[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3157":3,"related-3157":63},{"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":28,"search_phrases":34,"slug":37,"view_count":38,"doi":39,"paper":40,"created_at":62},3157,"Influence of Sound Frequencies on Plant Growth and Physiological Development","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22873563","Plant growth and physiological development are influenced by a wide range of environmental stimuli, and recent research has increasingly focused on the role of acoustic energy as a non-invasive growth-modulating factor. Conventional agricultural practices rely primarily on light, water, nutrients, and temperature control, while the potential of sound frequency exposure remains largely underexplored in mainstream cultivation systems. This paper presents a systematic experimental framework for studying the Influence of Sound Frequencies on Plant Growth and Physiological Development, which examines how controlled acoustic stimuli across different frequency ranges affect germination rate, stem elongation, leaf area, chlorophyll content, and overall biomass accumulation. The proposed framework integrates a calibrated sound frequency generator, a controlled plant exposure chamber, and a network of growth-parameter sensors to capture physiological responses under repeatable experimental conditions. Statistical and machine-learning-based correlation analysis is applied to the collected data to identify frequency ranges that produce measurable and consistent effects on plant development. In addition, an intelligent recommendation module suggests optimal frequency and exposure duration for specific plant species based on observed growth trends. The framework is designed to minimize experimental variability, ensure reproducibility across trials, and support data-driven insights for sustainable and technology-assisted agriculture. Experimental evaluation demonstrates measurable variation in physiological parameters across frequency treatments, improved understanding of acoustic-plant interaction, and a practical pathway toward sound-assisted cultivation techniques, making the proposed framework a valuable contribution to smart and precision agriculture research.","植物生长和生理发育受到多种环境刺激的影响，近年来的研究日益关注声能作为一种非侵入性生长调控因子的作用。传统农业实践主要依赖光照、水分、养分和温度控制，而声频暴露的潜力在主流栽培系统中仍未得到充分探索。本文提出了一个系统性的实验框架，用于研究声频对植物生长和生理发育的影响，该框架考察不同频率范围内的受控声刺激如何影响发芽率、茎伸长、叶面积、叶绿素含量和整体生物量积累。所提出的框架集成了经过校准的声频发生器、受控植物暴露舱以及生长参数传感器网络，以在可重复的实验条件下捕获生理响应。研究对采集的数据应用统计和基于机器学习的相关性分析，以识别对植物发育产生可测量且一致影响的频率范围。此外，智能推荐模块根据观察到的生长趋势，为特定植物物种建议最佳频率和暴露时长。该框架旨在最大限度地减少实验变异性，确保跨试验的可重复性，并支持面向可持续和技术辅助农业的数据驱动洞察。实验评估表明，不同频率处理下生理参数存在可测量的差异，增进了对声-植物相互作用的理解，并提供了通向声辅助栽培技术的实践路径，使所提出的框架成为智能和精准农业研究的有价值贡献。",null,"International Journal of Science Engineering and Technology","2026-09-21T00:00:00Z","论文",25,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,8,1,"该论文提出声频刺激植物生长的系统实验框架并引入智能推荐模块，方法新颖、结论可靠，对智慧农业与精准农业研究有参考价值，但属细分领域基础研究，产业影响有限。