[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4657":3,"related-4657":94},{"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":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":74,"slug":77,"view_count":20,"doi":78,"paper":79,"created_at":93},4657,"Agriculture and Farmer Digital Assistant: An AI-Based Digital Decision Support Platform for Smart Farming","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23278187","Abstract Farmers frequently make decisions under uncertain weather, incomplete agricultural information, changing soil conditions, and limited access to timely expert guidance. The Agriculture and Farmer Digital Assistant (AFDA) is a web-based digital decision-support platform designed to bring agricultural data, artificial intelligence, recommendation services, weather information, and role-based administration into a single system. The platform accepts agricultural inputs from farmers and processes them to provide crop recommendations and basic farming guidance. The design also considers soil analysis, pest and disease support, market information, notifications, multilingual assistance, and future sensor integration. The implemented project version focuses on user registration and login, agricultural data input, crop recommendation, basic weather guidance, and a recommendation dashboard, while several advanced capabilities remain future extensions. A layered architecture separates the user interface, application logic, AI processing, database, and notification\u002Frecommendation functions. The project further defines functional and non-functional requirements, data-flow and entity relationships, user workflows, testing procedures, and maintenance considerations. The reported evaluation is functional rather than numerical: the major input, authentication, recommendation, navigation, and output-display workflows were checked during development, while no measured accuracy or response-time benchmark is claimed. AFDA therefore provides a foundation for a connected and extensible agricultural assistance system intended to support better-informed and more sustainable farming decisions. Keywords Agriculture and Farmer Digital Assistant, AFDA, artificial intelligence, machine learning, crop recommendation, smart farming, agricultural decision support, weather guidance, role-based access control.","农户常常在天气不确定、农业信息不完整、土壤条件不断变化以及难以及时获得专家指导的情况下作出决策。农业与农户数字助手（Agriculture and Farmer Digital Assistant，AFDA）是一个基于网络的数字决策支持平台，旨在将农业数据、人工智能、推荐服务、天气信息和基于角色的管理整合到一个系统中。该平台接收农户输入的农业数据，并对其进行处理，以提供作物推荐和基本农耕指导。其设计还考虑了土壤分析、病虫害支持、市场信息、通知、多语言辅助以及未来的传感器集成。已实现的项目版本侧重于用户注册与登录、农业数据输入、作物推荐、基本天气指导以及推荐仪表板，而若干高级功能仍留作未来扩展。分层架构将用户界面、应用逻辑、人工智能处理、数据库以及通知\u002F推荐功能相互分离。该项目进一步定义了功能性与非功能性需求、数据流与实体关系、用户工作流程、测试程序以及维护方面的考虑。所报告的评估是功能性的而非数值性的：在开发过程中检查了主要输入、身份验证、推荐、导航和输出显示等工作流程，但未声称有任何经过测量的准确性或响应时间基准。因此，AFDA为一个互联且可扩展的农业辅助系统提供了基础，旨在支持更明智且更可持续的农耕决策。关键词：农业与农户数字助手，AFDA，人工智能，机器学习，作物推荐，智慧农业，农业决策支持，天气指导，基于角色的访问控制。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z","论文",10,false,54,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":13,"relevant":20,"comment":21},8,14,12,1,"面向农户的AI决策支持平台论文，架构与需求设计较完整，但仅做功能验证、无精度或性能数据，属早期原型，参考价值有限。",[23],{"name":10,"url":6},[25,26,27,28,29],"数字农业","智慧农业","农业人工智能","作物推荐","农业决策支持",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,53,61,67],{"code":31,"score":44,"supported":45,"title":46,"abstract":48,"tags":52},18,true,[47],"decision support",[49,50,51,47],"机器学习","machine learning","artificial intelligence",[27],{"code":54,"score":55,"supported":45,"title":56,"abstract":57,"tags":60},"H2",6,[],[58,59],"土壤","soil",[],{"code":62,"score":63,"supported":45,"title":64,"abstract":65,"tags":66},"I1",5,[],[26],[25,26],{"code":68,"score":69,"supported":45,"title":70,"abstract":71,"tags":73},"I6",3,[],[72],"agricultural