Precision Agriculture最新文献_第3页

A practical model and online tool to predict cover crop nitrogen content and carbon-to-nitrogen ratio via remote sensing 遥感预测覆盖作物氮含量和碳氮比的实用模型和在线工具

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-09-25 DOI: 10.1007/s11119-026-10434-6

N. Kaur,C. White,A. Lefever,A. Thieme,J. Jennewein,WD. Hively,D. Carrijo

{"title":"A practical model and online tool to predict cover crop nitrogen content and carbon-to-nitrogen ratio via remote sensing","authors":"N. Kaur,C. White,A. Lefever,A. Thieme,J. Jennewein,WD. Hively,D. Carrijo","doi":"10.1007/s11119-026-10434-6","DOIUrl":"https://doi.org/10.1007/s11119-026-10434-6","url":null,"abstract":"Abstract Purpose Estimations of cover crop (CC) nitrogen (N) content and carbon-to-nitrogen (C:N) ratio are crucial for guiding N fertilizer recommendations for subsequent cash crops, yet prediction tools that can be easily adopted by practitioners are lacking. This study aims to (1) develop models to predict CC N content and C:N ratio using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) from Uncrewed Aerial Vehicles (UAV) and satellite imagery, combined with growing degree days (GDD, base 5 °C) and CC species; and (2) integrate the models developed into an online application. Methods Over 260 CC biomass samples were collected from 10 Pennsylvania counties in the Chesapeake Bay watershed representing diverse species (triticale, cereal rye, wheat, brassicas, legumes, and mixtures). Four models were evaluated: (1) UAV NDVI + GDD + species, (2) UAV NDRE + GDD + species, (3) satellite NDVI + GDD + species, and (4) satellite NDRE + GDD + species. Results Random forest models (R² > 0.74 for N content and R² > 0.86 for C: N ratio) generally explained more variability than generalized additive models (R² > 0.70 for N content and R² > 0.85 for C: N ratio) based on 10-fold cross validation. GDD and vegetation indices were the strongest predictors for N content, while GDD and species group influenced C: N ratio the most. In the independent site-years validation, UAV-NDRE explained the most variability in CC N content (R² = 0.82), and both UAV-based models (R² = 0.52) explained more variability than the satellite models for C: N ratio prediction. Conclusion Random forest models accurately predicted CC N content and C: N ratio and were integrated into an online application to support precision agriculture practices.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"52 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149049184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-destructive nitrogen estimation in pastures from UAV multispectral imagery using a heterogeneity-driven machine-learning framework 基于异构驱动的机器学习框架的无人机多光谱图像牧场非破坏性氮估计

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-09-22 DOI: 10.1007/s11119-026-10453-3

Antonio de Oliveira Costa Neto,Yiannis Ampatzidis,Andrea Lazzari,Jim Fletcher

引用次数: 0

Precision Diagnosis of Apple Leaf Diseases Across Infection Stages via Web-Based Analysis 基于网络分析的苹果叶片病跨感染阶段的精确诊断

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-09-18 DOI: 10.1007/s11119-026-10451-5

Kangrui Han,Hao Cai,Kari Peter,Long He

{"title":"Precision Diagnosis of Apple Leaf Diseases Across Infection Stages via Web-Based Analysis","authors":"Kangrui Han,Hao Cai,Kari Peter,Long He","doi":"10.1007/s11119-026-10451-5","DOIUrl":"https://doi.org/10.1007/s11119-026-10451-5","url":null,"abstract":"Abstract Purpose Throughout the growing season, leaf diseases pose a huge risk to apple fruit yield and quality. While early detection targets timely intervention, understanding disease progression across different stages is essential for effective treatment and resistance breeding. Methods This study proposes a deep learning-based detection system that identifies apple leaf diseases at various infection stages in real-world environments. Most existing methods have focused on disease detection in controlled conditions, while the developed system operates in complex field settings with variable lighting and background noise. Two YOLOv8-based variants integrated with a multi-head self-attention (MHSA) module were developed to improve detection of dense, tiny, and feature-similar lesions. In addition, a web-based interactive tool was built by combining the Segment Anything Model (SAM) with the detection model to support leaf isolation, disease localization, infection estimation, severity categorization, and automatic report generation. Results The training results on proposed PA-ALeaf dataset demonstrated that YOLOv8-MHSA_b_h varaint achieved the highest performance (Precision 60.5%, mAP50 56.3%, F1 confidence 56.0%), while maintaining real-time inference (12.38 ms per image), compared to state-of-the-art models. Conclusion By bridging practical disease monitoring and scientific research, our system offers a comprehensive, scalable solution for apple leaf disease detection in real-world orchards. This system could benefit both farmers by enabling multi-stage intervention and researchers by providing insights into disease progression.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"3 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149021265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UAV-based rice nitrogen status monitoring using machine learning and deep learning 基于无人机的水稻氮素状态监测，利用机器学习和深度学习

