Pub Date : 2026-04-25DOI: 10.1007/s11119-026-10337-6
Jonas Bömer, Elias Marks, Facundo Ramón Ispizua Yamati, Cyrill Stachniss, Stefan Paulus, Anne-Katrin Mahlein
Purpose 3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select desirable traits, associated with e.g. drought tolerance or increased productivity. In sugar beet, morphological parameters depict an essential element of the variety approval for distinguishing between genotypes. However, only a limited number of measured or scored parameters are considered at a single time point. This study aims to disclose the benefit of incorporating dynamic spatio-temporal development of 3D parameters for automated crop genotype differentiation. Methods A greenhouse experiment was conducted covering twelve sugar beet genotypes. High-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted. The importance of these parameters was assessed over time, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Results Varying importance of parameters depending on the time point and the noticeable higher importance of plant parameters compared to leaf parameters are demonstrated by our results. Moreover, time series clustering produced more stable results than pointwise clustering with comparable accuracy. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development. Conclusions Spatio-temporal phenotyping did not outperform point-wise methods in peak accuracy but delivered markedly more stable genotype separation. Plant-level traits dominated discrimination, enabling simplified phenotyping. Early growth stages were most informative, with maximal separability at 72–75 days after sowing, contributing to breeding and variety approval.
{"title":"Spatio-temporal 4D phenotyping for automated morphological genotype differentiation of sugar beet","authors":"Jonas Bömer, Elias Marks, Facundo Ramón Ispizua Yamati, Cyrill Stachniss, Stefan Paulus, Anne-Katrin Mahlein","doi":"10.1007/s11119-026-10337-6","DOIUrl":"https://doi.org/10.1007/s11119-026-10337-6","url":null,"abstract":"Purpose 3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select desirable traits, associated with e.g. drought tolerance or increased productivity. In sugar beet, morphological parameters depict an essential element of the variety approval for distinguishing between genotypes. However, only a limited number of measured or scored parameters are considered at a single time point. This study aims to disclose the benefit of incorporating dynamic spatio-temporal development of 3D parameters for automated crop genotype differentiation. Methods A greenhouse experiment was conducted covering twelve sugar beet genotypes. High-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted. The importance of these parameters was assessed over time, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Results Varying importance of parameters depending on the time point and the noticeable higher importance of plant parameters compared to leaf parameters are demonstrated by our results. Moreover, time series clustering produced more stable results than pointwise clustering with comparable accuracy. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development. Conclusions Spatio-temporal phenotyping did not outperform point-wise methods in peak accuracy but delivered markedly more stable genotype separation. Plant-level traits dominated discrimination, enabling simplified phenotyping. Early growth stages were most informative, with maximal separability at 72–75 days after sowing, contributing to breeding and variety approval.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"26 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147739541","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 : 2026-04-24DOI: 10.1007/s11119-026-10345-6
Riccardo Testa, Gianluca Brunori, Antonino Galati
Purpose Viticulture is one of the most input-intensive agricultural sectors, in which the adoption of precision technologies could contribute significantly to reduce environmental impacts and operating costs. Previous studies have primarily focused on technical aspects, often examining individual farm operations or specific technologies using hypothetical data, rather than assessing economic impacts. To fill this research gap, this study evaluates the profitability of adopting varying levels of precision technologies in an Italian vineyard, focusing on two key farming operations - fertilisation and harvesting - using empirical data. Method This study adopted a partial budgeting approach comparing three differently managed vineyards: (a) conventional management (conventional spreader and manual harvest); (b) low-innovative management (VRT spreader and self-propelled harvester); (c) high-innovative management (VRT spreader and selective self-propelled harvester). Results The findings show that high-innovative management achieved the highest profitability value of 10,732.82 € ha − 1 year − 1 , which is twice that of conventional management. This is due to both direct cost savings (-66.1%) and increased revenues (+ 33.6%). However, precision technologies are only economically viable for farms larger than 25.81 ha (high-innovative management) and 16.42 ha (low-innovative management). Conclusion In this context, as the results of this study demonstrate, the provision of public subsidies aimed at reducing the high investment costs could represent a valid instrument to promote the adoption of precision agriculture technologies among winegrowers, thereby reducing the minimum farm size for their adoption. This study enriches the economic literature on precision agriculture technologies, also providing useful insights for winegrowers and policymakers.
{"title":"Conventional management vs. precision viticulture: A comparison of different levels of mechanization and their impact on vineyard profitability","authors":"Riccardo Testa, Gianluca Brunori, Antonino Galati","doi":"10.1007/s11119-026-10345-6","DOIUrl":"https://doi.org/10.1007/s11119-026-10345-6","url":null,"abstract":"Purpose Viticulture is one of the most input-intensive agricultural sectors, in which the adoption of precision technologies could contribute significantly to reduce environmental impacts and operating costs. Previous studies have primarily focused on technical aspects, often examining individual farm operations or specific technologies using hypothetical data, rather than assessing economic impacts. To fill this research gap, this study evaluates the profitability of adopting varying levels of precision technologies in an Italian vineyard, focusing on two key farming operations - fertilisation and harvesting - using empirical data. Method This study adopted a partial budgeting approach comparing three differently managed vineyards: (a) conventional management (conventional spreader and manual harvest); (b) low-innovative management (VRT spreader and self-propelled harvester); (c) high-innovative management (VRT spreader and selective self-propelled harvester). Results The findings show that high-innovative management achieved the highest profitability value of 10,732.82 € ha <jats:sup>− 1</jats:sup> year <jats:sup>− 1</jats:sup> , which is twice that of conventional management. This is due to both direct cost savings (-66.1%) and increased revenues (+ 33.6%). However, precision technologies are only economically viable for farms larger than 25.81 ha (high-innovative management) and 16.42 ha (low-innovative management). Conclusion In this context, as the results of this study demonstrate, the provision of public subsidies aimed at reducing the high investment costs could represent a valid instrument to promote the adoption of precision agriculture technologies among winegrowers, thereby reducing the minimum farm size for their adoption. This study enriches the economic literature on precision agriculture technologies, also providing useful insights for winegrowers and policymakers.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"34 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147739542","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 : 2026-03-27DOI: 10.1007/s11119-026-10347-4
Jiali Guo, He Zhao, Haibin Tan, Yunling Wang, Haijun Yan
{"title":"Multisource data and multitask deep learning for predicting alfalfa growth indicators","authors":"Jiali Guo, He Zhao, Haibin Tan, Yunling Wang, Haijun Yan","doi":"10.1007/s11119-026-10347-4","DOIUrl":"https://doi.org/10.1007/s11119-026-10347-4","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"192 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147535829","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/s11119-026-10336-7
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}
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