Precision Agriculture最新文献

{"title":"Correction to: Using planetscope imagery to evaluate herbicide efficacy in maize (Zea mays) through post-application weed detection","authors":"Shlomi Aharon,Ran Lati,Hanan Eizenberg,Yafit Cohen","doi":"10.1007/s11119-026-10435-5","DOIUrl":"https://doi.org/10.1007/s11119-026-10435-5","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"61 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090530","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}

{"title":"Estimation of leaf nitrogen content of typical fruit trees in arid regions based on fusion of UAV multispectral information and texture features","authors":"Ran Pang,Mamat Sawut,Eniwar Sidik,Shanshan Liu,Weichen Tian,Reheman Rouzi,Xiaolong Zhang","doi":"10.1007/s11119-026-10465-z","DOIUrl":"https://doi.org/10.1007/s11119-026-10465-z","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"36 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149092192","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}

{"title":"The effect of train-test data splitting ratio on yield prediction from multispectral UAV imagery: effective reduction of training ratio with algorithm selection for single- and multi-date models","authors":"Lukas Prey,Patrick Ole Noack","doi":"10.1007/s11119-026-10464-0","DOIUrl":"https://doi.org/10.1007/s11119-026-10464-0","url":null,"abstract":"Abstract Context Optimizing the ratio of data required for training spectral grain yield (GY) prediction models is critical for efficient sampling of reference data, but has been rarely addressed so far. Given the high costs for collecting ground truth data, standard data splitting ratios which use the majority of the data for training and the rest for testing the models should be minimized, but in turn may affect prediction accuracy. Methods Therefore, this study evaluated 11 different train-test data splitting ratios (TSR), ranging from using 5% to 95% of the data for training, and the remaining portion as test sets. Models with six different machine learning algorithms were compared in winter wheat breeding trials conducted with each several thousand plots in two locations in Germany over a period of four years. The input data consisted of the (unmanned aerial vehicle) UAV-based NDRE index data from individual measurements dates as well as multi-date combinations. Results The results indicate that GY prediction remained relatively stable when decreasing TSR to about 0.30. Conversely, multi-date models tended to benefit more from higher TSR than single-date models. Support vector machine and random forest algorithms demonstrated relative advantage both for higher TSR and multi-date models, whereas partial least squares and ridge regression were the best algorithms for lowest TSR-values. Furthermore, analysis via repeated data splitting revealed minimum R² variability at a TSR of 0.30, but substantially increased variance at higher TSR values. Conclusions It is concluded that decreasing TSR while considering algorithm selection can potentially reduce costs without compromising prediction accuracy, therefore making spectral phenotyping methods more accessible and ready-to-use, whereas the absolute number of data points requires additional examination.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"3 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090531","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}

{"title":"Uncertainty propagation and intercomparison of multi-sensor measurements of vegetation stress in sub-optimal conditions","authors":"Mike Werfeli,Michal Antala,Abdallah Yussuf Ali Abdelmajeed,Álvaro Sánchez-Virosta,Zoe Halem,Alexander Merrington,Yousra El-Mejjaouy,Bojana Petrovic,Andreas Hueni,El Houssaine Bouras,Shawn Carlisle Kefauver,Sahameh Shafiee,Anshu Rastogi,Laura Mihai","doi":"10.1007/s11119-026-10455-1","DOIUrl":"https://doi.org/10.1007/s11119-026-10455-1","url":null,"abstract":"Abstract Purpose This study evaluates the metrological consistency and uncertainty propagation of spectral reflectance and vegetation indices derived from multiple remote sensing sensors under sub-optimal and variable illumination conditions. While multi-sensor data fusion is increasingly common in precision farming, the extent to which sensor differences arise from biological variation versus measurement uncertainty remains poorly quantified. Methods and results Field measurements were conducted on spring wheat ( Triticum aestivum L.) using three ground-based field spectroradiometers and two uncrewed aerial vehicles (UAV)-mounted multispectral cameras. Following a standardised Guide to the Expression of Uncertainty in Measurement (GUM) framework, uncertainties from sensor noise, spatial plot heterogeneity, and transient cloud cover were propagated using Monte Carlo simulations. The results indicate that total reflectance uncertainty peaked at approximately 11% in the red-edge and near-infrared regions, primarily driven by plot-level heterogeneity and fluctuating irradiance. Among the evaluated indices, the Optimized Soil-Adjusted Vegetation Index (OSAVI) demonstrated the highest stability across platforms, whereas the Enhanced Vegetation Index (EVI) proved highly sensitive to environmental noise, leading to metrological breakdown in sensor interoperability. The findings demonstrate that under unstable atmospheric conditions, the window for reliable multi-sensor data integration is limited to synchronous acquisitions within minutes. Conclusion This research provides a rigorous statistical foundation for identifying the limits of sensor agreement, ensuring that management decisions in precision agriculture are based on true crop signals rather than measurement artifacts.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"21 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149050063","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}