data",[],[75,76],"AFDA 农业数字助手","农业人工智能 农业决策支持 作物推荐 数字农业","AFDA农业数字助手-4657","10.5281\u002Fzenodo.23278187",{"doi":78,"openalex_id":80,"authors":81,"venue":10,"cited_by_count":84,"oa_url":6,"card":85,"direction":91,"ingested_from":92},"W7221331787",[82],{"name":83,"orcid":9},"B. Siddhu, G. Naveen, S. Guru Charan, T. Ajay, C. Naresh",0,{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"开发了基于AI的农业数字决策支持平台AFDA，为农民提供作物推荐和农事指导。","采用分层架构的Web平台，集成AI处理、数据库和推荐服务，支持多语言。","平台实现了用户认证、数据输入、作物推荐和天气指导等核心功能，但未提供精度或响应时间基准。","农业人工智能与决策模型","可补充传感器实时数据集成与模型精度评估，提升决策支持的可靠性和实时性。","智慧农业 \u002F 农业物联网","openalex","2026-10-11T23:30:07.538409Z",{"total":55,"page":20,"page_size":55,"items":95},[96,154,222,280,343,435],{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":9,"source_name":10,"source_url":99,"published_at":102,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":103,"score_detail":104,"sources":108,"tags":110,"primary_topic":113,"secondary_topics":114,"classification":115,"search_phrases":130,"slug":133,"view_count":20,"doi":134,"paper":135,"created_at":153},5194,"A Comprehensive Review on Multi-Modal Large Language Models and Agentic Architectures for Precision Agricultural Decision Support","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23411181","This article reviews recent developments in multi-modal large language models and agentic architectures for agricultural decision support. Forty-five representative studies published between 2023 and 2026 were selected across perception, language, orchestration, soil intelligence and governance, organised under a four-layer taxonomy, and classified against nine functional capabilities using a stated, reproducible procedure. The comparative analysis exposes a pronounced imbalance. Crop and disease perception is covered by 36 per cent of the selected studies, while dose-bearing prescription appears in 7 per cent, profit-aware recommendation in 4 per cent, and persistent per-field memory in a single study. Advisory tools in this literature therefore remain unable to exchange state: image-based scanners are rarely conditioned on measured soil chemistry, and crop-recommendation models optimise agronomic suitability without reference to farm economics. On this basis the article synthesises a conceptual reference architecture from the reviewed literature. It is a literature-derived synthesis rather than an implemented system, and calibrated abstention, evaluation protocols for longitudinal memory, and vernacular voice coverage emerge as the most urgent gaps.","本文综述了面向农业决策支持的多模态大语言模型与智能体架构的最新进展。按照可复现的既定流程，选取2023年至2026年间发表的45项代表性研究，涵盖感知、语言、编排、土壤智能与治理等方向，将其纳入一个四层分类体系，并依据九项功能能力进行分类。比较分析揭示出明显的不均衡：作物与病害感知在所选研究中占36%，而含剂量处方的研究仅占7%，利润感知推荐占4%，持久性田块记忆仅见于一项研究。因此，该领域的咨询工具仍无法交换状态：基于图像的扫描器很少以实测土壤化学状况为条件，作物推荐模型在优化农艺适宜性时也未考虑农场经济性。在此基础上，本文综合所综述文献提出一个概念性参考架构。该架构是文献衍生的综合框架，而非已实现的系统；校准弃权、纵向记忆的评估协议以及方言语音覆盖被列为最紧迫的空白。","2026-10-16T00:00:00Z",63,{"impact":19,"substance":44,"depth":105,"authority":17,"freshness":106,"relevant":20,"comment":107},16,9,"系统性综述揭示处方决策与农场经济维度严重缺位，对农业AI研发方向有参考价值，但属文献综述而非实证突破，影响范围有限。",[109],{"name":10,"url":99},[26,27,111,112,29],"智能体","多模态大模型",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":116},{"candidates":117,"agricultural_evidence":45},[118,122,126],{"code":31,"score":18,"supported":45,"title":119,"abstract":120,"tags":121},[47],[47],[27,112,111],{"code":54,"score":55,"supported":45,"title":123,"abstract":124,"tags":125},[],[58,59],[],{"code":62,"score":20,"supported":14,"title":127,"abstract":128,"tags":129},[],[],[26],[131,132],"多模态大模型 农业决策支持","农业人工智能 农业决策支持 多模态大模型 智慧农业","多模态大模型农业决策支持-5194","10.5281\u002Fzenodo.23411181",{"doi":134,"openalex_id":136,"authors":137,"venue":10,"cited_by_count":84,"oa_url":99,"card":148,"direction":89,"ingested_from":92},"W7222605322",[138,140,142,144,146],{"name":139,"orcid":9},"Tanmay