[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2682":3,"related-2682":99},{"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,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":79,"slug":82,"view_count":83,"doi":84,"paper":85,"created_at":98},2682,"Machine Learning-Based Decision Support System for Greenhouse Crop Management Under Mite Infestation Conditions","https:\u002F\u002Fdoi.org\u002F10.36099\u002Fjess.v1i3.001","This paper presents an integrated machine learning-based Decision Support System (DSS) for greenhouse crop management under mite infestation conditions, specifically designed for Sri Lankan agricultural contexts. The research addresses critical challenges faced by greenhouse farmers regarding pest management and crop productivity optimization through a comprehensive system combining NetLogo simulation for synthetic data generation, ensemble machine learning models, and a web-based interface. Field research with Sri Lankan greenhouse farmers revealed that mite infestations cause up to 40% crop losses, driving panic-induced pesticide overuse and knowledge gaps in pest management timing. The system integrates environmental monitoring, pest prediction, and crop yield forecasting to provide actionable recommendations for farmers. The mite infestation prediction model achieved 86% accuracy, while the system successfully addresses data scarcity challenges through agent-based modeling. The DSS demonstrates potential for transforming reactive farming practices into predictive, data-driven approaches while accommodating the technological constraints of developing agricultural contexts.","本文提出了一种基于机器学习的集成决策支持系统（DSS），用于螨虫侵染条件下的温室作物管理，专为斯里兰卡农业情境设计。该研究针对温室农户在害虫管理和作物生产力优化方面面临的关键挑战，通过一个综合系统加以解决，该系统结合了用于合成数据生成的NetLogo仿真、集成机器学习模型以及基于网络的界面。针对斯里兰卡温室农户的实地研究表明，螨虫侵染可导致高达40%的作物损失，进而引发恐慌性农药过度使用以及害虫管理时机方面的知识缺口。该系统整合了环境监测、害虫预测和作物产量预测，为农户提供可操作的推荐建议。螨虫侵染预测模型达到了86%的准确率，同时该系统通过基于智能体的建模成功应对了数据稀缺的挑战。该决策支持系统展现出将被动应对式耕作实践转变为预测性、数据驱动方法的潜力，同时兼顾了发展中农业情境的技术约束。",null,"Journal of Environmental and Sustainability Science","2026-09-16T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},15,20,17,12,1,"将机器学习与智能体仿真结合用于温室螨害预测与决策支持，方法新颖、数据翔实，对设施农业植保信息化有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","设施农业","决策支持系统","病虫害预警",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,59,66,74],{"code":39,"score":51,"supported":52,"title":53,"abstract":56,"tags":58},27,true,[54,55],"machine learning","decision support",[57,54,55],"机器学习",[27,29],{"code":32,"score":60,"supported":52,"title":61,"abstract":63,"tags":65},14,[62],"greenhouse",[64,62],"温室",[],{"code":67,"score":68,"supported":52,"title":69,"abstract":70,"tags":73},"F4",6,[],[71,72],"农药","pesticide",[],{"code":75,"score":21,"supported":14,"title":76,"abstract":77,"tags":78},"I1",[],[],[26],[80,81],"农业人工智能 决策支持系统 病虫害预警 智慧农业","农业人工智能 决策支持系统","农业人工智能决策支持系统病虫害预警智慧农业-2682",3,"10.36099\u002Fjess.v1i3.001",{"doi":84,"openalex_id":86,"authors":87,"venue":10,"cited_by_count":90,"oa_url":6,"card":91,"direction":95,"ingested_from":97},"W7213235680",[88],{"name":89,"orcid":9},"S. Nasiketha",0,{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"为斯里兰卡温室农户开发基于机器学习的决策支持系统，预测螨害并优化作物管理。","NetLogo仿真生成合成数据，集成机器学习模型与网页界面。","螨害预测准确率达86%，可缓解数据稀缺并减少农药滥用。","农业人工智能与决策模型","可探索小样本下合成数据与迁移学习结合，提升发展中国家温室病虫害预测泛化能力。","openalex","2026-09-16T23:30:51.573509Z",{"total":68,"page":21,"page_size":68,"items":100},[101,195,258,314,375,422],{"id":102,"title":103,"url":104,"summary":105,"summary_zh":106,"content":9,"source_name":107,"source_url":104,"published_at":108,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":109,"score_detail":110,"sources":114,"tags":116,"primary_topic":120,"secondary_topics":121,"classification":128,"search_phrases":167,"slug":170,"view_count":171,"doi":172,"paper":173,"created_at":194},4569,"SMART