Pub Date : 2026-03-04DOI: 10.1007/s11119-026-10336-7
Osiris Chávez-Martínez, Sergio Alberto Monjardin-Armenta, Jesús Gabriel Rangel-Peraza, Zuriel Dathan Mora-Félix, Antonio Jesús Sanhouse-García
{"title":"Crop Monitoring with Multiple Sensors: A Comparative Analysis and Validation of UAV, PlanetScope, and Sentinel-2 in Cherry Tomato","authors":"Osiris Chávez-Martínez, Sergio Alberto Monjardin-Armenta, Jesús Gabriel Rangel-Peraza, Zuriel Dathan Mora-Félix, Antonio Jesús Sanhouse-García","doi":"10.1007/s11119-026-10336-7","DOIUrl":"https://doi.org/10.1007/s11119-026-10336-7","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"200 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147359509","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}
Pub Date : 2026-03-04DOI: 10.1007/s11119-026-10322-z
Mehdi Rafiei, Muhammad Rizwan Asif, Michael Nørremark, Claus Aage Grøn Sørensen
Purpose Soil Water Content (SWC) is a critical factor in precision agriculture, influencing crop health, irrigation planning, and land management. Current methods for estimating SWC have low spatial resolution, fail to account for the spatial impact of surrounding areas, and are often limited to surface SWC. Therefore, this research aims to assess the feasibility of a high-resolution, multi-depth (root zone) SWC estimation method tailored for precision agriculture applications. Methods We propose a deep learning-based approach that integrates remote sensing and multimodal data to estimate SWC at high spatial resolution across multiple depths. Our method combines a U-Net model for spatial feature extraction, a Temporal Convolutional Network (TCN) for time-series processing, and a Feed-Forward Neural Network (FNN) for contextual information. A key challenge in this task is the scarcity of ground truth data due to the limited number of in-situ SWC measurements. To address this, we introduce the Relative Soil Water Content (RSWC) parameter, which enhances surface SWC estimation by leveraging historical remote sensing data. Results Using two field cases, we evaluate our model against two state-of-the-art methods: a point-based deep learning model and a numerical model. Results demonstrate that our approach outperforms both baselines in SWC estimation across different depths, achieving Mean Square Errors (MSEs) of 1.54% and 2.01% for the two fields, compared to 2.69% and 3.37% for the point-based method and 3.82% and 6.21% for the numerical model. Conclusions Our method generates high-resolution, multi-depth SWC maps for the entire field without requiring extensive in-situ measurements, presenting a multimodal deep learning approach as a practical proof-of-concept solution for large-scale agricultural applications.
{"title":"Towards high-spatial-resolution, multi-depth soil water content estimation via SAR data and multimodal deep learning","authors":"Mehdi Rafiei, Muhammad Rizwan Asif, Michael Nørremark, Claus Aage Grøn Sørensen","doi":"10.1007/s11119-026-10322-z","DOIUrl":"https://doi.org/10.1007/s11119-026-10322-z","url":null,"abstract":"Purpose Soil Water Content (SWC) is a critical factor in precision agriculture, influencing crop health, irrigation planning, and land management. Current methods for estimating SWC have low spatial resolution, fail to account for the spatial impact of surrounding areas, and are often limited to surface SWC. Therefore, this research aims to assess the feasibility of a high-resolution, multi-depth (root zone) SWC estimation method tailored for precision agriculture applications. Methods We propose a deep learning-based approach that integrates remote sensing and multimodal data to estimate SWC at high spatial resolution across multiple depths. Our method combines a U-Net model for spatial feature extraction, a Temporal Convolutional Network (TCN) for time-series processing, and a Feed-Forward Neural Network (FNN) for contextual information. A key challenge in this task is the scarcity of ground truth data due to the limited number of in-situ SWC measurements. To address this, we introduce the Relative Soil Water Content (RSWC) parameter, which enhances surface SWC estimation by leveraging historical remote sensing data. Results Using two field cases, we evaluate our model against two state-of-the-art methods: a point-based deep learning model and a numerical model. Results demonstrate that our approach outperforms both baselines in SWC estimation across different depths, achieving Mean Square Errors (MSEs) of 1.54% and 2.01% for the two fields, compared to 2.69% and 3.37% for the point-based method and 3.82% and 6.21% for the numerical model. Conclusions Our method generates high-resolution, multi-depth SWC maps for the entire field without requiring extensive in-situ measurements, presenting a multimodal deep learning approach as a practical proof-of-concept solution for large-scale agricultural applications.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"2 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147359510","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}
Pub Date : 2026-03-04DOI: 10.1007/s11119-026-10334-9
José O. Payero, Selvaraj Selvalakshmi
{"title":"Development and evaluation of a low-cost multispectral monitoring system for agricultural applications","authors":"José O. Payero, Selvaraj Selvalakshmi","doi":"10.1007/s11119-026-10334-9","DOIUrl":"https://doi.org/10.1007/s11119-026-10334-9","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"69 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147359511","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}
Pub Date : 2026-02-21DOI: 10.1007/s11119-026-10330-z
Maria Teresa Cappella, Francesco Caracciolo, Emanuele Blasi
Purpose This study evaluates the impact of nitrogen recommendations provided by a Decision Support Systems (DSS) on soft wheat production and technical efficiency in specialized Italian cereal farms. Methods Employing a Stochastic Frontier Analysis, the research evaluates the relationship between adherence to DSS recommendations and farm performance. The analysis relies on real farm data from the Barilla Farming platform, agrarian year 2022/2023, covering 487 farms and 1,664 fields, including suggested and actual nitrogen applications and observed yields. Results Findings indicate that compliance with DSS recommendations enhances output levels and efficiency, particularly for medium and large farms, whereas deviations, especially over-application, reduce efficiency with potential increase of costs and environmental risks. Notably, small farms maintain efficiency despite lower nitrogen applications, indicating the need for tailored DSS calibration. Results highlight the importance of site-specific nitrogen management strategies to optimize both economic and environmental outcomes. Conclusion While promoting DSS adoption is essential, our findings suggest that ensuring farmers’ compliance with DSS recommendations is equally—if not more—critical to realizing its full benefits. Policymakers and extension services should not only encourage the uptake of DSS but also focus on strategies that enhance farmers’ adherence to recommended practices. Additionally, ensuring the adaptability of DSS to different farm structures is key to maximizing its impact across varying production scales.