Bhamre",{"name":141,"orcid":9},"Yash Barde",{"name":143,"orcid":9},"Devendra Yeole",{"name":145,"orcid":9},"Gajanan Chaudhary",{"name":147,"orcid":9},"Amarsinh Verpe",{"tldr":149,"method":150,"finding":151,"direction":89,"opportunity":152},"综述多模态大模型与智能体架构在精准农业决策支持中的应用与不足。","筛选2023-2026年45篇代表性研究，按四层分类与九项功能能力归类比较。","感知研究占36%，处方仅7%、利润推荐4%、田块记忆仅1篇，系统难以交换状态。","可研究跨模态状态交换、纵向记忆评测协议与方言语音覆盖，填补处方与经济决策空白。","2026-10-17T23:30:55.282694Z",{"id":155,"title":156,"url":157,"summary":158,"summary_zh":159,"content":9,"source_name":160,"source_url":157,"published_at":161,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":162,"score_detail":163,"sources":165,"tags":167,"primary_topic":170,"secondary_topics":171,"classification":172,"search_phrases":198,"slug":201,"view_count":202,"doi":203,"paper":204,"created_at":221},4990,"Smart Crop Advisory System","https:\u002F\u002Fdoi.org\u002F10.55041\u002Fijcope.v2i10.081","The Smart Crop Advisory System is a web-based agricultural decision-support platform that uses Artificial Intelligence (AI) and Machine Learning (ML) technologies to assist farmers in making informed farming decisions. Traditional agriculture faces several challenges, including unsuitable crop selection, unpredictable weather conditions, crop diseases, and fluctuations in market prices. The proposed system integrates essential agricultural services, including crop recommendation, weather forecasting, crop disease detection, agricultural market price information, and an AI-based multilingual voice assistant. It also provides farmer profile management, crop price trend analysis, and rainfall trend visualization through an interactive dashboard. The crop recommendation module helps farmers identify suitable crops based on relevant agricultural parameters, while the disease detection module supports the identification of potential crop diseases. Weather and market information help farmers plan agricultural activities and make informed selling decisions. The multilingual voice assistant aims to improve accessibility by enabling farmers to interact with the system through voice-based communication. Overall, the Smart Crop Advisory System aims to improve access to agricultural information, support effective decision-making, promote efficient resource utilization, and encourage sustainable farming practices. Keywords: Smart Agriculture, Crop Recommendation, Machine Learning, Crop Disease Detection, Weather Forecasting, Market Price Analysis, Voice Assistant, Sustainable Agriculture.","智能作物咨询系统是一个基于网络的农业决策支持平台，利用人工智能（AI）和机器学习（ML）技术帮助农民做出明智的农作决策。传统农业面临诸多挑战，包括作物选择不当、天气条件不可预测、作物病害以及市场价格波动。所提出的系统整合了基本农业服务，包括作物推荐、天气预报、作物病害检测、农产品市场价格信息以及基于AI的多语言语音助手。它还通过交互式仪表板提供农民档案管理、作物价格趋势分析和降雨趋势可视化。作物推荐模块帮助农民根据相关农业参数识别适宜作物，而病害检测模块支持识别潜在作物病害。天气和市场信息帮助农民规划农业活动并做出明智的销售决策。多语言语音助手旨在通过使农民能够以语音方式与系统交互来提高可及性。总体而言，智能作物咨询系统旨在改善农业信息的获取，支持有效决策，促进资源高效利用，并鼓励可持续农业实践。关键词：智慧农业、作物推荐、机器学习、作物病害检测、天气预报、市场价格分析、语音助手、可持续农业。","International Journal of Creative and Open Research in Engineering and Management","2026-10-14T00:00:00Z",48,{"impact":17,"substance":18,"depth":19,"authority":55,"freshness":17,"relevant":20,"comment":164},"面向农户的AI农业决策支持系统论文，功能集成度较高但方法新颖性与数据规模有限，属细分领域进展，可作为智慧农业主题聚合素材。",[166],{"name":160,"url":157},[26,27,168,169,28],"病虫害识别","农业气象",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":173},{"candidates":174,"agricultural_evidence":45},[175,180,186,193],{"code":31,"score":176,"supported":45,"title":177,"abstract":178,"tags":179},11,[],[49,50,51],[27,168],{"code":62,"score":181,"supported":45,"title":182,"abstract":183,"tags":185},7,[],[26,184],"smart agriculture",[26],{"code":187,"score":55,"supported":45,"title":188,"abstract":189,"tags":192},"H8",[],[190,191],"可持续农业","sustainable