Pub Date : 2025-12-05DOI: 10.1007/s11119-025-10304-7
Vinay Vijayakumar, Antonio de Oliveira Costa Neto, Yiannis Ampatzidis, John Schueller, Won Suk Lee, Tom Burks
{"title":"Design and evaluation of a PI-controlled robotic smart sprayer for precision herbicide applications with multi-nozzle integration","authors":"Vinay Vijayakumar, Antonio de Oliveira Costa Neto, Yiannis Ampatzidis, John Schueller, Won Suk Lee, Tom Burks","doi":"10.1007/s11119-025-10304-7","DOIUrl":"https://doi.org/10.1007/s11119-025-10304-7","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"26 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680380","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 : 2025-12-02DOI: 10.1007/s11119-025-10300-x
Muhammad Abdul Munnaf, Xun Liao, Paula Sangines, Maria Calera, Angela Guerrero, Abdul Mounem Mouazen
{"title":"Environmental life cycle assessment of precision nitrogen fertilization in multiple field crops","authors":"Muhammad Abdul Munnaf, Xun Liao, Paula Sangines, Maria Calera, Angela Guerrero, Abdul Mounem Mouazen","doi":"10.1007/s11119-025-10300-x","DOIUrl":"https://doi.org/10.1007/s11119-025-10300-x","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"116 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657695","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 : 2025-11-16DOI: 10.1007/s11119-025-10298-2
Daniele Pinna, Elena Basso, Cristina Pornaro, Reddy Pullanagari, Stefano Macolino, Andrea Pezzuolo, Francesco Marinello
Context Accurate and regular estimation of above-ground biomass (AGB) in grassland ecosystems is essential for sustainable grazing management, feed planning, and carbon accounting. However, AGB mapping in heterogeneous grasslands remains challenging due to the spatial and temporal variability of vegetation and management practices. Aims This study explores the potential of Gaussian Process Regression (GPR) models combined with multispectral imagery from Sentinel-2 and PlanetScope to predict AGB across different grassland systems in Northern Italy. Methods Extensive field measurements (n = 954) were collected over 18 months across meadows, lowland pastures, and alpine grasslands, covering a range of altitudes, management regimes, and canopy structures. Spectral predictors from Sentinel-2 and PlanetScope were used to train independent GPR models and evaluate their predictive performance at both pixel and field scales. Key Results At the pixel level, GPR models achieved R 2 = 0.520 (Sentinel-2) and R 2 = 0.514 (PlanetScope) with mean absolute errors (MAE) of ~400 kg DM ha −1 , consistent with the high heterogeneity of grassland canopies. Aggregating predictions at the field scale markedly improved accuracy (R 2 = 0.972 and 0.968; MAE = 60–120 kg DM ha −1 , ≤10% relative error). These results are comparable to those of commercial pasture monitoring platforms. Conclusion The integration of high-resolution multispectral imagery and non-parametric GPR modeling allows robust AGB estimation in heterogeneous grasslands, reducing uncertainty through field-scale aggregation. Implications and Impacts This research provides a scalable and transferable framework for operational biomass monitoring, offering a practical tool for digital decision support systems (DSS) and a scientific basis for integration into carbon Measurement, Reporting, and Verification (MRV) protocols. The novelty of the study lies in demonstrating the combined use of Sentinel-2 and PlanetScope data within a unified GPR framework for multi-site grassland systems, validated through extensive field observations.
