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}
Pub Date : 2026-01-31DOI: 10.1007/s11119-026-10323-y
Niharika Vullaganti, Billy G. Ram, Xiaomo Zhang, Carlos B. Pires, William Aderholdt, Paul Overby, Xin Sun
Introduction Soil nutrient management is essential for sustainable agriculture, directly affecting crop productivity and food security. Conventional laboratory-based methods for estimating soil nitrogen (N) and phosphorus (P), although accurate, are time-consuming, labor-intensive, and unsuitable for rapid or large-scale monitoring. Objectives This study aimed to develop an efficient, accurate, and scalable framework for soil nitrogen and phosphorus estimation using hyperspectral imaging integrated with deep learning techniques. Methods A total of 286 soil samples were collected from two agricultural locations in North Dakota during pre-sowing and post-harvest periods, capturing spatio-temporal variability. Laboratory chemical analyses were conducted to quantify soil N and P, and corresponding hyperspectral data were acquired in the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions. Spectral data were processed and categorized based on laboratory reference values. A convolutional neural network (CNN) model was developed for nutrient prediction, incorporating neural architecture search (NAS) and hyperparameter tuning for model optimization. The framework was evaluated using single-sensor and fused multi-sensor datasets, with spectral augmentation techniques applied to improve model robustness. Results Baseline CNN models achieved prediction accuracies of approximately 0.44, which improved to 0.68 with multi-sensor data fusion and spectral augmentation. Integration of NAS and hyperparameter tuning resulted in an additional 10–15% performance gain, achieving a final prediction accuracy of approximately 0.83 for combined nitrogen and phosphorus classification. NAS-based models showed minimal performance differences between raw and augmented datasets, while computational training time nearly doubled due to increased model search complexity. Applying NAS on raw hyperspectral data provided the most balanced trade-off between computational efficiency and predictive performance. Conclusions The integration of hyperspectral imaging with optimized CNN architectures and NAS enables accurate, scalable, and efficient soil nutrient prediction. This framework addresses spectral variability and environmental noise, offering a robust pathway for real-time soil nutrient monitoring and advancing data-driven precision agriculture.
{"title":"AI-Augmented hyperspectral soil sensing: predictive modeling of nitrogen and phosphorus using neural architecture search","authors":"Niharika Vullaganti, Billy G. Ram, Xiaomo Zhang, Carlos B. Pires, William Aderholdt, Paul Overby, Xin Sun","doi":"10.1007/s11119-026-10323-y","DOIUrl":"https://doi.org/10.1007/s11119-026-10323-y","url":null,"abstract":"Introduction Soil nutrient management is essential for sustainable agriculture, directly affecting crop productivity and food security. Conventional laboratory-based methods for estimating soil nitrogen (N) and phosphorus (P), although accurate, are time-consuming, labor-intensive, and unsuitable for rapid or large-scale monitoring. Objectives This study aimed to develop an efficient, accurate, and scalable framework for soil nitrogen and phosphorus estimation using hyperspectral imaging integrated with deep learning techniques. Methods A total of 286 soil samples were collected from two agricultural locations in North Dakota during pre-sowing and post-harvest periods, capturing spatio-temporal variability. Laboratory chemical analyses were conducted to quantify soil N and P, and corresponding hyperspectral data were acquired in the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions. Spectral data were processed and categorized based on laboratory reference values. A convolutional neural network (CNN) model was developed for nutrient prediction, incorporating neural architecture search (NAS) and hyperparameter tuning for model optimization. The framework was evaluated using single-sensor and fused multi-sensor datasets, with spectral augmentation techniques applied to improve model robustness. Results Baseline CNN models achieved prediction accuracies of approximately 0.44, which improved to 0.68 with multi-sensor data fusion and spectral augmentation. Integration of NAS and hyperparameter tuning resulted in an additional 10–15% performance gain, achieving a final prediction accuracy of approximately 0.83 for combined nitrogen and phosphorus classification. NAS-based models showed minimal performance differences between raw and augmented datasets, while computational training time nearly doubled due to increased model search complexity. Applying NAS on raw hyperspectral data provided the most balanced trade-off between computational efficiency and predictive performance. Conclusions The integration of hyperspectral imaging with optimized CNN architectures and NAS enables accurate, scalable, and efficient soil nutrient prediction. This framework addresses spectral variability and environmental noise, offering a robust pathway for real-time soil nutrient monitoring and advancing data-driven precision agriculture.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"381 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095816","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 : 2026-01-31DOI: 10.1007/s11119-026-10319-8
K. Persson, E. Ekholm, M. Söderström
Purpose Around a quarter of Sweden’s arable land is located within 20 m of a field boundary, yet little is known about crop growth conditions and optimal fertilization in field margins. Therefore, the present study aimed to investigate this, and assess whether there is reason to adjust fertilization in field edge zones. Methods The yield and grain quality of winter wheat ( Triticum aestivum L.) were determined at three distances from field edges (8 m, 26 m and 45 m) in eight transects bordering forests and eight transects bordering open land. Topsoil properties were determined in the same locations and differences between groups were statistically evaluated. Results The yield and thousand kernel weight were lower, and protein content was higher, close to field edges compared to yields in field interiors. The topsoil content of plant-available phosphorous (P) and potassium (K) was higher near the borders. Edge effects were greater towards forests than towards open land. The observed differences suggest lower rates of N, P and K by 22, 5 and 6 kg ha − 1 by field edges towards open land and 28, 13 and 19 kg ha − 1 by field edges towards forests, although the difference in K-rate by open land was not statistically demonstrated ( p > 0.05). Conclusion Reducing fertilizer rates in field margins can be a simple method of reducing redundant nutrient use without losing yield. More efficient nutrient use in crop production is necessary for the work towards environmental objectives, such as the 50% reduction of nutrient losses of the EU Farm to Fork Strategy.