agriculture",[],{"code":194,"score":20,"supported":14,"title":195,"abstract":196,"tags":197},"H7",[],[],[169],[199,200],"AI 作物推荐 语音助手","农业人工智能 病虫害识别 作物推荐 农业气象","AI作物推荐语音助手-4990",2,"10.55041\u002Fijcope.v2i10.081",{"doi":203,"openalex_id":205,"authors":206,"venue":160,"cited_by_count":84,"oa_url":215,"card":216,"direction":91,"ingested_from":92},"W7221803029",[207,209,211,213],{"name":208,"orcid":9},"Bhuvana H P",{"name":210,"orcid":9},"Kavya C M",{"name":212,"orcid":9},"Manjula S",{"name":214,"orcid":9},"Priyanka A","https:\u002F\u002Fijcope.org\u002Fwp-content\u002Fuploads\u002F2026\u002F10\u002FSmart-Crop-Advisory-System.pdf",{"tldr":217,"method":218,"finding":219,"direction":89,"opportunity":220},"构建基于AI\u002FML的Web农业决策支持平台，为农民提供作物推荐、病害检测、天气与市场价格等服务。","集成机器学习模型、多语言语音助手与交互式仪表盘，整合天气、市场及农业参数数据。","系统可提升农业信息获取与决策效率，促进资源高效利用和可持续耕作。","可探索多源数据融合的轻量化模型与本地化语音交互，提升小农户采纳率与实时决策精度。","2026-10-15T23:30:09.048921Z",{"id":223,"title":224,"url":225,"summary":226,"summary_zh":227,"content":9,"source_name":228,"source_url":225,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":229,"score_detail":230,"sources":233,"tags":235,"primary_topic":238,"secondary_topics":239,"classification":240,"search_phrases":254,"slug":257,"view_count":84,"doi":258,"paper":259,"created_at":279},4695,"AgroNLP-SimAssist: a multilingual transformer-based decision support simulator for crop disease diagnosis, advisory generation, and market intelligence","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12870-026-10040-8","Abstract There are three major shortcomings of agricultural NLP and need addressing, regardless of the recent developments in the field: (i) use of proprietary or small-scale datasets that affect reproducibility; (ii) one-task models that only focus on intent recognition but lack integration for crop disease diagnosis, advice, and market information; and (iii) the absence of simulators for evaluating the system’s performance during actual deployment. The application of precision agriculture is constrained by limitations in diverse farming environments. The study introduces AgroNLP-SimAssist, which represents a unified multilingual transformer architecture augmented by an easily deployable agricultural decision support simulator, as the proposed solution to overcome the challenges. The five major components constituting the proposed framework are: (1) domain-specific multilingual transformer embedding; (2) data collection and cleaning from publicly available agricultural datasets; (3) multiple prediction tasks including trend price estimation, crop disease detection, and advisory recommendation; (4) SHAP value explainability for expert validation; and (5) advisory simulation engine for evaluating latency and scalability. Multilingually available data sources, which include multilingual agricultural reports from the FAO organization, crop-disease descriptions from the PlantVillage knowledge base, and price-market narratives from the Agmarknet website, have been used for training and validation purposes. Among the 3,200 multilingual farmer queries dataset (in English, Hindi, and Kannada), there was an assignment of 70%, 15%, and 15% to the training, validation, and testing sets, respectively. In terms of accuracy compared to the existing classifiers, results from experiments yielded 96.1% disease classification, 94.3% advisory relevance, and 92.7% named entity recognition with F1-measure. The simulation system scaled up to 10,000 concurrent users with an inference latency of 0.35 s\u002Fquery. The proposed data-driven AgroNLP-SimAssist framework facilitates extension services and precision agriculture through providing a scalable, reproducible, and interpretable solution for intelligent agricultural advisory systems.","尽管近年来农业自然语言处理（NLP）领域取得了进展，但仍存在三个主要不足亟需解决：(i) 使用专有或小规模数据集，影响可复现性；(ii) 单任务模型仅关注意图识别，缺乏对作物病害诊断、建议和市场信息的整合；(iii) 缺乏用于评估系统实际部署性能的模拟器。