GREENHOUSE MEASUREMENT AND MONITORING SYSTEM BASED ON IOT AND COMPUTER VISION","https:\u002F\u002Fdoi.org\u002F10.46242\u002Fjstiuh.v83i05.6189","This paper presents a smart greenhouse measurement and monitoring system integrating IoT and computer vision for non-invasive plant growth observation and remote supervision. The proposed architecture combines environmental sensors, STM32-based data acquisition and control modules, a Raspberry Pi 4 with a USB camera, a lightweight deep learning-based stem segmentation module, Firebase Realtime Database, and a web application for monitoring and control. In the computer vision module, tomato plant images are processed directly on the Raspberry Pi to segment stem regions and estimate plant height from calibrated image measurements, enabling edge-based phenotypic monitoring without destructive sampling or manual measurement. Experimental results on a tomato cultivation prototype show that the system can collect temperature, air humidity, soil moisture, and light intensity data in real time while providing automated plant height estimation from RGB images. During the reported period, temperature ranged from 27.8°C to 38.8°C, air humidity from 40.2% to 77.0%, soil moisture from 52.0% to 82.4%, and plant height increased from 65.8 cm to 77.0 cm. The segmentation results demonstrate the feasibility of extracting stem-level structural information under practical greenhouse conditions, including complex background, leaf occlusion, and non-uniform illumination. The prototype confirms the potential of a low-cost smart greenhouse monitoring system and provides a practical basis for future expansion toward richer agronomic indicators, such as stem width, leaf area, canopy coverage, growth rate estimation, and automated environmental control.","本文提出了一种集成物联网与计算机视觉的智能温室测量与监测系统，用于非侵入式植物生长观测与远程监管。所提出的架构结合了环境传感器、基于STM32的数据采集与控制模块、配备USB摄像头的Raspberry Pi 4、基于轻量级深度学习的茎秆分割模块、Firebase实时数据库以及用于监测与控制的Web应用程序。在计算机视觉模块中，番茄植株图像直接在Raspberry Pi上处理，以分割茎秆区域并根据标定后的图像测量值估算株高，从而实现基于边缘的表型监测，无需破坏性采样或人工测量。在番茄栽培原型上的实验结果表明，该系统能够实时采集温度、空气湿度、土壤湿度和光照强度数据，同时基于RGB图像提供自动株高估算。在报告期内，温度范围为27.8°C至38.8°C，空气湿度为40.2%至77.0%，土壤湿度为52.0%至82.4%，株高从65.8 cm增长至77.0 cm。分割结果证明了在实际温室条件下（包括复杂背景、叶片遮挡和非均匀光照）提取茎秆级结构信息的可行性。该原型验证了低成本智能温室监测系统的潜力，并为未来扩展至更丰富的农艺指标（如茎宽、叶面积、冠层覆盖度、生长速率估算和自动化环境控制）提供了实践基础。","Journal of Science and Technology - IUH","2026-10-08T00:00:00Z",66,{"impact":20,"substance":18,"depth":111,"authority":13,"freshness":112,"relevant":21,"comment":113},16,8,"低成本IoT与边缘视觉融合的温室监测原型，方法可行、数据翔实，但属细分技术验证，产业影响有限。",[115],{"name":107,"url":104},[26,27,117,118,119],"计算机视觉","农业物联网","温室种植",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[122,123],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":124,"slug":125,"name":126,"path":127,"parent_code":43},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":45,"locked":14,"version":46,"method":47,"evidence":129},{"candidates":130,"agricultural_evidence":52},[131,139,143,149,156,163],{"code":39,"score":132,"supported":52,"title":133,"abstract":135,"tags":138},19,[134],"computer