准确、规律地估算草地生态系统的地上生物量（AGB）对可持续放牧管理、饲料规划和碳核算至关重要。然而，由于植被和管理实践的时空变化，异质草原的AGB制图仍然具有挑战性。本研究探讨了高斯过程回归（GPR）模型结合Sentinel-2和PlanetScope的多光谱图像预测意大利北部不同草原系统AGB的潜力。方法在18个月的时间里，对草甸、低地牧场和高寒草原进行了广泛的野外测量（n = 954），涵盖了一系列海拔、管理制度和冠层结构。来自Sentinel-2和PlanetScope的光谱预测器用于训练独立的GPR模型，并在像素和场尺度上评估其预测性能。在像元水平上，GPR模型（Sentinel-2）和（PlanetScope）的平均绝对误差（MAE）分别为0.520和0.514，平均绝对误差为~400 kg DM ha - 1，与草地冠层的高度异质性相一致。在田间尺度上聚合预测显著提高了预测精度（r2 = 0.972和0.968;MAE = 60-120 kg DM ha - 1，相对误差≤10%）。这些结果与商业牧场监测平台的结果相当。结论高分辨率多光谱图像与非参数GPR模型的集成可以在非均匀草原上进行稳健的AGB估计，通过场尺度聚集减少不确定性。本研究为可操作的生物质监测提供了一个可扩展和可转移的框架，为数字决策支持系统（DSS）提供了实用工具，并为整合到碳测量、报告和验证（MRV）协议中提供了科学基础。该研究的新颖之处在于展示了在统一的GPR框架内结合使用Sentinel-2和PlanetScope数据用于多站点草地系统，并通过广泛的实地观测进行了验证。
{"title":"Optimising grassland Above-Ground biomass Estimation for managed grasslands: A Gaussian process regression approach for Sentinel-2 and Planet Scope in Northern Italy","authors":"Daniele Pinna, Elena Basso, Cristina Pornaro, Reddy Pullanagari, Stefano Macolino, Andrea Pezzuolo, Francesco Marinello","doi":"10.1007/s11119-025-10298-2","DOIUrl":"https://doi.org/10.1007/s11119-025-10298-2","url":null,"abstract":"Context Accurate and regular estimation of above-ground biomass (AGB) in grassland ecosystems is essential for sustainable grazing management, feed planning, and carbon accounting. However, AGB mapping in heterogeneous grasslands remains challenging due to the spatial and temporal variability of vegetation and management practices. Aims This study explores the potential of Gaussian Process Regression (GPR) models combined with multispectral imagery from Sentinel-2 and PlanetScope to predict AGB across different grassland systems in Northern Italy. Methods Extensive field measurements (n = 954) were collected over 18 months across meadows, lowland pastures, and alpine grasslands, covering a range of altitudes, management regimes, and canopy structures. Spectral predictors from Sentinel-2 and PlanetScope were used to train independent GPR models and evaluate their predictive performance at both pixel and field scales. Key Results At the pixel level, GPR models achieved R <jats:sup>2</jats:sup> = 0.520 (Sentinel-2) and R <jats:sup>2</jats:sup> = 0.514 (PlanetScope) with mean absolute errors (MAE) of ~400 kg DM ha <jats:sup>−1</jats:sup> , consistent with the high heterogeneity of grassland canopies. Aggregating predictions at the field scale markedly improved accuracy (R <jats:sup>2</jats:sup> = 0.972 and 0.968; MAE = 60–120 kg DM ha <jats:sup>−1</jats:sup> , ≤10% relative error). These results are comparable to those of commercial pasture monitoring platforms. Conclusion The integration of high-resolution multispectral imagery and non-parametric GPR modeling allows robust AGB estimation in heterogeneous grasslands, reducing uncertainty through field-scale aggregation. Implications and Impacts This research provides a scalable and transferable framework for operational biomass monitoring, offering a practical tool for digital decision support systems (DSS) and a scientific basis for integration into carbon Measurement, Reporting, and Verification (MRV) protocols. The novelty of the study lies in demonstrating the combined use of Sentinel-2 and PlanetScope data within a unified GPR framework for multi-site grassland systems, validated through extensive field observations.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"3 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145531529","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 : 2025-11-16DOI: 10.1007/s11119-025-10292-8
Kabindra Adhikari, Douglas R. Smith, Chad Hajda
Assessment of spatial variability of soil health indicators (SHI) from the root zone, not just the topsoil, is crucial for precise farm management decisions. We predicted the spatial distribution of soil organic carbon (SOC), inorganic carbon (SIC), total nitrogen (total-N), nitrate nitrogen (NO 3 -N), C: N ratio, phosphorus (PO 4 ), soil pH, and soil moisture (SM) from the root zone using soil samples from 0–15, 15–30, 30–60, 60–90 cm depths, apparent soil electrical conductivity (EC a ), topography, and a random forest (RF) model. The SHI and corn yield relationship was modeled and mapped, and the field was divided into soil health zones (SHZ) which were assessed for their agronomic significance. The RF model performed very well in predicting SM, pH, and SIC (R 2 up to 0.81), whereas PO 4 and total-N were weakly predicted (R 2 < 0.20) based on 30% test data. The EC a and terrain attributes (mrvbf, normht, sagawi, and fdem) were the most important predictors of SHI. The RF model was robust in quantifying the relationship between SHI and corn yield (R 2 = 0.64; RMSE = 0.80 Mt/ha) where SM appeared as the main predictor of yield variations followed by SIC, NO 3 -N, and pH. The field was divided into four SHZs, and yield responses from these zones were different. Results from this study can be useful for farm management decisions such as in soil health monitoring and variable-rate fertilization, and as a reference to future soil health and precision agriculture research.