精准农业的应用受到多样化农业环境的限制。本研究提出了AgroNLP-SimAssist，一种统一的多语言Transformer架构，并辅以易于部署的农业决策支持模拟器，作为克服上述挑战的解决方案。该框架由五个主要组件构成：(1) 领域特定的多语言Transformer嵌入；(2) 从公开农业数据集中进行数据收集与清洗；(3) 多预测任务，包括趋势价格估计、作物病害检测和咨询推荐；(4) SHAP值可解释性用于专家验证；(5) 咨询模拟引擎用于评估延迟和可扩展性。多语言可用数据源已用于训练和验证，包括联合国粮农组织（FAO）的多语言农业报告、PlantVillage知识库的作物病害描述以及Agmarknet网站的价格市场叙述。在包含3200条多语言农民查询数据集（英语、印地语和卡纳达语）中，训练集、验证集和测试集的分配比例分别为70%、15%和15%。与现有分类器相比，实验结果显示病害分类准确率达96.1%，咨询相关性达94.3%，命名实体识别的F1值为92.7%。模拟系统可扩展至10000名并发用户，推理延迟为0.35秒\u002F查询。所提出的数据驱动AgroNLP-SimAssist框架通过为智能农业咨询系统提供可扩展、可复现且可解释的解决方案，促进了推广服务和精准农业的发展。","BMC Plant Biology",81,{"impact":44,"substance":231,"depth":44,"authority":18,"freshness":106,"relevant":20,"comment":232},22,"多语言农业大模型整合病害诊断、农技咨询与市场情报，数据规模与仿真验证扎实，对智慧农业信息化有参考价值。",[234],{"name":228,"url":225},[26,27,236,29,237],"农业大模型","作物病害诊断",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":241},{"candidates":242,"agricultural_evidence":45},[243,248],{"code":31,"score":244,"supported":45,"title":245,"abstract":246,"tags":247},13,[47],[47],[27,236],{"code":62,"score":181,"supported":45,"title":249,"abstract":250,"tags":253},[],[251,252],"精准农业","precision agriculture",[26],[255,256],"AgroNLP-SimAssist 作物病害诊断","多语言农业大模型 农业决策支持","AgroNLP-SimAssist作物病害诊断-4695","10.1186\u002Fs12870-026-10040-8",{"doi":258,"openalex_id":260,"authors":261,"venue":228,"cited_by_count":84,"oa_url":225,"card":274,"direction":89,"ingested_from":92},"W7221339938",[262,264,266,268,270,272],{"name":263,"orcid":9},"Priya Thirugnanam",{"name":265,"orcid":9},"Sandeep kumar Mathivanan",{"name":267,"orcid":9},"Sundara Kumar M R",{"name":269,"orcid":9},"Sangeetha Ramaswamy",{"name":271,"orcid":9},"Shruthi T S",{"name":273,"orcid":9},"Sangeetha S.K.B",{"tldr":275,"method":276,"finding":277,"direction":89,"opportunity":278},"提出多语言Transformer农业决策支持模拟器，统一实现作物病害诊断、农技建议与市场情报。","基于FAO、PlantVillage、Agmarknet多语数据训练Transf","病害分类96.1%、建议相关性94.3%、NER F1 92.7%，万级并发延迟0.35秒。","可扩展至更多语种与作物，并引入真实田间部署反馈闭环优化模拟器。","2026-10-11T23:30:56.064196Z",{"id":281,"title":282,"url":283,"summary":284,"summary_zh":285,"content":9,"source_name":10,"source_url":283,"published_at":286,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":287,"score_detail":288,"sources":290,"tags":292,"primary_topic":294,"secondary_topics":298,"classification":300,"search_phrases":323,"slug":326,"view_count":69,"doi":327,"paper":328,"created_at":342},4374,"AI-Driven Crop Recommendation and Growth Prediction System Using Soil Fertility and Climate Intelligence","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23179023","Agricultural decisions are increasingly affected by changing climate conditions, soil fertility, and the availability of timely technical guidance. This mini project presents an AI-Driven Agricultural Intelligence System that combines crop recommendation, soil fertility assessment, weather-risk prediction, yield estimation, fertilizer recommendation, and multilingual conversational support in one web platform. The system accepts soil and climate parameters such as nitrogen, phosphorus, potassium, pH, temperature, humidity, rainfall, location, crop, and season. The supplied implementation uses a Random Forest model for crop recommendation, XGBoost for weather-risk prediction, a Random Forest regressor for yield estimation, a Random Forest classifier for fertilizer recommendation, and a deterministic rule-based soilfertility module with model-independent fallback logic. A FastAPI backend performs validation, model loading, chained inference, weather integration, authentication, and response formatting, while a React-based Progressive Web Application provides the user interface. OpenWeatherMap supplies current weather context and Google Gemini AI supports multilingual