vision",[136,117,137,134],"深度学习","deep learning",[27,117],{"code":32,"score":17,"supported":52,"title":140,"abstract":141,"tags":142},[62],[64,62],[119],{"code":124,"score":17,"supported":52,"title":144,"abstract":146,"tags":148},[145],"IoT",[147,145],"物联网",[118],{"code":150,"score":68,"supported":52,"title":151,"abstract":152,"tags":155},"B1",[],[153,154],"番茄","tomato",[],{"code":157,"score":68,"supported":52,"title":158,"abstract":159,"tags":162},"H2",[],[160,161],"土壤","soil",[],{"code":75,"score":21,"supported":14,"title":164,"abstract":165,"tags":166},[],[],[26],[168,169],"Raspberry Pi 温室监测","番茄 株高 图像识别","RaspberryPi温室监测-4569",4,"10.46242\u002Fjstiuh.v83i05.6189",{"doi":172,"openalex_id":174,"authors":175,"venue":107,"cited_by_count":90,"oa_url":187,"card":188,"direction":192,"ingested_from":97},"W7221046739",[176,178,180,182,184],{"name":177,"orcid":9},"HOANG THI TU UYEN",{"name":179,"orcid":9},"NGUYEN HOANH",{"name":181,"orcid":9},"NGO THANH QUYEN",{"name":183,"orcid":9},"Lam Binh Minh",{"name":185,"orcid":186},"Nguyễn Trung Nhân","https:\u002F\u002Forcid.org\u002F0000-0001-9328-7693","https:\u002F\u002Fjst.iuh.edu.vn\u002Findex.php\u002Fjst-iuh\u002Farticle\u002Fdownload\u002F6189\u002F1131",{"tldr":189,"method":190,"finding":191,"direction":192,"opportunity":193},"集成物联网与计算机视觉的智能温室系统，实现非破坏性植物生长监测与远程管控。","环境传感器、STM32、树莓派4、USB摄像头、轻量茎分割模型、Firebase","原型可实时采集温湿度、土壤水分和光照，并从RGB图像估计株高，验证了边缘表型监测可行性。","智慧农业 \u002F 农业物联网","可扩展至茎宽、叶面积、冠层覆盖度等更丰富农艺指标，并实现自动化环境控制闭环。","2026-10-10T23:30:11.420750Z",{"id":196,"title":197,"url":198,"summary":199,"summary_zh":200,"content":9,"source_name":201,"source_url":198,"published_at":202,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":203,"score_detail":204,"sources":207,"tags":209,"primary_topic":212,"secondary_topics":213,"classification":214,"search_phrases":244,"slug":247,"view_count":90,"doi":248,"paper":249,"created_at":257},5311,"AI SMART AGRO SYSTEM: A WEB-BASED AI-ASSISTED DECISION SUPPORT PLATFORM FOR FARMERS","https:\u002F\u002Fdoi.org\u002F10.56726\u002Firjmets103514","Farmers often have to decide on disease control, crop selection, soil management and irrigation without timely expert advice, which leads to yield loss and wastage of water and fertiliser.This paper presents AI Smart Agro System, a web-based decision-support platform that brings five services into one dashboard: leaf disease detection from an uploaded photograph, crop recommendation, soil health analysis, irrigation advisory and weather-based farming advice.The system is built with PHP, MySQL and JavaScript, while disease detection and crop recommendation are handled by an external Python Flask AI service that is called through a secured REST API.Soil, irrigation and weather advice are generated by transparent rule-based logic using soil pH, N-P-K values, soil moisture, rainfall, temperature and humidity.Every result is stored in the user's history, and notifications and an administrator panel support monitoring.The prototype is designed to return the disease name, confidence, treatment and prevention tips for an uploaded leaf image, to recommend the three most suitable crops, and to give clear advice for soil and water management.Quantitative evaluation of the AI models is left for future work.","农民常常需要在没有及时专家建议的情况下决定病害防治、作物选择、土壤管理和灌溉，这会导致产量损失以及水和肥料的浪费。