{"title":"Wheat yield prediction using machine learning, proximal sensing, nitrogen and climate data for Oklahoma","authors":"Meryem Maatougui,Steve Phillips,Samson Olaniyi Abiola,Daryl Brian Arnall","doi":"10.1007/s11119-026-10462-2","DOIUrl":"https://doi.org/10.1007/s11119-026-10462-2","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"56 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149049180","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}

{"title":"An agronomically informed framework for temporally transferable yield prediction","authors":"Ricardo Canal Filho,José Paulo Molin,Patrick Filippi,Eudocio Rafael Otavio da Silva,Luiz Gustavo de Goes Sterle,Marcelo Chan Fu Wei","doi":"10.1007/s11119-026-10454-2","DOIUrl":"https://doi.org/10.1007/s11119-026-10454-2","url":null,"abstract":"Abstract Purpose Yield maps are essential for agricultural decision-making, yet existing methods are often crop- or machine-specific, limiting their broader applicability. Methods This study introduces an agronomically informed framework for temporally transferable yield prediction that combines optimal phenological moment (OPM) selection with a spatial yield redistribution strategy: density-based yield mapping (DBYM). The framework relies on vegetation indices derived from satellite imagery acquired at physiologically meaningful crop stages and on independently known field-scale production values to guide spatial yield allocation. The method was evaluated across 10 growing seasons involving coffee, sugarcane, and wheat under both manual and mechanized production systems in commercial fields located in Brazil and Australia. In addition to DBYM, a simple linear regression baseline using the same OPM-derived inputs was evaluated to investigate the contribution of agronomically informed temporal input selection itself. Results Results demonstrated that OPM-based inputs alone already provided strong predictive capability across seasons, while DBYM improved the spatial coherence of predictions, particularly under sparse calibration scenarios. The index of agreement ranged from 0.73 to 0.94, while Moran’s bivariate index was significant ( p  < 0.01) for all DBYM predictions, confirming spatial consistency between observed and predicted yield patterns. RMSE values were comparable to those reported in machine learning and deep learning yield prediction studies, ranging from 0.21 to 0.28 Mg ha⁻¹ for coffee, 5.43–6.22 Mg ha⁻¹ for sugarcane, 0.15–0.26 Mg ha⁻¹ for wheat in Brazil, and 0.44–0.61 Mg ha⁻¹ for wheat in Australia. Conclusion The results suggest that predictive performance can be affected by the agronomic relevance of acquisition timing. By combining agronomically informed temporal inputs with a flexible spatial redistribution framework, DBYM enables the extension of yield map time series across seasons and provides a scalable and resource-efficient alternative for precision agriculture applications in fields lacking dedicated yield monitoring systems.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"22 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149049179","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}

{"title":"A hybrid PCA and 1D-CNN approach with regional priors for accurate agricultural monitoring: a case study with EuroCropsML","authors":"Nourelhouda Groun,Kheir-Eddine Otmani","doi":"10.1007/s11119-026-10449-z","DOIUrl":"https://doi.org/10.1007/s11119-026-10449-z","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"9 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149049185","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}

{"title":"Soil-driven variability in crop response to variable-rate seeding and fertilization in an irrigated maize–sunflower system","authors":"M. Videgain,J. A. Martínez-Casasnovas,S. Artero,A. Vigo,M. Vidal,F. J. García-Ramos","doi":"10.1007/s11119-026-10458-y","DOIUrl":"https://doi.org/10.1007/s11119-026-10458-y","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"9 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149050062","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}

{"title":"Factors influencing digital technology investments on French farms","authors":"Maha Ben Jaballah,Aude Ridier,Karine Daniel","doi":"10.1007/s11119-026-10429-3","DOIUrl":"https://doi.org/10.1007/s11119-026-10429-3","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"10 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149050060","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}

{"title":"Multi-source data assimilation of Sentinel-2 reflectance and SMAP soil moisture into APSIM for maize biomass estimation","authors":"Manuela Montella,Christian Bossung,Thanh Huy Nguyen,Marco Chini,Jean François Iffly,Thomas Udelhoven,Julia Kubanek,Zoltan Szantoi,Miriam Machwitz","doi":"10.1007/s11119-026-10442-6","DOIUrl":"https://doi.org/10.1007/s11119-026-10442-6","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"110 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149050061","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}