agricultural explanations with offline FAQ and rulebased fallbacks. Reported module performance is approximately 88-95%, and representative API, fallback, authentication, translation, rate-limiting, and chatbot tests pass in the supplied project material. The project demonstrates how machine learning, web development, external APIs, and multilingual AI can be combined to deliver practical, accessible, and data-driven agricultural decision support. Keywords: Machine Learning, Agriculture, Crop Recommendation, Soil Fertility, Weather Risk, Yield Prediction, Fertilizer Recommendation, FastAPI, React, XGBoost, Google Gemini AI, Precision Agriculture.","农业决策日益受到气候条件变化、土壤肥力以及及时技术指导可得性的影响。本小型项目提出了一套人工智能驱动的农业智能系统，将作物推荐、土壤肥力评估、天气风险预测、产量估算、肥料推荐和多语言对话支持集成于一个网络平台。该系统接受土壤和气候参数，如氮、磷、钾、pH值、温度、湿度、降雨量、地点、作物和季节。所提供的实现采用随机森林（Random Forest）模型进行作物推荐，采用XGBoost进行天气风险预测，采用随机森林回归器进行产量估算，采用随机森林分类器进行肥料推荐，并采用基于确定性规则的土壤肥力模块，具备与模型无关的回退逻辑。FastAPI后端执行验证、模型加载、链式推理、天气集成、身份认证和响应格式化，而基于React的渐进式网络应用提供用户界面。OpenWeatherMap提供当前天气上下文，Google Gemini AI支持多语言农业解释，并具备离线FAQ和基于规则的回退机制。所报告的模块性能约为88-95%，且在所提供的项目材料中，具有代表性的API、回退、身份认证、翻译、速率限制和聊天机器人测试均通过。该项目展示了如何将机器学习、网络开发、外部API和多语言AI相结合，以提供实用、可及且数据驱动的农业决策支持。关键词：机器学习、农业、作物推荐、土壤肥力、天气风险、产量预测、肥料推荐、FastAPI、React、XGBoost、Google Gemini AI、精准农业。","2026-10-06T00:00:00Z",50,{"impact":17,"substance":18,"depth":244,"authority":55,"freshness":106,"relevant":20,"comment":289},"学生级迷你项目论文，方法组合常规、性能数据自报，工程整合有一定参考价值但学术与产业增量有限，可作智慧农业应用案例收录。",[291],{"name":10,"url":283},[26,27,251,28,293],"土壤肥力",{"code":54,"slug":59,"name":295,"path":296,"parent_code":297},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[299],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":301},{"candidates":302,"agricultural_evidence":45},[303,308,312,316],{"code":54,"score":304,"supported":45,"title":305,"abstract":306,"tags":307},15,[59],[58,59],[293],{"code":31,"score":13,"supported":45,"title":309,"abstract":310,"tags":311},[],[49,50,47],[27],{"code":62,"score":17,"supported":45,"title":313,"abstract":314,"tags":315},[],[251,252],[26,251],{"code":317,"score":55,"supported":45,"title":318,"abstract":319,"tags":322},"F3",[],[320,321],"肥料","fertilizer",[],[324,325],"AI 作物推荐 土壤肥力","XGBoost 天气风险 产量预测","AI作物推荐土壤肥力-4374","10.5281\u002Fzenodo.23179023",{"doi":327,"openalex_id":329,"authors":330,"venue":10,"cited_by_count":84,"oa_url":283,"card":337,"direction":89,"ingested_from":92},"W7220664944",[331,333,335],{"name":332,"orcid":9},"Gunashekar B.",{"name":334,"orcid":9},"Saiteja G.",{"name":336,"orcid":9},"Maniteja K.",{"tldr":338,"method":339,"finding":340,"direction":89,"opportunity":341},"构建集成作物推荐、产量预测、肥料建议与多语言问答的AI农业决策Web平台。","随机森林、XGBoost、FastAPI、React PWA、OpenWeath","各模块准确率约88-95%，API、认证、翻译与聊天机器人测试均通过。","可引入真实田间时序数据与可解释性，验证模型跨区域跨季节泛化能力。","2026-10-07T23:30:47.248380Z",{"id":344,"title":345,"url":346,"summary":347,"summary_zh":348,"content":9,"source_name":349,"source_url":346,"published_at":350,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":351,"score_detail":352,"sources":355,"tags":357,"primary_topic":360,"secondary_topics":361,"classification":367,"search_phrases":404,"slug":407,"view_count":69,"doi":408,"paper":409,"created_at":434},4165,"An AI-Driven Smart Farming System for Crop Recommendation and Plant Disease Detection","https:\u002F\u002Fdoi.org\u002F10.48084\u002Fetasr.20031","Climate variability, soil degradation, water scarcity, and plant diseases are all contributing to new challenges in agriculture. Conventional approaches often rely on hands-on monitoring and intuition, leading to suboptimal decision-making and lower throughput. However, existing Machine Learning (ML)-based systems for crop recommendation and disease detection face challenges such as limited scope due to the lack of real-time data integration, dependency on a limited dataset, and