本文提出AI Smart Agro System，这是一个基于Web的决策支持平台，将五项服务整合到一个仪表板中：根据上传照片进行叶片病害检测、作物推荐、土壤健康分析、灌溉建议以及基于天气的农事建议。该系统使用PHP、MySQL和JavaScript构建，而病害检测和作物推荐由外部Python Flask AI服务处理，并通过安全的REST API调用。土壤、灌溉和天气建议由透明的基于规则的逻辑生成，使用土壤pH、N-P-K值、土壤湿度、降雨量、温度和湿度。每个结果都存储在用户历史记录中，通知和管理员面板支持监控。该原型旨在为上传的叶片图像返回病害名称、置信度、治疗和预防提示，推荐三种最合适的作物，并为土壤和水管理提供明确建议。AI模型的定量评估留待未来工作。","International Research Journal of Modernization in Engineering Technology and Science","2026-10-17T00:00:00Z",49,{"impact":112,"substance":60,"depth":20,"authority":68,"freshness":205,"relevant":21,"comment":206},9,"原型系统整合病害识别、作物推荐与灌溉建议，思路清晰但缺乏定量评估，属细分技术进展，公共影响有限。",[208],{"name":201,"url":198},[26,27,29,210,211],"病害识别","灌溉管理",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":215},{"candidates":216,"agricultural_evidence":52},[217,222,230,234,240],{"code":39,"score":218,"supported":52,"title":219,"abstract":220,"tags":221},11,[55],[],[27,29,210],{"code":223,"score":224,"supported":52,"title":225,"abstract":226,"tags":229},"H3",7,[],[227,228],"灌溉","irrigation",[211],{"code":157,"score":68,"supported":52,"title":231,"abstract":232,"tags":233},[],[160,161],[],{"code":235,"score":83,"supported":52,"title":236,"abstract":237,"tags":239},"F3",[],[238],"肥料",[],{"code":75,"score":21,"supported":14,"title":241,"abstract":242,"tags":243},[],[],[26],[245,246],"Web 农业决策支持平台","农业人工智能 决策支持系统 智慧农业 灌溉管理","Web农业决策支持平台-5311","10.56726\u002Firjmets103514",{"doi":248,"openalex_id":250,"authors":251,"venue":201,"cited_by_count":90,"oa_url":198,"card":252,"direction":192,"ingested_from":97},"W7222785888",[],{"tldr":253,"method":254,"finding":255,"direction":95,"opportunity":256},"构建一个面向农民的网页版AI决策支持平台，集成病害识别、选种、土壤、灌溉与天气建议。","PHP\u002FMySQL\u002FJS前端，Python Flask提供病害与选种AI服务，规","原型可输出病害名称、置信度与防治建议，推荐三种适宜作物并给出水土管理建议。","AI模型尚未定量评估，可补充精度验证与田间实测，并扩展多语言、离线与本地化作物模型。","2026-10-19T23:30:08.043132Z",{"id":259,"title":260,"url":261,"summary":262,"summary_zh":263,"content":9,"source_name":264,"source_url":261,"published_at":265,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":266,"score_detail":267,"sources":270,"tags":272,"primary_topic":275,"secondary_topics":279,"classification":281,"search_phrases":296,"slug":299,"view_count":90,"doi":300,"paper":301,"created_at":313},4498,"SMART CROP SELECTION MODEL USING SOIL PH VALUE, TEMPERATURE, HUMIDITY, AND RAINFALL PREDICTION","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23211381","Crop suitability with soil and climatic conditions plays a vital role in increasing productivity of agriculture. This study proposes a smart crop selection model which recommends the suitable crop based on the machine learning techniques with the input parameters of soil pH, soil temperature, soil humidity and rainfall. The data set consisted of 1000 observations and 8 different crop classes and missing values were handled, crop labels were encoded and numerical features were selected. Five-fold cross validation was used to compare the performance of the three classifiers: Random Forest, XGBoost and Support Vector Machine (SVM). Finally, a Gradient-Boosted classification model was trained and evaluated with test-set accuracy, classification metrics, Multiclass ROC analysis and feature importance. The results of the application were 93.60% of the final test accuracy and the model comparison was used to compare the three classification methods. The contribution