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-09-11 DOI: 10.1007/s11119-026-10440-8

Xi Tao,Jiayi Zhang,Zhaopeng Fu,Xinye Xu,Qiang Cao,Yongchao Tian,Yan Zhu,Weixing Cao,Xiaojun Liu

引用次数: 0

Heterogeneous behaviours towards precision agriculture adoption among Italian winegrowers: insights from latent class analysis 意大利葡萄种植者采用精准农业的异质行为：来自潜在阶层分析的见解

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-09-10 DOI: 10.1007/s11119-026-10448-0

Adriano Biondo,Antonino Galati,Francesco Caracciolo

{"title":"Heterogeneous behaviours towards precision agriculture adoption among Italian winegrowers: insights from latent class analysis","authors":"Adriano Biondo,Antonino Galati,Francesco Caracciolo","doi":"10.1007/s11119-026-10448-0","DOIUrl":"https://doi.org/10.1007/s11119-026-10448-0","url":null,"abstract":"Abstract Purpose Precision Agriculture technologies including satellite data, drones, field robots and automatic guidance, are increasingly promoted as tools to improve sustainability, competitiveness, and environmental efficiency in viticulture. However, the adoption of these practices among Italian winegrowers exhibits significant heterogeneity, influenced by a combination of structural and behavioural factors. This study aims to identify distinct groups of Italian winegrowers based on their intentions to adopt PATs, examining how these intentions relate to four behavioural constructs that capture cognitive, social, and risk-related influences. Method The present study integrates a profile-based perspective by estimating a latent-class generalised structural equation model on 272 Italian winegrowers. The model of class membership is predicated upon a multifaceted conceptualisation encompassing risk perception, risk tolerance, perceived ease of use, and subjective norms and the technology-specific five-year intentions are incorporated as class-specific probit intercepts. Results Three qualitatively distinct profiles emerge: risk-sensitive sceptics, selective pragmatists and usability-oriented high adopters with sharply different adoption patterns across technologies. Participation in rural development schemes is the strongest predictor of membership in the higher-propensity latent classes, namely the selective-pragmatist and usability-oriented high-adopter profiles. In addition, familiarity with PATs, male gender and participation in consortium are positively associated with the higher-propensity class, while age shows a weak negative correlation. Conclusion These results inform the design of extension and policy for specific segments, prioritising risk-mitigation trials for cautious growers, targeted information services for select profiles, and integrated PATs bundles for those with a high propensity.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"17 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148934093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Site-specific mechanical weeding robot with automatic self-adjustable individually controlled cultivator 现场专用机械除草机器人，带有自动自调节的独立控制耕耘机

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-09-04 DOI: 10.1007/s11119-026-10445-3

Shafi Md. Istiak, Mohammad Aftabi Talami, James Y. Kim, Sulaymon Eshkabilov

{"title":"Site-specific mechanical weeding robot with automatic self-adjustable individually controlled cultivator","authors":"Shafi Md. Istiak, Mohammad Aftabi Talami, James Y. Kim, Sulaymon Eshkabilov","doi":"10.1007/s11119-026-10445-3","DOIUrl":"https://doi.org/10.1007/s11119-026-10445-3","url":null,"abstract":"Context Weeds significantly reduce crop productivity and profitability, particularly in sugar beet production where losses exceed $1.25 billion annually in the United States. Limitations of herbicide-based control and drawbacks of conventional tillage highlight the need for efficient, sustainable alternatives. Aims To develop and evaluate an autonomous site-specific mechanical weeding (SSMW) system with adaptive control for precise inter-row weed removal under field conditions. Methods An SSMW system was designed by integrating an inter-row tillage mechanism with a robotic platform using RTK-GPS for navigation and site-specific actuation. Site-specific weeding was performed in the field test and a strain-based feedback control system was implemented in computer simulation to enable ASIC tillage under uneven terrain. Simulation studies based on field data were conducted to validate control performance, followed by field experiments to assess system effectiveness. Key Results The SSMW system’s efficacy was found to be 96.5% by tillage length. Conclusion The SSMW system achieved precise site-specific weeding using GPS-based control of individual sweeps, with a weeding efficacy of 96.5% based on tillage length. The ASIC framework with sensor feedback simulation showed the feasibility of improved adaptability to uneven terrain, with future work focusing on enhanced sensing and faster actuation for improved field performance. Implications and Impacts This study contributes to precision agriculture by enabling targeted weed management through intelligent sensing and control, reducing energy use and soil disruption. The system offers a sustainable alternative for organic farming, improving operational efficiency and environmental stewardship.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"43 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148883801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Facilitating a future agricultural data ecosystem: a cross-sectoral review of blockchain application in data sharing 促进未来农业数据生态系统：区块链在数据共享中的应用的跨部门审查