reduced accuracy under different environmental conditions. To address these challenges, the present study proposes an Artificial Intelligence (AI)-based smart farm system that integrates ML, Deep Learning (DL), and Internet of Things (IoT) technologies. Random Forest (RF) and Support Vector Machine (SVM) are widely employed ML algorithms for crop recommendation based on environmental parameters such as soil nutrients, temperature, humidity, and rainfall. Image-based plant disease detection is conducted through a Convolutional Neural Network (CNN)-based model, allowing for early diagnosis by analyzing the leaves' image. The experimental results reveal that the proposed system yields high performance. In addition, the crop recommendation accuracy ranges between 98% and 99%, with RF performing best on average, while the proposed CNN-based model achieved a disease detection accuracy ranging from 95% to 97%. This integration of real-time data and intelligent decision support drives farm efficiency, cuts pesticide overuse, and maximizes crop yield.","气候变异性、土壤退化、水资源短缺和植物病害共同给农业带来了新的挑战。传统方法往往依赖人工监测和直觉，导致决策欠佳和产量降低。然而，现有的基于机器学习（Machine Learning, ML）的作物推荐和病害检测系统面临诸多挑战，如缺乏实时数据集成导致适用范围有限、依赖有限数据集，以及在不同环境条件下准确率下降。为应对这些挑战，本研究提出了一种基于人工智能（Artificial Intelligence, AI）的智慧农场系统，该系统集成了机器学习、深度学习（Deep Learning, DL）和物联网（Internet of Things, IoT）技术。随机森林（Random Forest, RF）和支持向量机（Support Vector Machine, SVM）是基于土壤养分、温度、湿度和降雨量等环境参数进行作物推荐的广泛使用的机器学习算法。基于图像的植物病害检测通过基于卷积神经网络（Convolutional Neural Network, CNN）的模型进行，通过分析叶片图像实现早期诊断。实验结果表明，所提出的系统具有高性能。此外，作物推荐准确率在98%至99%之间，其中随机森林平均表现最佳，而所提出的基于卷积神经网络的模型实现了95%至97%的病害检测准确率。这种实时数据与智能决策支持的集成提升了农场效率，减少了农药过度使用，并最大化了作物产量。","Engineering Technology & Applied Science Research","2026-10-02T00:00:00Z",72,{"impact":105,"substance":44,"depth":353,"authority":244,"freshness":17,"relevant":20,"comment":354},17,"该研究将机器学习、深度学习与物联网整合用于作物推荐和病害检测，方法有创新且准确率数据具体，对智慧农业领域有参考价值，但属学术论文，产业影响有限。",[356],{"name":349,"url":346},[26,27,358,28,359],"农业物联网","病害识别",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[362],{"code":363,"slug":364,"name":365,"path":366,"parent_code":35},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":38,"locked":14,"version":39,"method":40,"evidence":368},{"candidates":369,"agricultural_evidence":45},[370,376,383,390,394,400],{"code":31,"score":176,"supported":45,"title":371,"abstract":372,"tags":375},[],[373,49,50,374,51,47],"深度学习","deep learning",[27,359],{"code":363,"score":13,"supported":45,"title":377,"abstract":378,"tags":382},[],[379,380,381],"物联网","internet of things","IoT",[358],{"code":384,"score":55,"supported":45,"title":385,"abstract":386,"tags":389},"F4",[],[387,388],"农药","pesticide",[],{"code":54,"score":55,"supported":45,"title":391,"abstract":392,"tags":393},[],[58,59],[],{"code":395,"score":69,"supported":45,"title":396,"abstract":397,"tags":399},"H3",[],[398],"水资源",[],{"code":62,"score":20,"supported":14,"title":401,"abstract":402,"tags":403},[],[],[26],[405,406],"AI 作物推荐 病害检测","物联网 智慧农场 系统","AI作物推荐病害检测-4165","10.48084\u002Fetasr.20031",{"doi":408,"openalex_id":410,"authors":411,"venue":349,"cited_by_count":84,"oa_url":346,"card":429,"direction":91,"ingested_from":92},"W7219079341",[412,414,416,418,420,422,425,427],{"name":413,"orcid":9},"S. J. Yashvanth",{"name":415,"orcid":9},"Prasanna Kumar M J",{"name":417,"orcid":9},"S. Preethi",{"name":419,"orcid":9},"B. V. Shruti",{"name":421,"orcid":9},"C. Lohith",{"name":423,"orcid":424},"Veena V. Pattankar","https:\u002F\u002Forcid.org\u002F0000-0002-4630-3969",{"name":426,"orcid":9},"P. Raghu",{"name":428,"orcid":9},"Rajesh Kumar