瑞典大约四分之一的可耕地位于距农田边界20米的范围内，但人们对农田边缘的作物生长条件和最佳施肥知之甚少。因此，本研究旨在对此进行调查，并评估是否有理由调整田间边缘地带的施肥。方法在与森林接壤的8个样带和与开阔地接壤的8个样带中，对冬小麦（Triticum aestivum L.）的产量和籽粒品质进行了距离田边8 m、26 m和45 m距离的测定。在同一地点测定表土性质，并统计各组之间的差异。结果籽粒产量和千粒重较低，籽粒蛋白质含量较高，接近田间边缘。表层土壤速效磷(P)和钾(K)含量在边界附近较高。森林的边缘效应大于开阔地的边缘效应。观察到的差异表明，裸地的N、P和K速率比裸地低22、5和6 kg ha−1，裸地的28、13和19 kg ha−1，但裸地的K速率差异没有统计学意义（P > 0.05）。结论在不损失产量的情况下，降低田间边缘的施肥量是一种减少多余养分使用的简便方法。在作物生产中更有效地利用养分是实现环境目标的必要条件，例如欧盟“从农场到餐桌”战略将养分损失减少50%。
{"title":"Crop yield levels and nutrient requirements in field edge zones—is precision management motivated?","authors":"K. Persson, E. Ekholm, M. Söderström","doi":"10.1007/s11119-026-10319-8","DOIUrl":"https://doi.org/10.1007/s11119-026-10319-8","url":null,"abstract":"Purpose Around a quarter of Sweden’s arable land is located within 20 m of a field boundary, yet little is known about crop growth conditions and optimal fertilization in field margins. Therefore, the present study aimed to investigate this, and assess whether there is reason to adjust fertilization in field edge zones. Methods The yield and grain quality of winter wheat ( <jats:italic>Triticum aestivum</jats:italic> L.) were determined at three distances from field edges (8 m, 26 m and 45 m) in eight transects bordering forests and eight transects bordering open land. Topsoil properties were determined in the same locations and differences between groups were statistically evaluated. Results The yield and thousand kernel weight were lower, and protein content was higher, close to field edges compared to yields in field interiors. The topsoil content of plant-available phosphorous (P) and potassium (K) was higher near the borders. Edge effects were greater towards forests than towards open land. The observed differences suggest lower rates of N, P and K by 22, 5 and 6 kg ha <jats:sup>− 1</jats:sup> by field edges towards open land and 28, 13 and 19 kg ha <jats:sup>− 1</jats:sup> by field edges towards forests, although the difference in K-rate by open land was not statistically demonstrated ( <jats:italic>p</jats:italic> > 0.05). Conclusion Reducing fertilizer rates in field margins can be a simple method of reducing redundant nutrient use without losing yield. More efficient nutrient use in crop production is necessary for the work towards environmental objectives, such as the 50% reduction of nutrient losses of the EU Farm to Fork Strategy.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"89 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095817","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 : 2026-01-17DOI: 10.1007/s11119-025-10289-3
Helen Snethemba Ndlovu, John Odindi, Mbulisi Sibanda, Onisimo Mutanga
Purpose Taro (Colocasia esculenta (L)) , a neglected and underutilized crop species (NUS), holds great potential as a future smart crop that can thrive under climate variability and change, hence sustaining food security. While taro exhibits tolerance to drought conditions, variations in physiological attributes such as leaf temperature that rises under water stress and the associated stomatal closure that is initiated to conserve water, compromise crop productivity and overall yield. Therefore, monitoring taro crop physiological indicators of water status allows for the implementation of timely interventions and targeted adaption strategies to mitigate the effects of water deficit on taro crop productivity. Methods Unmanned Aerial Vehicles (UAV), integrated with high-resolution thermal sensors, provide valuable platform for generating near-real-time spatially explicit information suitable for assessing taro crop water status physiological indicators at farm scale. Hence, this study sought to evaluate the utility of UAV multi-modal thermal remote sensing and deep neural network techniques to estimate the equivalent water thickness, fuel moisture content, stomatal conductance, canopy temperature, and the chlorophyll content of smallholder taro crops. Results Findings showed that the multi-modal variable method achieves higher estimation accuracies in comparison to a single-modal technique, achieving R 2 values greater than 0.91 and rRSME values less than 14.15% of equivalent water thickness, fuel moisture content, stomatal conductance, canopy temperature, and chlorophyll content. Additionally, the results illustrated that the thermal wavebands and derived thermal indices are the most influential variables in estimating stomatal conductance and leaf temperature, yielding R 2 of 0.96 and 0.95, respectively. Conclusion These research findings underscore the applicability of UAV-acquired thermal remote sensing in providing rapid and robust spatially explicit information on smallholder taro crop water status for ensuring crop productivity and developing early warning systems of water stress. These findings serve as a stepping stone towards advancing agricultural monitoring frameworks and integrating NUS, such as taro, into traditional farming.