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-09-04 DOI: 10.1007/s11119-026-10446-2

Younghoo Cho, Ziwen Yu, Yiannis Ampatzidis, Ju-Seok Nam

引用次数: 0

In-season optical sensor-based variable-rate nitrogen management in maize production: a meta-analysis 基于季节光学传感器的玉米生产变速率氮肥管理：一项荟萃分析

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-09-04 DOI: 10.1007/s11119-026-10447-1

Amrinder Jakhar, Gonzalo J. Scarpin, Lorena N. Lacerda, Brenda V. Ortiz, Miguel L. Cabrera, George Vellidis, Leonardo M. Bastos

{"title":"In-season optical sensor-based variable-rate nitrogen management in maize production: a meta-analysis","authors":"Amrinder Jakhar, Gonzalo J. Scarpin, Lorena N. Lacerda, Brenda V. Ortiz, Miguel L. Cabrera, George Vellidis, Leonardo M. Bastos","doi":"10.1007/s11119-026-10447-1","DOIUrl":"https://doi.org/10.1007/s11119-026-10447-1","url":null,"abstract":"Purpose In-season optical sensor-based variable-rate nitrogen application (VRA) is designed to optimize nitrogen (N) management in maize (Zea mays L.) by aligning N inputs with real-time crop demand, yet reports on its performance are inconsistent. This study quantified the effects of VRA relative to fixed-rate management on N inputs, yield, N use efficiency, and profit; assessed VRA’s economic response to market volatility; identified key moderators; tested hierarchical interactions among moderators; and characterized economic and N-efficiency trade-offs. Methods A systematic review through February 2026 yielded 25 studies and 235 paired comparisons. The log response ratio was calculated for total N rate, grain yield, partial factor productivity of N (PFPN), and partial profit. Weighted linear mixed-effects models estimated overall effects, single-moderator analyses identified drivers, and conditional inference trees identified hierarchical interactions. Results VRA reduced total N rate by 18%, increased PFPN by 22%, and produced a 6% profit gain without affecting grain yield. Profitability was significant across all six market scenarios and scaled with the N: Maize price ratio. Agronomic outcomes were governed primarily by pre-plant N strategy, with the greatest benefits when pre-plant N was absent or minimal (≤ 12 kg N ha⁻¹). Economic outcomes were driven by algorithm design and application timing. A quadrant analysis showed simultaneous improvements in profit and PFPN in 57% of comparisons. Conclusion Relative to the uniform-rate treatments reported in the included studies, VRA reduced N inputs and improved efficiency while maintaining yield. Success depends on conservative pre-plant N strategies, appropriate algorithm selection, and early to mid-season timing.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"494 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148883834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Powdery mildew detection in sugar beet breeding trials using spectral sensing techniques 利用光谱传感技术检测甜菜育种试验中的白粉病

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-08-29 DOI: 10.1007/s11119-026-10441-7

François Stevens, Jeroen Degerickx, Stephanie Delalieux, Kristof Govaerts, Erik De Bruyne, Vincent Baeten, Philippe Vermeulen

引用次数: 0

Maize seedling center detection based on instance segmentation and skeleton-driven geometric morphological analysis 基于实例分割和骨架驱动几何形态分析的玉米幼苗中心检测

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-08-29 DOI: 10.1007/s11119-026-10438-2

Jiahang Pan, Xingyu Ban, Yuanzhi Xu, Qingzhen Zhu, Liyuan Zhang, Jizhan Liu, Liang Cao, Aichen Wang

引用次数: 0