{"title":"Digital mapping of selected soil health indicators from the root zone and their relationship with rainfed corn yield in Texas vertisols","authors":"Kabindra Adhikari, Douglas R. Smith, Chad Hajda","doi":"10.1007/s11119-025-10292-8","DOIUrl":"https://doi.org/10.1007/s11119-025-10292-8","url":null,"abstract":"Assessment of spatial variability of soil health indicators (SHI) from the root zone, not just the topsoil, is crucial for precise farm management decisions. We predicted the spatial distribution of soil organic carbon (SOC), inorganic carbon (SIC), total nitrogen (total-N), nitrate nitrogen (NO <jats:sub>3</jats:sub> -N), C: N ratio, phosphorus (PO <jats:sub>4</jats:sub> ), soil pH, and soil moisture (SM) from the root zone using soil samples from 0–15, 15–30, 30–60, 60–90 cm depths, apparent soil electrical conductivity (EC <jats:sub>a</jats:sub> ), topography, and a random forest (RF) model. The SHI and corn yield relationship was modeled and mapped, and the field was divided into soil health zones (SHZ) which were assessed for their agronomic significance. The RF model performed very well in predicting SM, pH, and SIC (R <jats:sup>2</jats:sup> up to 0.81), whereas PO <jats:sub>4</jats:sub> and total-N were weakly predicted (R <jats:sup>2</jats:sup> < 0.20) based on 30% test data. The EC <jats:sub>a</jats:sub> and terrain attributes (mrvbf, normht, sagawi, and fdem) were the most important predictors of SHI. The RF model was robust in quantifying the relationship between SHI and corn yield (R <jats:sup>2</jats:sup> = 0.64; RMSE = 0.80 Mt/ha) where SM appeared as the main predictor of yield variations followed by SIC, NO <jats:sub>3</jats:sub> -N, and pH. The field was divided into four SHZs, and yield responses from these zones were different. Results from this study can be useful for farm management decisions such as in soil health monitoring and variable-rate fertilization, and as a reference to future soil health and precision agriculture research.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"154 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145532121","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 : 2025-11-16DOI: 10.1007/s11119-025-10294-6
Octavian P. Chiriac, Samuele De Petris, Laura Zavattaro, Davide Cammarano
Purpose Nitrogen (N) fertilisation is one of the main factors contributing to crop yield. Nevertheless, only a limited number of studies have addressed the consequences of spatial variability on the N balance (Nb). While the spatial variability of soil properties has been widely investigated, its influence on Nb has been analysed in only a few studies. Therefore, the objectives of this study were to compute a complete Nb over two growing seasons at various points in a field, and to investigate the relationship between Nb and soil properties. Methods To investigate the effect of soil properties on Nb, a linear multivariate regression (LMR) model, was compared with a geographically weighted regression (GWR) model, which evaluates spatial variability. The data were collected in Denmark over a field cropped with potato and barley for two years. Results The average Nb was − 127 kg N ha − 1 in potato and 65 kg N ha − 1 in barley, and its primary driver was crop N uptake. Clay, silt, and pH were the most important soil drivers in both models but their effect was highly dependent on the year and location. Overall, GWR outperformed LMR in terms of explained variability (84% versus 30%, on average) and root mean squared error (16 versus 34 kg N ha − 1 , on average) in both years. Conclusion These results underline the importance of considering spatial variability when analysing N dynamics at the field level. Integrating the effect of soil properties on the N balance may promote more precise and sustainable fertilisation strategies.