of four environmental parameters to the crop classification was analyzed by feature-importance analysis. To facilitate practical accessibility, the entire process was carried out using an interactive dashboard developed with Streamlit, allowing for the upload of datasets, evaluation of models, selection of parameters and the generation of predictions in real time for the crops of interest. The developed system is an example of how machine learning can be integrated with an interactive decision support interface to develop a data-driven, crop selection system, relying on simple soil and weather parameters.","作物适宜性与土壤及气候条件对提高农业生产率至关重要。本研究提出了一种智能作物选择模型，该模型基于机器学习技术，以土壤pH值、土壤温度、土壤湿度和降雨量为输入参数，推荐适宜的作物。数据集包含1000个观测值和8个不同作物类别，对缺失值进行了处理，对作物标签进行了编码，并筛选了数值特征。采用五折交叉验证比较了三种分类器——随机森林（Random Forest）、XGBoost和支持向量机（Support Vector Machine, SVM）的性能。最终，训练了一个梯度提升分类模型，并通过测试集准确率、分类指标、多类ROC分析和特征重要性进行了评估。应用结果为最终测试准确率93.60%，并通过模型比较对三种分类方法进行了对比。通过特征重要性分析，探讨了四个环境参数对作物分类的贡献。为便于实际应用，整个流程通过使用Streamlit开发的交互式仪表板实现，支持数据集上传、模型评估、参数选择以及针对感兴趣作物的实时预测生成。所开发的系统展示了如何将机器学习与交互式决策支持界面相结合，构建一个数据驱动的作物选择系统，且仅依赖于简单的土壤和气象参数。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-07T00:00:00Z",69,{"impact":20,"substance":18,"depth":111,"authority":268,"freshness":112,"relevant":21,"comment":269},13,"基于土壤pH、温湿度与降雨的机器学习选种模型，含93.6%测试精度与Streamlit交互看板，方法完整但属常规应用型研究，产业影响有限。",[271],{"name":264,"url":261},[26,27,29,273,274],"土壤墒情","作物选择",{"code":157,"slug":161,"name":276,"path":277,"parent_code":278},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[280],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":282},{"candidates":283,"agricultural_evidence":52},[284,288,292],{"code":157,"score":17,"supported":52,"title":285,"abstract":286,"tags":287},[161],[160,161],[273],{"code":39,"score":218,"supported":52,"title":289,"abstract":290,"tags":291},[],[57,54,55],[27,29],{"code":75,"score":21,"supported":14,"title":293,"abstract":294,"tags":295},[],[],[26],[297,298],"土壤pH 降雨量 作物选择模型","机器学习 作物推荐 智慧农业","土壤pH降雨量作物选择模型-4498","10.5281\u002Fzenodo.23211381",{"doi":300,"openalex_id":302,"authors":303,"venue":264,"cited_by_count":90,"oa_url":261,"card":308,"direction":192,"ingested_from":97},"W7220991946",[304,306],{"name":305,"orcid":9},"C. SAI KUMAR",{"name":307,"orcid":9},"Prof. G SREENIVASULU",{"tldr":309,"method":310,"finding":311,"direction":95,"opportunity":312},"基于土壤pH、温湿度和降雨，用机器学习构建智能选种模型并开发交互式仪表板。","1000条8类作物数据，五折交叉验证比较RF、XGBoost、SVM，最终用梯度","梯度提升模型测试准确率达93.60%，特征重要性揭示四参数对选种的贡献。","可引入时序气象与多源土壤数据，提升模型泛化性并适配不同区域作物推荐。","2026-10-09T23:30:12.285129Z",{"id":315,"title":316,"url":317,"summary":318,"summary_zh":319,"content":9,"source_name":320,"source_url":317,"published_at":321,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":322,"score_detail":323,"sources":326,"tags":328,"primary_topic":331,"secondary_topics":332,"classification":333,"search_phrases":349,"slug":352,"view_count":83,"doi":353,"paper":354,"created_at":374},4108,"Visión artificial para la detección de plagas en cultivos de tomate: revisión sistemática de la literatura","https:\u002F\u002Fdoi.org\u002F10.36825\u002Friti.14.34.010","Phytosanitary monitoring is an essential activity for reducing production losses caused by pests in tomato crops grown under protected agriculture conditions. In recent years, computer vision and deep learning methods have gained relevance as tools capable of automating agricultural image