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22873562",[29,30,31,32,33],"智慧农业","机器学习","精准农业","声波助长","植物生理",[35,36],"声频 植物生长 生理发育","声波助长 智慧农业","声频植物生长生理发育-3157",0,"10.5281\u002Fzenodo.22873563",{"doi":39,"openalex_id":41,"authors":42,"venue":10,"cited_by_count":38,"oa_url":6,"card":55,"direction":59,"ingested_from":61},"W7213863269",[43,45,47,49,51,53],{"name":44,"orcid":9},"R. Baby",{"name":46,"orcid":9},"J. Jerlin",{"name":48,"orcid":9},"M. Veni",{"name":50,"orcid":9},"S. Divya",{"name":52,"orcid":9},"M. Arunatharan",{"name":54,"orcid":9},"A. Mohamed Esmail",{"tldr":56,"method":57,"finding":58,"direction":59,"opportunity":60},"构建声频刺激植物生长实验框架，分析不同频率对生理指标的影响并推荐最优频率。","校准声频发生器、受控暴露舱与生长传感器网络，结合统计与机器学习分析。","不同频率处理下植物生理参数存在可测量差异，声频可辅助栽培。","智慧农业 \u002F 农业物联网","可探索特定作物声频响应机制，并将声频调控集成到物联网精准农业系统中。","openalex","2026-09-22T23:30:10.713102Z",{"total":64,"page":22,"page_size":64,"items":65},6,[66,115,156,189,221,255],{"id":67,"title":68,"url":69,"summary":70,"summary_zh":71,"content":9,"source_name":72,"source_url":69,"published_at":73,"category":12,"cover_url":9,"hotness":74,"is_selected":14,"score":75,"score_detail":76,"sources":81,"tags":83,"search_phrases":86,"slug":89,"view_count":38,"doi":90,"paper":91,"created_at":114},2803,"Volatile Fingerprinting Empowers Salinity Monitoring in Peppermint Using MOS Sensors and Feature-Optimized Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15184233","Early detection of salinity stress is essential for precision agriculture, particularly in scalable, resource-constrained monitoring systems. This study presents a portable sensing module integrating a low-cost metal oxide semiconductor (MOS) sensor array with potential application for edge deployment to detect salinity stress in peppermint. Salinity significantly reduced plant biomass, confirming physiological stress induction. Volatile organic compound (VOC) fingerprints were collected over eleven consecutive days in a controlled enclosure. Sensor signals underwent outlier filtering, normalization, and smoothing, while treatment discrimination was verified using the Kruskal–Wallis test. Thirty-three machine learning models were evaluated using a 75:25 train–test split with five-fold cross-validation. Wide neural network models achieved the highest predictive performance, exceeding 98% test accuracy and a 97% macro F1 score. Feature adequacy analysis showed that six sensors captured the dominant variance required for reliable classification. Considering computational constraints, a bilayered neural network using only six features maintained over 97% accuracy with a memory footprint of 0.008 MB while remaining Pareto optimal. These findings support the feasibility of a compact, computationally efficient, and edge-compatible VOC sensing framework for salinity stress detection in precision agriculture and intelligent crop monitoring.","盐胁迫的早期检测对精准农业至关重要，尤其是在可扩展、资源受限的监测系统中。本研究提出了一种便携式传感模块，将低成本金属氧化物半导体（MOS）传感器阵列集成其中，具备边缘部署的应用潜力，用于检测薄荷中的盐胁迫。盐胁迫显著降低了植物生物量，证实了生理胁迫的诱导作用。在受控密闭环境中连续十一天采集了挥发性有机化合物（VOC）指纹图谱。对传感器信号进行了异常值过滤、归一化和平滑处理，并使用Kruskal–Wallis检验验证了处理组间的区分度。采用75:25的训练-测试划分和五折交叉验证评估了三十三种机器学习模型。宽神经网络模型取得了最高的预测性能，测试准确率超过98%，宏F1分数达到97%。特征充分性分析表明，六个传感器即可捕获可靠分类所需的主要方差。考虑到计算约束，仅使用六个特征的双层神经网络在保持超过97%准确率的同时，内存占用仅为0.008 MB，且仍处于帕累托最优。