505,"orcid":506},"Sandra Sendra","https:\u002F\u002Forcid.org\u002F0000-0001-9556-9088",{"name":508,"orcid":509},"Jaime Lloret","https:\u002F\u002Forcid.org\u002F0000-0002-0862-0533",{"tldr":511,"method":512,"finding":513,"direction":85,"opportunity":514},"开发AI驱动的RGB光频传感器，实现药用植物盐胁迫无损实时监测。","RGB与CIELAB色度特征、ReliefF特征选择、SHAP解释及TinyML","Subspace KNN验证准确率96.28%，10特征Wide NN模型仅0.017MB且推理超2","可探索多作物多胁迫通用色度特征库，及田间光照变化下的模型鲁棒性与长期部署验证。","2026-10-20T23:30:35.809539Z",{"id":517,"title":518,"url":519,"summary":520,"summary_zh":521,"content":9,"source_name":522,"source_url":519,"published_at":523,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":524,"score_detail":525,"sources":528,"tags":530,"primary_topic":533,"secondary_topics":539,"classification":540,"search_phrases":561,"slug":564,"view_count":79,"doi":565,"paper":566,"created_at":586},5401,"A HIERARCHICAL DISTRIBUTED EDGE AI FRAMEWORK FOR REAL-TIME APPLE LEAF DISEASE CLASSIFICATION","https:\u002F\u002Fdoi.org\u002F10.22190\u002Ffuacr260506002g","The application of artificial intelligence (AI) in agriculture has transformed crop management, particularly in the detection of plant diseases. Apple trees, a key agricultural commodity, are vulnerable to various diseases, which can severely impact crop yields. Traditional disease detection methods, often reliant on manual inspections, are time-consuming and prone to error. In response, this study proposes a novel two-stage edge AI framework for apple leaf disease classification that integrates RGB imagery with distributed edge computing. The first stage performs binary classification to distinguish between healthy and diseased leaves, achieving an F1 score of 95.50%. This optimization is achieved by utilizing lightweight models on edge devices, thereby enhancing computational efficiency. The second stage employs a consensus-based multiclass classification approach, achieving a 96.44% F1 score in identifying specific diseases. This dual-stage system, supported by an edge AI architecture, ensures real-time disease detection while minimizing resource consumption and enhancing scalability in large-scale agricultural environments. The full system achieved a 95.50% accuracy and F1 score on the special dataset. The results demonstrate the system’s efficacy in controlled laboratory conditions, offering a promising solution for precise, real-time agricultural monitoring. This study underscores the potential of distributed edge AI systems for scalable and efficient disease detection in precision agriculture.","人工智能（AI）在农业中的应用已深刻改变了作物管理方式，尤其是在植物病害检测方面。苹果树作为重要的农业经济作物，易受多种病害侵袭，严重影响作物产量。传统的病害检测方法通常依赖人工检查，耗时且易出错。为此，本研究提出了一种新颖的两阶段边缘AI框架，用于苹果叶片病害分类，该框架将RGB图像与分布式边缘计算相结合。第一阶段执行二分类以区分健康叶片与患病叶片，F1分数达到95.50%。该优化通过在边缘设备上采用轻量级模型实现，从而提升了计算效率。第二阶段采用基于共识的多类分类方法，在识别具体病害方面F1分数达到96.44%。该双阶段系统在边缘AI架构的支持下，实现了实时病害检测，同时最大限度地降低了资源消耗，并增强了在大规模农业环境中的可扩展性。完整系统在专用数据集上达到了95.50%的准确率和F1分数。结果表明，该系统在受控实验室条件下具有有效性，为精准、实时的农业监测提供了一种有前景的解决方案。本研究凸显了分布式边缘AI系统在精准农业中实现可扩展且高效病害检测的潜力。","Facta Universitatis Series Automatic Control and Robotics","2026-10-18T00:00:00Z",76,{"impact":102,"substance":526,"depth":272,"authority":218,"freshness":17,"relevant":22,"comment":527},21,"该研究提出两级边缘AI框架，在苹果叶片病害识别上取得95%以上F1值，方法新颖、数据可靠，对精准农业实时监测有参考价值，但尚处实验室验证阶段，产业影响有限。",[529],{"name":522,"url":519},[27,28,29,531,532],"作物病害识别","苹果病害",{"code":534,"slug":535,"name":536,"path":537,"parent_code":538},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":120,"locked":14,"version":35,"method":36,"evidence":541},{"candidates":542,"agricultural_evidence":41},[543,549,553,557],{"code":534,"score":102,"supported":41,"title":544,"abstract":546,"tags":548},[545],"apple",[536,547,545],"苹果",[532],{"code":55,"score":48,"supported":41,"title":550,"abstract":551,"tags":552},[],[146,147],[27],{"code":40,"score":48,"supported":41,"title":554,"abstract":555,"tags":556},[],[29,214],[29],{"code":47,"score":20,"supported":41,"title":558,"abstract":559,"tags":560},[],[292],[531,28],[562,563],"苹果叶片病害 边缘AI","分布式边缘计算 病害分类","苹果叶片病害边缘AI-5401","10.22190\u002Ffuacr260506002g",{"doi":565,"openalex_id":567,"authors":568,"venue":522,"cited_by_count":79,"oa_url":519,"card":581,"direction":439,"ingested_from":87},"W7222964752",[569,572,575,578],{"name":570,"orcid":571},"Milan Grujev","https:\u002F\u002Forcid.org\u002F0009-0002-3211-6844",{"name":573,"orcid":574},"Dragan H. Stojanovic","https:\u002F\u002Forcid.org\u002F0000-0003-4893-3814",{"name":576,"orcid":577},"Miloš Ilić","https:\u002F\u002Forcid.org\u002F0000-0002-9696-2874",{"name":579,"orcid":580},"Aleksandar Milosavljević","https:\u002F\u002Forcid.org\u002F0000-0003-3057-7727",{"tldr":582,"method":583,"finding":584,"direction":85,"opportunity":585},"提出两级边缘AI框架，用RGB图像实时分类苹果叶片病害。","分布式边缘计算+轻量模型，先二分类再共识多分类。","两级系统准确率与F1均达95.50%，多分类F1为96.44%。","仅在受控实验室验证，可延伸至田间复杂光照与多作物泛化及边缘能耗优化。","2026-10-20T23:30:33.830748Z"]