detection and analysis processes. Despite these advances, there is still a need to determine which techniques have been used, under what conditions they have been evaluated, and what performance they have reported, particularly in protected agriculture and small-scale production contexts. This study aims to examine research published on these technologies and assess their potential incorporation into participatory agricultural monitoring schemes. To this end, a systematic review of scientific literature published between 2020 and 2026 was conducted following the methodological guidelines proposed by Kitchenham. The review was developed through the stages of planning, study search and selection, and information synthesis, considering previously established inclusion and exclusion criteria. As a result, fifty studies related to image processing, artificial intelligence, and phytosanitary monitoring were selected and analyzed. Among the main findings of the review, several studies reported high-performance levels for deep learning models in tasks involving the automatic identification of symptoms associated with pests and other crop-related conditions. The synthesis identified the main techniques, data, application contexts, and reported performance levels, providing reference elements for assessing their potential incorporation into participatory agricultural monitoring schemes.","植物检疫监测是减少保护地番茄种植中由有害生物造成的产量损失的一项重要活动。近年来，计算机视觉和深度学习方法作为能够自动化农业图像检测与分析流程的工具而日益受到重视。尽管取得了这些进展，但仍需明确已采用了哪些技术、这些技术在何种条件下进行了评估，以及它们报告了怎样的性能，尤其是在保护地农业和小规模生产情境下。本研究旨在考察已发表的关于这些技术的研究，并评估其纳入参与式农业监测方案的潜力。为此，按照Kitchenham提出的方法学指南，对2020年至2026年间发表的科学文献进行了系统综述。该综述通过规划、研究检索与筛选以及信息综合等阶段展开，并依据事先确立的纳入与排除标准。结果共筛选并分析了50项与图像处理、人工智能和植物检疫监测相关的研究。在综述的主要发现中，多项研究报告了深度学习模型在自动识别与有害生物相关的症状及其他作物相关状况的任务中具有高性能水平。该综合识别了主要技术、数据、应用情境及所报告的性能水平，为评估其纳入参与式农业监测方案的潜力提供了参考要素。","Revista de Investigación en Tecnologías de la Información","2026-10-02T00:00:00Z",63,{"impact":20,"substance":324,"depth":17,"authority":13,"freshness":112,"relevant":21,"comment":325},18,"系统综述梳理2020-2026年计算机视觉与深度学习在番茄病虫害检测中的应用，方法规范、结论有参考价值，但属细分领域综述，公共影响有限。",[327],{"name":320,"url":317},[26,27,28,329,330],"番茄种植","病虫害监测",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":334},{"candidates":335,"agricultural_evidence":52},[336,341,345],{"code":39,"score":13,"supported":52,"title":337,"abstract":338,"tags":340},[],[136,117,137,339,134],"artificial intelligence",[27],{"code":150,"score":224,"supported":52,"title":342,"abstract":343,"tags":344},[],[153,154],[329],{"code":75,"score":21,"supported":14,"title":346,"abstract":347,"tags":348},[],[],[26],[350,351],"计算机视觉 番茄 病虫害检测","深度学习 设施农业 植保监测","计算机视觉番茄病虫害检测-4108","10.36825\u002Friti.14.34.010",{"doi":353,"openalex_id":355,"authors":356,"venue":320,"cited_by_count":90,"oa_url":317,"card":369,"direction":95,"ingested_from":97},"W7216085028",[357,359,362,365,367],{"name":358,"orcid":9},"Jesús Alberto Alarcón Luna",{"name":360,"orcid":361},"María Salomé Alejandre Apolinar","https:\u002F\u002Forcid.org\u002F0009-0008-0232-8217",{"name":363,"orcid":364},"Hugo Amores Pérez","https:\u002F\u002Forcid.org\u002F0009-0009-0260-4872",{"name":366,"orcid":9},"Jacel Adame García",{"name":368,"orcid":9},"María Angélica