这些发现支持了一种紧凑、计算高效且兼容边缘计算的VOC传感框架用于精准农业和智能作物监测中盐胁迫检测的可行性。","Electronics","2026-09-17T00:00:00Z",10,78,{"impact":77,"substance":78,"depth":79,"authority":20,"freshness":74,"relevant":22,"comment":80},15,22,18,"低成本MOS传感器阵列结合特征优化机器学习实现薄荷盐胁迫早期无损检测，方法新颖、数据扎实，对边缘部署式作物监测有参考价值。",[82],{"name":72,"url":69},[29,30,31,84,85],"农业传感器","盐胁迫监测",[87,88],"农业传感器 盐胁迫监测 智慧农业 机器学习","农业传感器 盐胁迫监测","农业传感器盐胁迫监测智慧农业机器学习-2803","10.3390\u002Felectronics15184233",{"doi":90,"openalex_id":92,"authors":93,"venue":72,"cited_by_count":38,"oa_url":69,"card":108,"direction":113,"ingested_from":61},"W7213452763",[94,97,100,102,105],{"name":95,"orcid":96},"Ahmad Ali","https:\u002F\u002Forcid.org\u002F0000-0001-5530-7374",{"name":98,"orcid":99},"Vinie Lee Silva Alvarado","https:\u002F\u002Forcid.org\u002F0009-0000-5857-3248",{"name":101,"orcid":9},"Arman Heydari",{"name":103,"orcid":104},"Sandra Sendra","https:\u002F\u002Forcid.org\u002F0000-0001-9556-9088",{"name":106,"orcid":107},"Jaime Lloret","https:\u002F\u002Forcid.org\u002F0000-0002-0862-0533",{"tldr":109,"method":110,"finding":111,"direction":59,"opportunity":112},"用低成本MOS传感器阵列采集薄荷VOC指纹，结合特征优化机器学习实现盐胁迫检测。","11天VOC指纹采集，33种机器学习模型，五折交叉验证，特征充分性分析。","宽神经网络准确率超98%，仅用6个特征的双层网络保持97%以上且内存仅0.008MB。","可探索多作物VOC指纹迁移学习与田间边缘设备长期稳定性验证。","农业人工智能与决策模型","2026-09-17T23:30:59.249847Z",{"id":116,"title":117,"url":118,"summary":119,"summary_zh":120,"content":9,"source_name":121,"source_url":118,"published_at":73,"category":12,"cover_url":9,"hotness":74,"is_selected":14,"score":122,"score_detail":123,"sources":126,"tags":128,"search_phrases":131,"slug":134,"view_count":38,"doi":135,"paper":136,"created_at":155},2802,"Advanced olive leaf area prediction using machine learning methods","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71390-9","Abstract Accurate leaf area estimation is essential for understanding olive tree physiology, productivity, and stress adaptationThis study developed and evaluated machine learning models for non-destructive olive leaf area prediction using linear measurements (length and width) from 30 diverse cultivars at the Tarom Olive Research Station, Iran. Six machine learning algorithms, Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest, Decision Tree, AdaBoost, and XGBoost were optimized using Bayesian optimization, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Results demonstrated that PSO consistently outperformed other optimization methods across most models. The ANN model optimized with PSO achieved the highest prediction accuracy (R 2 = 0.9828, RMSE = 0.3009 cm 2 ). External validation using eight additional cultivars confirmed model generalizability, with the universal ANN model maintaining R 2 > 0.98. This study provides a robust, non-destructive methodology for olive leaf area estimation applicable across diverse cultivars, offering practical implications for precision agriculture, phenotyping, and orchard management under changing climatic conditions.","摘要 准确的叶面积估算对于理解油橄榄树的生理特性、生产力及逆境适应性至关重要。本研究在伊朗塔罗姆油橄榄研究站，利用来自30个不同品种的线性测量数据（长度和宽度），开发并评估了用于无损油橄榄叶面积预测的机器学习模型。采用贝叶斯优化、遗传算法（GA）和粒子群优化（PSO）对六种机器学习算法——人工神经网络（ANN）、支持向量回归（SVR）、随机森林、决策树、AdaBoost和XGBoost——进行了优化。结果表明，在大多数模型中，PSO始终优于其他优化方法。经PSO优化后的ANN模型取得了最高的预测精度（R² = 0.9828，RMSE = 0.3009 cm²）。