氮肥是影响作物产量的主要因素之一。然而，只有有限数量的研究解决了空间变异对氮平衡（Nb）的影响。虽然土壤性质的空间变异已被广泛研究，但其对铌的影响仅在少数研究中进行了分析。因此，本研究的目的是计算在田间不同地点的两个生长季节的完整铌，并研究铌与土壤性质之间的关系。方法采用线性多元回归（LMR）模型与地理加权回归（GWR）模型比较土壤性质对铌含量的影响。这些数据是在丹麦一块种植了马铃薯和大麦的土地上收集的，为期两年。结果马铃薯和大麦的平均Nb值分别为- 127 kg N ha - 1和65 kg N ha - 1，其主要驱动因素是作物对氮的吸收。在两个模型中，粘土、粉砂和pH值是最重要的土壤驱动因素，但它们的影响高度依赖于年份和地点。总体而言，在两年中，GWR在可解释变异性（平均为84%对30%）和均方根误差（平均为16对34 kg N ha - 1）方面优于LMR。结论这些结果强调了在分析农田水平氮动态时考虑空间变异的重要性。综合土壤性质对氮平衡的影响，可以促进更精确和可持续的施肥策略。
{"title":"Exploring the spatial variability of nitrogen balance and its relationship with soil properties","authors":"Octavian P. Chiriac, Samuele De Petris, Laura Zavattaro, Davide Cammarano","doi":"10.1007/s11119-025-10294-6","DOIUrl":"https://doi.org/10.1007/s11119-025-10294-6","url":null,"abstract":"Purpose Nitrogen (N) fertilisation is one of the main factors contributing to crop yield. Nevertheless, only a limited number of studies have addressed the consequences of spatial variability on the N balance (Nb). While the spatial variability of soil properties has been widely investigated, its influence on Nb has been analysed in only a few studies. Therefore, the objectives of this study were to compute a complete Nb over two growing seasons at various points in a field, and to investigate the relationship between Nb and soil properties. Methods To investigate the effect of soil properties on Nb, a linear multivariate regression (LMR) model, was compared with a geographically weighted regression (GWR) model, which evaluates spatial variability. The data were collected in Denmark over a field cropped with potato and barley for two years. Results The average Nb was − 127 kg N ha <jats:sup>− 1</jats:sup> in potato and 65 kg N ha <jats:sup>− 1</jats:sup> in barley, and its primary driver was crop N uptake. Clay, silt, and pH were the most important soil drivers in both models but their effect was highly dependent on the year and location. Overall, GWR outperformed LMR in terms of explained variability (84% versus 30%, on average) and root mean squared error (16 versus 34 kg N ha <jats:sup>− 1</jats:sup> , on average) in both years. Conclusion These results underline the importance of considering spatial variability when analysing N dynamics at the field level. Integrating the effect of soil properties on the N balance may promote more precise and sustainable fertilisation strategies.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"7 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145532120","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 : 2025-11-16DOI: 10.1007/s11119-025-10296-4
Bernat Lavaquiol-Colell, Jordi Llorens-Calveras, Ricardo Sanz, Xavier Torrent, José M. Plata, Alexandre Escolà
{"title":"Methodology for the assessment of leaf area in fruit tree orchards using a terrestrial LiDAR-based system","authors":"Bernat Lavaquiol-Colell, Jordi Llorens-Calveras, Ricardo Sanz, Xavier Torrent, José M. Plata, Alexandre Escolà","doi":"10.1007/s11119-025-10296-4","DOIUrl":"https://doi.org/10.1007/s11119-025-10296-4","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"153 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525055","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 : 2025-11-16DOI: 10.1007/s11119-025-10293-7
Ana Morales-Ona, James Camberato, Robert Nielsen, Siddhartho Paul, Daniel Quinn
Purpose Spatial variability within fields and unpredictable rainfall patterns make nitrogen (N) management challenging, with up to 65% of applied N being lost to the environment. Post-emergence sidedress applications of N fertilizer can improve plant uptake and reduce N losses, making it critical to efficiently identify corn ( Zea mays L.) N status at early growth stages. We hypothesized that indicators of plant structure (plant height and canopy cover fraction), canopy greenness (vegetation indices), and their integration with soil and topography-related po would improve the prediction of early-season corn N status. The objectives of this study were to: (1) evaluate plant height, canopy cover fraction (CCF), and vegetation indices (VI) as indicators of biomass, N concentration, and N uptake at early growth stages (~ V4); (2) assess whether linear models integrating UAV-derived CCF with VI improve N uptake prediction; and (3) determine whether incorporating soil and topographic parameters from publicly available datasets into machine learning (ML) models improves performance over linear regressions. Methods Two large-scale field trials were conducted in Indiana during the 2019 growing season. Multispectral UAV (MicaSense Altum, 0.03 m resolution) and satellite imagery (Planet, 3 m resolution) were acquired and processed to extract CCF and calculate VI. Biomass samples were collected to determine N uptake. Linear regressions and three ML models were evaluated. Results Plant structural metrics, CCF and plant height, were the most reliable predictors of biomass and N uptake (R² up to 0.95). Integrating CCF with NIR-based VI improved or maintained model performance. Adding soil and topographic metrics provided limited improvement. Conclusion Linear regression models performed comparably to ML approaches, emphasizing the utility of simpler models for supporting more efficient in-season fertilizer applications. Performance differences across sites reflected variability in crop development and underscore challenges in model generalization.