{"title":"A stochastic frontier approach to nitrogen use and efficiency in soft wheat cultivation","authors":"Maria Teresa Cappella, Francesco Caracciolo, Emanuele Blasi","doi":"10.1007/s11119-026-10330-z","DOIUrl":"https://doi.org/10.1007/s11119-026-10330-z","url":null,"abstract":"Purpose This study evaluates the impact of nitrogen recommendations provided by a Decision Support Systems (DSS) on soft wheat production and technical efficiency in specialized Italian cereal farms. Methods Employing a Stochastic Frontier Analysis, the research evaluates the relationship between adherence to DSS recommendations and farm performance. The analysis relies on real farm data from the Barilla Farming platform, agrarian year 2022/2023, covering 487 farms and 1,664 fields, including suggested and actual nitrogen applications and observed yields. Results Findings indicate that compliance with DSS recommendations enhances output levels and efficiency, particularly for medium and large farms, whereas deviations, especially over-application, reduce efficiency with potential increase of costs and environmental risks. Notably, small farms maintain efficiency despite lower nitrogen applications, indicating the need for tailored DSS calibration. Results highlight the importance of site-specific nitrogen management strategies to optimize both economic and environmental outcomes. Conclusion While promoting DSS adoption is essential, our findings suggest that ensuring farmers’ compliance with DSS recommendations is equally—if not more—critical to realizing its full benefits. Policymakers and extension services should not only encourage the uptake of DSS but also focus on strategies that enhance farmers’ adherence to recommended practices. Additionally, ensuring the adaptability of DSS to different farm structures is key to maximizing its impact across varying production scales.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"30 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146230822","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}
Pub Date : 2026-02-12DOI: 10.1007/s11119-026-10320-1
Bhaskar Aryal, Ajay Sharda, Andres Patrignani, Trevor Hefley, Ignacio Ciampitti
{"title":"Assessing inequality in corn plant spacing and yield using Lorenz curves and the Gini coefficient","authors":"Bhaskar Aryal, Ajay Sharda, Andres Patrignani, Trevor Hefley, Ignacio Ciampitti","doi":"10.1007/s11119-026-10320-1","DOIUrl":"https://doi.org/10.1007/s11119-026-10320-1","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"36 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146196671","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}
Pub Date : 2026-02-12DOI: 10.1007/s11119-026-10324-x
Chufeng Wang, Bin Liu, Jian Zhang, Yunhao You, Botao Wang, Guangshen Zhou, Bo Wang, Tao Wang
{"title":"From plot to field: A practical and robust model for rapeseed LAI inversion using a consumer-grade UAV RGB imaging platform","authors":"Chufeng Wang, Bin Liu, Jian Zhang, Yunhao You, Botao Wang, Guangshen Zhou, Bo Wang, Tao Wang","doi":"10.1007/s11119-026-10324-x","DOIUrl":"https://doi.org/10.1007/s11119-026-10324-x","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"119 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146196674","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}
Pub Date : 2026-02-09DOI: 10.1007/s11119-026-10326-9
Daniel Jackson, Jason Lessl, Leonardo M. Bastos, Matthew R. Levi
{"title":"Spatial variability in soil characteristics is associated with Vidalia onion pungency and yield","authors":"Daniel Jackson, Jason Lessl, Leonardo M. Bastos, Matthew R. Levi","doi":"10.1007/s11119-026-10326-9","DOIUrl":"https://doi.org/10.1007/s11119-026-10326-9","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"9 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146037","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}
Pub Date : 2026-02-09DOI: 10.1007/s11119-025-10307-4
Zhaosheng Yao, Dongwei Han, Ruimin Shao, Hainie Zha, Shaolong Zhu, Jianliang Wang, Muhammad Zain, Tao Liu, Fei Wu, Yuanzhi Wang, Chengming Sun
{"title":"Wheat biomass estimation by fusing color index and canopy volume based on UAV RGB images","authors":"Zhaosheng Yao, Dongwei Han, Ruimin Shao, Hainie Zha, Shaolong Zhu, Jianliang Wang, Muhammad Zain, Tao Liu, Fei Wu, Yuanzhi Wang, Chengming Sun","doi":"10.1007/s11119-025-10307-4","DOIUrl":"https://doi.org/10.1007/s11119-025-10307-4","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"108 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146035","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}
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