利用另外八个品种进行的外部验证证实了模型的泛化能力，通用ANN模型保持R² > 0.98。本研究为适用于不同品种的油橄榄叶面积估算提供了一种稳健的无损方法，为气候变化条件下的精准农业、表型分析和果园管理提供了实际应用价值。","Scientific Reports",73,{"impact":17,"substance":18,"depth":19,"authority":124,"freshness":74,"relevant":22,"comment":125},14,"基于30个品种的机器学习叶片面积无损预测研究，方法新颖、验证充分，对精准农业与表型分析有实用价值，但属细分领域技术进展，影响范围有限。",[127],{"name":121,"url":118},[29,129,30,31,130],"农业人工智能","表型分析",[132,133],"农业人工智能 智慧农业 机器学习 精准农业","农业人工智能 智慧农业","农业人工智能智慧农业机器学习精准农业-2802","10.1038\u002Fs41598-026-71390-9",{"doi":135,"openalex_id":137,"authors":138,"venue":121,"cited_by_count":38,"oa_url":118,"card":149,"direction":113,"ingested_from":61},"W7213449017",[139,142,144,146],{"name":140,"orcid":141},"Ahmad Reza Dadras","https:\u002F\u002Forcid.org\u002F0000-0001-8591-5813",{"name":143,"orcid":9},"Hossein Sabouri",{"name":145,"orcid":9},"Ali Tanhaei",{"name":147,"orcid":148},"Sayed Javad Sajadi","https:\u002F\u002Forcid.org\u002F0000-0002-6555-080X",{"tldr":150,"method":151,"finding":152,"direction":153,"opportunity":154},"用机器学习基于叶长宽非破坏性预测30个橄榄品种叶面积，PSO优化ANN精度最高。","30个品种叶长宽数据，六种ML算法结合贝叶斯、GA、PSO优化。","PSO优化ANN最优（R²=0.9828），外部8品种验证R²>0.98，通用性好。","农业遥感与作物表型","可拓展至多物种、多环境及无人机\u002F手机图像自动测量，构建通用叶面积表型平台。","2026-09-17T23:30:59.169744Z",{"id":157,"title":158,"url":159,"summary":160,"summary_zh":161,"content":9,"source_name":162,"source_url":159,"published_at":163,"category":12,"cover_url":9,"hotness":74,"is_selected":14,"score":164,"score_detail":165,"sources":168,"tags":170,"search_phrases":173,"slug":176,"view_count":38,"doi":177,"paper":178,"created_at":188},2652,"ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: TRANSFORMING TEACHING, LEARNING, AND STUDENT ENGAGEMENT","https:\u002F\u002Fdoi.org\u002F10.65725\u002Fijhlt\u002F1\u002F2\u002F001","Efficient irrigation management is essential for sustainable agriculture, particularly in the context of increasing freshwater scarcity and the growing imperative to optimize crop productivity. Conventional irrigation practices rely predominantly on fixed time schedules or manual field assessments, which frequently induce over-irrigation, root-zone nutrient leaching, under-irrigation water stress, and substantial resource inefficiency. This paper proposes an Intelligent Irrigation Management System that integrates Internet of Things (IoT) sensing architectures, multi-parameter environmental telemetry, and supervised machine learning (ML) algorithms to facilitate dynamic, data-driven, and automated irrigation control. The proposed system continuously acquires real-time field data—including soil moisture, ambient temperature, relative humidity, soil temperature, and rainfall—via deployed sensor nodes managed by an ESP32 microcontroller pipeline. The telemetry stream is transmitted through low-power communication channels to a centralized processing engine, where a Random Forest classification model evaluates multidimensional soil-environmental interactions to predict immediate irrigation requirements. The predicted states feed into an automated actuation layer that directly modulates a solenoid-valve and water-pump relay, forming a closed-loop feedback pipeline. Evaluated against traditional threshold-based and schedule-driven approaches, the proposed IoT-ML framework demonstrates superior operational responsiveness, minimizes unnecessary water application, and offers a robust, scalable architectural template for modern precision agriculture.","高效灌溉管理对可持续农业至关重要，尤其是在淡水日益稀缺、优化作物生产力需求不断增长的背景下。