Cerdán",{"tldr":370,"method":371,"finding":372,"direction":95,"opportunity":373},"系统综述2020-2026年计算机视觉与深度学习在番茄设施栽培虫害检测中的研究。","遵循Kitchenham指南系统检索筛选50篇文献，归纳技术、数据与性能。","深度学习模型在虫害症状自动识别任务中普遍报告高性能，但多限于特定条件。","面向设施农业与小规模生产的轻量化模型及参与式监测集成仍属研究空白。","2026-10-03T23:30:53.163959Z",{"id":376,"title":377,"url":378,"summary":379,"summary_zh":9,"content":9,"source_name":380,"source_url":9,"published_at":381,"category":382,"cover_url":9,"hotness":13,"is_selected":14,"score":383,"score_detail":384,"sources":387,"tags":389,"primary_topic":392,"secondary_topics":393,"classification":398,"search_phrases":417,"slug":420,"view_count":83,"doi":9,"paper":9,"created_at":421},3935,"知识与数据双驱动赋能温室番茄生长精准预测——中国农科院环发所研究成果发表","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fkyhd\u002F613bca00d9e84cfeb977251a606af346.htm","近日，中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队构建了知识与数据双驱动模型，实现温室番茄生长的高精度无损预测，研究成果发表在《农业计算机与电子学（Computers and Electronics in Agriculture）》上。科研团队提出一种尺度自适应时序卷积混合模型，根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测。","中国农业科学院·2026年10月","2026-10-01T00:00:00Z","报道",82,{"impact":324,"substance":385,"depth":324,"authority":17,"freshness":205,"relevant":21,"comment":386},22,"国家级科研机构在核心期刊发表的知识与数据双驱动温室番茄生长预测模型，方法新颖、结论可靠，对智慧农业与设施种植有实质参考价值。",[388],{"name":380,"url":378},[26,27,28,390,391],"温室番茄","生长预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[394],{"code":150,"slug":395,"name":396,"path":397,"parent_code":36},"vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":45,"locked":14,"version":46,"method":47,"evidence":399},{"candidates":400,"agricultural_evidence":52},[401,405,409,413],{"code":32,"score":20,"supported":52,"title":402,"abstract":403,"tags":404},[64],[64],[390],{"code":150,"score":218,"supported":52,"title":406,"abstract":407,"tags":408},[153],[153],[],{"code":75,"score":21,"supported":14,"title":410,"abstract":411,"tags":412},[],[],[26],{"code":39,"score":21,"supported":14,"title":414,"abstract":415,"tags":416},[],[],[27],[418,419],"中国农科院环发所 温室番茄 生长预测","知识与数据双驱动 番茄 模型","中国农科院环发所温室番茄生长预测-3935","2026-10-02T11:53:11.319506Z",{"id":423,"title":424,"url":425,"summary":426,"summary_zh":427,"content":9,"source_name":428,"source_url":425,"published_at":429,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":430,"score_detail":431,"sources":433,"tags":435,"primary_topic":437,"secondary_topics":438,"classification":439,"search_phrases":478,"slug":481,"view_count":68,"doi":482,"paper":483,"created_at":493},3197,"YieldVision: Model-Driven Decision Support System for Crop Yield Prediction","https:\u002F\u002Fdoi.org\u002F10.56201\u002Frjpst.vol.9.no1.2026.pg176.194","Agriculture, a cornerstone of global food security, faces unprecedented challenges in achieving sustainable productivity. Crop yield prediction, a crucial aspect of agricultural planning, suffers from inefficiencies rooted in technological and methodological gaps. While traditional approaches rely on historical data and empirical estimations, they often fail to address the complexity of environmental interactions because they lack real-time data integration and limited access to accurate data, leading to inaccuracies and resource mismanagement. Addressing these issues requires the development of advanced Machine Learning (ML) models-based system that can predict crop yield with high accuracy in dynamic environments. This paper presents YieldVision, a decision support system driven by advanced ML models for Crop Yield Prediction based on environmental, soil, and crop-specific factors. In this work, we proposed three crop