S",{"tldr":430,"method":431,"finding":432,"direction":91,"opportunity":433},"提出融合机器学习、深度学习和物联网的智能农场系统，实现作物推荐与植物病害检测。","随机森林、支持向量机做作物推荐，CNN做叶片病害检测，结合IoT实时数据。","作物推荐准确率98%-99%，CNN病害检测准确率95%-97%，提升农场效率并减少农药滥用。","可探索多源实时数据融合与跨环境泛化能力，并开发轻量化边缘部署模型以适配田间场景。","2026-10-04T23:30:08.930596Z",{"id":436,"title":437,"url":438,"summary":439,"summary_zh":440,"content":9,"source_name":441,"source_url":438,"published_at":350,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":442,"score_detail":443,"sources":445,"tags":447,"primary_topic":449,"secondary_topics":450,"classification":451,"search_phrases":484,"slug":487,"view_count":488,"doi":489,"paper":490,"created_at":504},4105,"Artificial Intelligence in Agriculture: Enhancing Yield and Sustainable Farming Practices among Farmers in India","https:\u002F\u002Fdoi.org\u002F10.31235\u002Fosf.io\u002Fg7ypt_v1","Artificial intelligence (AI) is increasingly being applied across sectors, including agriculture. In India, agriculture remains central to employment, rural livelihoods, and national food security. Rising population pressure, climate change, declining land availability, pest and disease risks, labour shortages, and resource constraints have intensified the need for more productive and sustainable farming systems. This paper uses secondary data to examine how AI can enhance agricultural yield and support sustainable farming practices among farmers in India. It reviews applications such as precision agriculture, drones, crop disease detection, digital advisory services, market information systems, and resource optimisation tools. The paper also analyses the major barriers to AI adoption, including weak rural connectivity, high initial investment, limited digital literacy, and the need for localised solutions. The study concludes that AI has strong potential to improve productivity and sustainability in Indian agriculture, provided that adoption is supported through public investment, affordable access models, local-language advisory systems, farmer training, and public- private partnerships.","人工智能（AI）正日益应用于包括农业在内的各个领域。在印度，农业仍然是就业、农村生计和国家粮食安全的核心。人口压力不断上升、气候变化、可用土地减少、病虫害风险、劳动力短缺以及资源约束，加剧了对更高生产力和更可持续农业体系的需求。本文利用二手数据，考察人工智能如何提升印度农民的农业产量并支持可持续耕作方式。文章综述了精准农业、无人机、作物病害检测、数字咨询服务、市场信息系统和资源优化工具等应用。本文还分析了人工智能应用的主要障碍，包括农村连通性薄弱、初始投资高、数字素养有限以及对本地化解决方案的需求。研究认为，人工智能在提升印度农业生产力和可持续性方面具有巨大潜力，前提是通过公共投资、可负担的获取模式、本地语言咨询系统、农民培训以及公私合作来支持其应用。","OpenAlex",69,{"impact":44,"substance":105,"depth":18,"authority":19,"freshness":106,"relevant":20,"comment":444},"基于二手数据综述印度AI农业应用与推广障碍，结论有参考价值但缺乏一手数据与实证方法创新。",[446],{"name":441,"url":438},[25,26,27,251,448],"印度农业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":452},{"candidates":453,"agricultural_evidence":45},[454,458,462,469,475,479],{"code":31,"score":19,"supported":45,"title":455,"abstract":456,"tags":457},[51],[51],[27],{"code":62,"score":106,"supported":45,"title":459,"abstract":460,"tags":461},[],[251,252],[25,26,251],{"code":463,"score":55,"supported":45,"title":464,"abstract":465,"tags":468},"A8",[],[466,467],"粮食安全","food security",[],{"code":194,"score":55,"supported":45,"title":470,"abstract":471,"tags":474},[],[472,473],"气候变化","climate change",[],{"code":187,"score":69,"supported":45,"title":476,"abstract":477,"tags":478},[],[190],[],{"code":480,"score":20,"supported":14,"title":481,"abstract":482,"tags":483},"J3",[],[],[448],[485,486],"印度 农业人工智能 可持续农业","印度 精准农业 无人机","印度农业人工智能可持续农业-4105",4,"10.31235\u002Fosf.io\u002Fg7ypt_v1",{"doi":489,"openalex_id":491,"authors":492,"venue":9,"cited_by_count":84,"oa_url":438,"card":498,"direction":503,"ingested_from":92},"W7215740376",[493,496],{"name":494,"orcid":495},"Seema Wankhade","https:\u002F\u002Forcid.org\u002F0009-0005-6663-3729",{"name":497,"orcid":9},"Subhash Patil",{"tldr":499,"method":500,"finding":501,"direction":91,"opportunity":502},"基于二手数据综述AI在印度农业中提升产量与可持续性的应用及障碍。","使用二手数据，综述精准农业、无人机、病害检测等AI应用。","AI能提升印度农业生产力与可持续性，但受网络、成本、数字素养等制约。","可研究小农户低成本AI采纳路径、本地语言咨询系统及公私合作模式的效果评估。","数字乡村与农业信息化","2026-10-03T23:30:42.370830Z"]