传统灌溉实践主要依赖固定时间表或人工田间评估，这常常导致过度灌溉、根区养分淋失、灌溉不足引起的水分胁迫以及严重的资源低效。本文提出了一种智能灌溉管理系统，该系统集成了物联网（IoT）感知架构、多参数环境遥测以及监督式机器学习（ML）算法，以实现动态、数据驱动和自动化的灌溉控制。所提出的系统通过由ESP32微控制器管道管理的部署传感器节点，持续采集实时田间数据——包括土壤湿度、环境温度、相对湿度、土壤温度和降雨量。遥测数据流通过低功耗通信信道传输至集中处理引擎，其中随机森林分类模型评估多维土壤-环境相互作用，以预测即时灌溉需求。预测状态输入自动执行层，直接调节电磁阀和水泵继电器，形成闭环反馈管道。与传统基于阈值和时间表驱动的方法相比，所提出的IoT-ML框架展现出更优的运行响应能力，最大限度地减少了不必要的灌溉用水，并为现代精准农业提供了一种稳健、可扩展的架构模板。","INTERNATIONAL JOURNAL OF HUMANITIES AND LEARNING TECHNOLOGY INNOVATION (IJHLT)","2026-09-15T00:00:00Z",72,{"impact":166,"substance":79,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":167},16,"论文提出IoT与随机森林融合的闭环智能灌溉系统，方法完整、数据驱动，对节水农业有参考价值，但标题与摘要主题不符需核实。",[169],{"name":162,"url":159},[29,171,30,172,31],"农业物联网","智能灌溉",[174,175],"农业物联网 智慧农业 智能灌溉 机器学习","农业物联网 智慧农业","农业物联网智慧农业智能灌溉机器学习-2652","10.65725\u002Fijhlt\u002F1\u002F2\u002F001",{"doi":177,"openalex_id":179,"authors":180,"venue":162,"cited_by_count":38,"oa_url":9,"card":183,"direction":59,"ingested_from":61},"W7213277724",[181],{"name":182,"orcid":9},"M. Rathamani",{"tldr":184,"method":185,"finding":186,"direction":59,"opportunity":187},"提出融合物联网传感与随机森林的智能灌溉系统，实现数据驱动的自动灌溉控制。","ESP32传感器节点采集土壤温湿度等数据，随机森林分类预测灌溉需求。","相比传统定时或阈值方法，该系统响应更优、减少不必要灌溉，可扩展性强。","可探索多模态数据融合与边缘智能，提升灌溉决策的实时性与泛化能力。","2026-09-16T23:30:16.194086Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":194,"content":9,"source_name":121,"source_url":192,"published_at":195,"category":12,"cover_url":9,"hotness":74,"is_selected":14,"score":196,"score_detail":197,"sources":199,"tags":201,"search_phrases":203,"slug":206,"view_count":38,"doi":207,"paper":208,"created_at":220},2338,"An optimized machine learning approach for reliable agronomic parameter prediction in precision farming systems","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71592-1","Optimized machine learning models are very important for improving predictive performance in precision agriculture because they let us analyze soil and environmental data in a data-driven way. But conventional predictive methods often can’t be used to make generalizations because agronomic data is often very variable, has nonlinear interactions, and is very different from one another. The study presents an enhanced machine learning-based predictive framework for estimating agricultural parameters utilizing structured numerical soil and environmental datasets. The framework combines systematic data preprocessing, feature selection, and hyperparameter optimization to make models more stable and reliable. The model performance was evaluated using five-fold cross-validation and standard regression metrics, including the Coefficient of Determination (R 2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The average R 2 is 0.696 ± 0.148, the RMSE is 5.160 ± 1.571, and the MAE is 4.636 ± 1.545, which shows that the model can make accurate predictions across all validation folds. Further the classification of the growth stages of spinach is done with an accuracy of 84.63% and with the precision of 85% using the proposed Hybrid ensemble model. The results show that the proposed framework works well with nonlinear agricultural data and could be used for data-driven decisions in precision farming systems.","优化的机器学习模型对于提升精准农业中的预测性能至关重要，因为它使我们能够以数据驱动的方式分析土壤和环境数据。