{"title":"Assessing neglected and underutilised taro crop water status using physiological indicators and UAV multi-modal thermal-multispectral data","authors":"Helen Snethemba Ndlovu, John Odindi, Mbulisi Sibanda, Onisimo Mutanga","doi":"10.1007/s11119-025-10289-3","DOIUrl":"https://doi.org/10.1007/s11119-025-10289-3","url":null,"abstract":"Purpose Taro <jats:italic>(Colocasia esculenta (L))</jats:italic> , a neglected and underutilized crop species (NUS), holds great potential as a future smart crop that can thrive under climate variability and change, hence sustaining food security. While taro exhibits tolerance to drought conditions, variations in physiological attributes such as leaf temperature that rises under water stress and the associated stomatal closure that is initiated to conserve water, compromise crop productivity and overall yield. Therefore, monitoring taro crop physiological indicators of water status allows for the implementation of timely interventions and targeted adaption strategies to mitigate the effects of water deficit on taro crop productivity. Methods Unmanned Aerial Vehicles (UAV), integrated with high-resolution thermal sensors, provide valuable platform for generating near-real-time spatially explicit information suitable for assessing taro crop water status physiological indicators at farm scale. Hence, this study sought to evaluate the utility of UAV multi-modal thermal remote sensing and deep neural network techniques to estimate the equivalent water thickness, fuel moisture content, stomatal conductance, canopy temperature, and the chlorophyll content of smallholder taro crops. Results Findings showed that the multi-modal variable method achieves higher estimation accuracies in comparison to a single-modal technique, achieving R <jats:sup>2</jats:sup> values greater than 0.91 and rRSME values less than 14.15% of equivalent water thickness, fuel moisture content, stomatal conductance, canopy temperature, and chlorophyll content. Additionally, the results illustrated that the thermal wavebands and derived thermal indices are the most influential variables in estimating stomatal conductance and leaf temperature, yielding R <jats:sup>2</jats:sup> of 0.96 and 0.95, respectively. Conclusion These research findings underscore the applicability of UAV-acquired thermal remote sensing in providing rapid and robust spatially explicit information on smallholder taro crop water status for ensuring crop productivity and developing early warning systems of water stress. These findings serve as a stepping stone towards advancing agricultural monitoring frameworks and integrating NUS, such as taro, into traditional farming.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"142 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005603","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 : 2026-01-03DOI: 10.1007/s11119-025-10313-6
Gabriel Dumbá Monteiro de Castro, Daniel Marçal de Queiroz, Domingos Sárvio Magalhães Valente, Diego Bedin Marin, Ryan Moreira Borges
{"title":"Early prediction of coffee production per plant using morphological indices","authors":"Gabriel Dumbá Monteiro de Castro, Daniel Marçal de Queiroz, Domingos Sárvio Magalhães Valente, Diego Bedin Marin, Ryan Moreira Borges","doi":"10.1007/s11119-025-10313-6","DOIUrl":"https://doi.org/10.1007/s11119-025-10313-6","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"3 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894510","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 : 2026-01-03DOI: 10.1007/s11119-025-10312-7
Carlos Castillo, Encarnación V. Taguas, Miguel Vallejo, Rafael Pérez, Robert R. Wells, Ronald L. Bingner, Helena Gómez-MacPherson
{"title":"Metrics of soil degradation by recent filling of permanent gullies: a study case on annual rainfed crops at the Campiña landscape (Spain)","authors":"Carlos Castillo, Encarnación V. Taguas, Miguel Vallejo, Rafael Pérez, Robert R. Wells, Ronald L. Bingner, Helena Gómez-MacPherson","doi":"10.1007/s11119-025-10312-7","DOIUrl":"https://doi.org/10.1007/s11119-025-10312-7","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"36 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894511","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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