prediction models: Random Forest (RF), Long Short Time Memory (LSTM) and Hybrid (RF LSTM). RF regressor effectively captured the complex, nonlinear relationships among soil nutrients, fertilizer application, and temperature. The LSTM network learned the way short-term weather fluctuations impact crop development over time, and the hybrid model combined LSTM for environmental-related factors, and RF for soil-related factors. Experimental results on the Crop Yield Prediction Dataset containing soil and weather parameters showed that RF has the highest accuracy 𝑅2 = 99.10% and outperforms both LSTM and hybrid which have 87.50% and 97.30% accuracy respectively, confirming its suitability for the dataset. RF has the lowest RMSE and MAE compared to LSTM and hybrid models. The models are integrated into a decision support system that is deployed to operationalize the models in real-world settings with user interfaces accessible via mobile or web-delivered real-time alerts and yield forecasts to farmers and agronomists.","农业作为全球粮食安全的基石，在实现可持续生产力方面面临着前所未有的挑战。作物产量预测作为农业规划的关键环节，因技术和方法上的不足而效率低下。传统方法依赖历史数据和经验估算，但由于缺乏实时数据集成且难以获取准确数据，往往无法应对环境相互作用的复杂性，导致预测不准确和资源管理不善。解决这些问题需要开发基于先进机器学习（ML）模型的系统，能够在动态环境中高精度地预测作物产量。本文提出了YieldVision，一个由先进机器学习模型驱动的决策支持系统，用于基于环境、土壤和作物特定因素的作物产量预测。在本研究中，我们提出了三种作物预测模型：随机森林（RF）、长短期记忆网络（LSTM）和混合模型（RF-LSTM）。随机森林回归器有效捕捉了土壤养分、施肥量和温度之间复杂的非线性关系。LSTM网络学习了短期天气波动如何随时间影响作物发育，而混合模型将LSTM用于环境相关因素，RF用于土壤相关因素。在包含土壤和天气参数的作物产量预测数据集上的实验结果表明，随机森林具有最高的准确率𝑅2 = 99.10%，优于LSTM和混合模型，后两者的准确率分别为87.50%和97.30%，证实了随机森林对该数据集的适用性。与LSTM和混合模型相比，随机森林具有最低的RMSE和MAE。这些模型被集成到一个决策支持系统中，该系统已部署用于在实际环境中运行这些模型，其用户界面可通过移动端或网页访问，向农民和农艺师提供实时警报和产量预测。","RESEARCH JOURNAL OF PURE SCIENCE AND TECHNOLOGY","2026-09-21T00:00:00Z",68,{"impact":20,"substance":18,"depth":111,"authority":20,"freshness":112,"relevant":21,"comment":432},"提出RF、LSTM及混合模型并集成决策支持系统，方法对比与精度数据扎实，对智慧农业产量预测有参考价值，但属单篇论文且来源期刊影响力有限。",[434],{"name":428,"url":425},[26,27,436,57,29],"产量预测",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":440},{"candidates":441,"agricultural_evidence":52},[442,446,453,459,463,469,473],{"code":39,"score":18,"supported":52,"title":443,"abstract":444,"tags":445},[55],[57,54,55],[27,29,57],{"code":447,"score":68,"supported":52,"title":448,"abstract":449,"tags":452},"A8",[],[450,451],"粮食安全","food security",[],{"code":235,"score":68,"supported":52,"title":454,"abstract":455,"tags":458},[],[456,457],"施肥","fertilizer",[],{"code":157,"score":68,"supported":52,"title":460,"abstract":461,"tags":462},[],[160,161],[],{"code":464,"score":83,"supported":52,"title":465,"abstract":466,"tags":468},"J1",[],[467],"农业规划",[],{"code":75,"score":21,"supported":14,"title":470,"abstract":471,"tags":472},[],[],[26],{"code":474,"score":21,"supported":14,"title":475,"abstract":476,"tags":477},"I3",[],[],[436],[479,480],"YieldVision 作物产量预测","RF LSTM 混合模型 产量预测","YieldVision作物产量预测-3197","10.56201\u002Frjpst.vol.9.no1.2026.pg176.194",{"doi":482,"openalex_id":484,"authors":485,"venue":428,"cited_by_count":90,"oa_url":9,"card":488,"direction":95,"ingested_from":97},"W7213883348",[486],{"name":487,"orcid":9},"D.J.S. Sako",{"tldr":489,"method":490,"finding":491,"direction":95,"opportunity":492},"提出YieldVision决策支持系统，用RF、LSTM及混合模型预测作物产量并部署应用。","基于土壤、环境与作物数据，构建RF、LSTM和RF-LSTM混合模型并集成到We","随机森林表现最佳，R²达99.10%，优于LSTM（87.50%）和混合模型（97.30%）。","可探索多源实时数据融合与模型可解释性，提升跨区域泛化能力并降低对高精度历史数据的依赖。","2026-09-22T23:30:43.591303Z"]