然而，传统的预测方法往往无法用于泛化，因为农艺数据通常变异性很大、具有非线性交互作用，且彼此之间差异显著。本研究提出了一种基于增强机器学习的预测框架，利用结构化数值土壤和环境数据集来估算农业参数。该框架结合了系统化的数据预处理、特征选择和超参数优化，使模型更加稳定可靠。采用五折交叉验证和标准回归指标评估模型性能，包括决定系数（R²）、均方根误差（RMSE）和平均绝对误差（MAE）。平均R²为0.696 ± 0.148，RMSE为5.160 ± 1.571，MAE为4.636 ± 1.545，表明该模型在所有验证折中均能做出准确预测。此外，利用所提出的混合集成模型对菠菜生长阶段进行分类，准确率达到84.63%，精确率达到85%。结果表明，所提出的框架能够很好地处理非线性农业数据，可用于精准农业系统中的数据驱动决策。","2026-09-12T00:00:00Z",67,{"impact":17,"substance":79,"depth":77,"authority":124,"freshness":21,"relevant":22,"comment":198},"该论文提出融合预处理、特征选择与超参数优化的机器学习框架，在土壤环境数据上取得R²约0.70的预测表现并以84.63%准确率识别菠菜生长期，方法扎实但属常规模型优化，产业影响有限，可作为智慧农业技术参考。",[200],{"name":121,"url":192},[29,129,30,31,202],"土壤数据",[204,205],"农业人工智能 土壤数据 智慧农业 机器学习","农业人工智能 土壤数据","农业人工智能土壤数据智慧农业机器学习-2338","10.1038\u002Fs41598-026-71592-1",{"doi":207,"openalex_id":209,"authors":210,"venue":121,"cited_by_count":38,"oa_url":192,"card":215,"direction":113,"ingested_from":61},"W7212396250",[211,213],{"name":212,"orcid":9},"T. Suba",{"name":214,"orcid":9},"K. Lakshmi Joshitha",{"tldr":216,"method":217,"finding":218,"direction":113,"opportunity":219},"提出优化机器学习框架，用土壤环境数据预测农艺参数并分类菠菜生长阶段。","数据预处理、特征选择、超参数优化，五折交叉验证与混合集成模型。","回归平均R²为0.696，菠菜生长阶段分类准确率达84.63%。","可探索跨作物、跨区域迁移学习与可解释性，提升非线性农艺数据泛化能力。","2026-09-13T23:30:43.564799Z",{"id":222,"title":223,"url":224,"summary":225,"summary_zh":226,"content":9,"source_name":227,"source_url":224,"published_at":228,"category":12,"cover_url":9,"hotness":74,"is_selected":14,"score":164,"score_detail":229,"sources":231,"tags":233,"search_phrases":235,"slug":237,"view_count":38,"doi":238,"paper":239,"created_at":254},2179,"Comparative evaluation of classical machine learning and deep learning models for early weed detection in precision agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02195-y","Unless treated and controlled early, weeds considerably impede the growth of crops as they compete over vital factors (resources) like nutrients, water, and light. To overcome this problem, an automated weed species early detection and classification system using machine learning-based image analysis is suggested. The framework integrates classical machine learning algorithms Support Vector Machines (SVM), Random Forests and k-Nearest Neighbors (k-NN), using handcrafted features like texture, shape, and color with deep learning models, Convolutional Neural Networks (CNNs) which automatically learn discriminative features in the data. The study comparatively evaluates classical ML and deep learning models. In order to test the experiments, a publicly available Sugar Beet dataset was used, as well as a custom maize seedling dataset. To enhance generalization and robustness, some preprocessing steps were performed, including normalization, background removal, and augmentation. Common metrics used to assess the performance of the models are F1-score and Intersection over Union (IoU). The experimental results show deep learning models outperforming the traditional machine learning methods, especially CNNs The CNN model also exhibited a classification accuracy of 98.7%. These results demonstrate the promise of deep learning to quickly, reliably, and in large scale detect weeds in the early stages of precision agriculture. This study highlights the feasibility of machine learning systems in proactive management of weed as well as in supporting healthy crop growth at the early stages of development.","除非在早期进行处理和控制，否则杂草会争夺养分、水分和光照等关键因素（资源），从而严重阻碍作物生长。为解决这一问题，提出了一种基于机器学习的图像分析自动化杂草物种早期检测与分类系统。该框架整合了经典机器学习算法——支持向量机（SVM）、随机森林和k近邻（k-NN），利用纹理、形状和颜色等手工特征，并结合深度学习模型——卷积神经网络（CNN），后者可自动学习数据中的判别性特征。本研究对经典机器学习和深度学习模型进行了对比评估。为验证实验，使用了公开的甜菜数据集以及自建的玉米幼苗数据集。为增强泛化性和鲁棒性，执行了一些预处理步骤，包括归一化、背景去除和数据增强。用于评估模型性能的常用指标为F1分数和交并比（IoU）。实验结果表明，深度学习模型优于传统机器学习方法，尤其是CNN。CNN模型的分类准确率达到了98.7%。这些结果证明了深度学习在精准农业中快速、可靠且大规模地早期检测杂草的潜力。本研究凸显了机器学习系统在杂草主动管理以及支持作物早期健康生长方面的可行性。","Discover Artificial Intelligence","2026-09-10T00:00:00Z",{"impact":166,"substance":18,"depth":166,"authority":17,"freshness":21,"relevant":22,"comment":230},"对比经典机器学习与深度学习在作物早期杂草检测中的表现，CNN 分类准确率达 98.7%，方法扎实、结论明确，对精准农业智能除草有参考价值。",[232],{"name":227,"url":224},[29,129,30,234,31],"杂草识别",[236,133],"农业人工智能 智慧农业 机器学习 杂草识别","农业人工智能智慧农业机器学习杂草识别-2179","10.1007\u002Fs44163-026-02195-y",{"doi":238,"openalex_id":240,"authors":241,"venue":227,"cited_by_count":38,"oa_url":224,"card":249,"direction":113,"ingested_from":61},"W7212111551",[242,245,247],{"name":243,"orcid":244},"Rajeev Kumar","https:\u002F\u002Forcid.org\u002F0000-0001-8414-3778",{"name":246,"orcid":9},"P. K. Singh",{"name":248,"orcid":9},"Rohit Kumar Tiwari",{"tldr":250,"method":251,"finding":252,"direction":113,"opportunity":253},"对比经典机器学习与深度学习模型在精准农业早期杂草检测中的性能。","用SVM、随机森林、k-NN及CNN，基于甜菜和玉米幼苗图像数据集。","CNN分类准确率达98.7%，显著优于传统机器学习方法。","可探索轻量化模型在田间边缘设备实时检测杂草的部署与跨物种泛化能力。","2026-09-11T23:30:52.642134Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":260,"content":9,"source_name":261,"source_url":258,"published_at":262,"category":12,"cover_url":9,"hotness":74,"is_selected":14,"score":263,"score_detail":264,"sources":267,"tags":269,"search_phrases":271,"slug":274,"view_count":38,"doi":275,"paper":276,"created_at":298},1911,"Intelligent Prediction of Spray Distribution and Hydraulic Behavior in an Automatic Spraying Machine for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.1002\u002Frob.70346","ABSTRACT Existing machine‐learning applications in agricultural spraying have mainly focused on droplet‐size descriptors, canopy coverage, deposition, or image‐based spray detection, while the simultaneous prediction of hydraulic and geometric spray responses from controllable operating variables remains limited. This study developed an experimental–computational framework for predicting mean nozzle discharge, effective spray width, derived spray angle, and mean inter‐nozzle overlap from nozzle model, operating pressure, and spray height. Four hollow‐cone ceramic nozzle models were evaluated at five pressures (6.0–9.5 bar) and three heights (30–50 cm), producing 60 operating conditions tested in triplicate. The resulting 180 experimental runs were averaged into 60 condition‐level records for machine‐learning analysis. Mean nozzle discharge ranged from 0.98 to 2.03 L , effective spray width ranged from 34.75 to 71.50 cm, the maximum derived spray angle reached 96.77°, and mean inter‐nozzle overlap ranged from 34.00 to 71.67 cm across the investigated operating conditions. Extra Trees Regressor provided the best predictions for mean nozzle discharge (, RMSE = 49.25 mL , MAE = 37.99 mL ), effective spray width (, RMSE = 2.37 cm), and mean overlap (, RMSE = 3.13 cm). Support vector regression with a radial basis function kernel performed best for derived spray angle (, RMSE = 3.53°). Pressure increased discharge and lateral footprint, whereas increasing height widened the footprint but generally reduced the geometrically derived angle. The models provide rapid condition‐level estimates within the investigated experimental domain and may complement physical calibration. Their transfer to other nozzle families, moving platforms, field environments, or real‐time control requires additional validation.","摘要 现有机器学习在农业喷施中的应用主要集中在雾滴尺寸描述符、冠层覆盖率、沉积量或基于图像的喷雾检测方面，而同时从可控操作变量预测水力与几何喷雾响应的研究仍较为有限。本研究开发了一个实验-计算框架，用于根据喷嘴型号、操作压力和喷雾高度预测平均喷嘴流量、有效喷雾宽度、推导喷雾角度及平均喷嘴间重叠量。研究评估了四种空心锥陶瓷喷嘴型号，在五个压力水平（6.0–9.5 bar）和三个高度（30–50 cm）下进行测试，共产生60种操作条件，每种条件重复三次。由此得到的180次实验运行被平均为60条条件级记录，用于机器学习分析。在所研究的操作条件下，平均喷嘴流量范围为0.98至2.03 L，有效喷雾宽度范围为34.75至71.50 cm，最大推导喷雾角度达到96.77°，平均喷嘴间重叠量范围为34.00至71.67 cm。极端随机树回归器（Extra Trees Regressor）在预测平均喷嘴流量（，RMSE = 49.25 mL，MAE = 37.99 mL）、有效喷雾宽度（，RMSE = 2.37 cm）和平均重叠量（，RMSE = 3.13 cm）方面表现最佳。采用径向基函数核的支持向量回归（support vector regression with a radial basis function kernel）在推导喷雾角度预测中表现最优（，RMSE = 3.53°）。压力增加了流量和横向覆盖范围，而增加高度则拓宽了覆盖范围，但通常减小了几何推导角度。所建立的模型能够在所研究的实验域内提供快速的条件级估计，并可作为物理校准的补充手段。将其推广至其他喷嘴系列、移动平台、田间环境或实时控制仍需进一步验证。","Journal of Field Robotics","2026-09-07T00:00:00Z",71,{"impact":17,"substance":18,"depth":79,"authority":124,"freshness":265,"relevant":22,"comment":266},7,"论文提出预测喷雾分布与液压行为的机器学习框架，数据详实，对精准农业装备优化有参考价值。",[268],{"name":261,"url":258},[29,30,31,270],"植保机械",[272,273],"智慧农业 机器学习 植保机械 精准农业","智慧农业 机器学习","智慧农业机器学习植保机械精准农业-1911","10.1002\u002Frob.70346",{"doi":275,"openalex_id":277,"authors":278,"venue":261,"cited_by_count":38,"oa_url":9,"card":293,"direction":113,"ingested_from":61},"W7211920463",[279,282,284,286,288,290],{"name":280,"orcid":281},"Abdallah Elshawadfy Elwakeel","https:\u002F\u002Forcid.org\u002F0000-0002-9360-051X",{"name":283,"orcid":9},"Abdallah Zein Elden",{"name":285,"orcid":9},"Saad Ahmed",{"name":287,"orcid":9},"L Nasrat",{"name":289,"orcid":9},"Mohammad S. AL-Harbi",{"name":291,"orcid":292},"Atef Fathy Ahmed","https:\u002F\u002Forcid.org\u002F0000-0001-8723-9097",{"tldr":294,"method":295,"finding":296,"direction":113,"opportunity":297},"用机器学习预测自动喷雾机的流量、喷幅、角度和重叠，实现精准农业。","实验测试4种喷嘴、5压力、3高度共60工况，用Extra Trees和SVR建模","Extra Trees预测流量、喷幅和重叠最佳，SVR预测角度最佳；压力增加流量和喷幅，高度增加喷幅","模型仅限特定喷嘴和静态条件，可扩展至其他喷嘴、移动平台和实时控制，需更多验证。","2026-09-08T23:30:35.848835Z"]