{"title":"Integration of satellite, UAV, soil, and topographic data for assessing corn nitrogen uptake at early vegetative growth stages","authors":"Ana Morales-Ona, James Camberato, Robert Nielsen, Siddhartho Paul, Daniel Quinn","doi":"10.1007/s11119-025-10293-7","DOIUrl":"https://doi.org/10.1007/s11119-025-10293-7","url":null,"abstract":"Purpose Spatial variability within fields and unpredictable rainfall patterns make nitrogen (N) management challenging, with up to 65% of applied N being lost to the environment. Post-emergence sidedress applications of N fertilizer can improve plant uptake and reduce N losses, making it critical to efficiently identify corn ( <jats:italic>Zea mays</jats:italic> L.) N status at early growth stages. We hypothesized that indicators of plant structure (plant height and canopy cover fraction), canopy greenness (vegetation indices), and their integration with soil and topography-related po would improve the prediction of early-season corn N status. The objectives of this study were to: (1) evaluate plant height, canopy cover fraction (CCF), and vegetation indices (VI) as indicators of biomass, N concentration, and N uptake at early growth stages (~ V4); (2) assess whether linear models integrating UAV-derived CCF with VI improve N uptake prediction; and (3) determine whether incorporating soil and topographic parameters from publicly available datasets into machine learning (ML) models improves performance over linear regressions. Methods Two large-scale field trials were conducted in Indiana during the 2019 growing season. Multispectral UAV (MicaSense Altum, 0.03 m resolution) and satellite imagery (Planet, 3 m resolution) were acquired and processed to extract CCF and calculate VI. Biomass samples were collected to determine N uptake. Linear regressions and three ML models were evaluated. Results Plant structural metrics, CCF and plant height, were the most reliable predictors of biomass and N uptake (R² up to 0.95). Integrating CCF with NIR-based VI improved or maintained model performance. Adding soil and topographic metrics provided limited improvement. Conclusion Linear regression models performed comparably to ML approaches, emphasizing the utility of simpler models for supporting more efficient in-season fertilizer applications. Performance differences across sites reflected variability in crop development and underscore challenges in model generalization.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"170 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145531528","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 : 2025-11-03DOI: 10.1007/s11119-025-10285-7
Jannik Aaron Dresemann, Leon Ranscht, Michael Kuhwald, Marco Lorenz
Purpose EU policies aim to reduce pesticide use, yet the on-farm competitiveness of site-specific weed management (SSWM) technologies remains unclear. This study evaluates the economic performance of three SSWM technologies in Western Pomerania, Germany, at both crop and whole-farm levels, integrating soil compaction risk and workability assessments resulting from practice changes. Methods A typical farm model representing regional production systems served as a reference. Data on plant protection, machinery, costs and capacities were collected for a hoe plus band-spraying system, spot spraying based on unmanned aerial vehicle (UAV) field mapping and real-time spot spraying. Ex-ante scenario calculations and break-even assessments evaluated economic viability. The Spatially Explicit Soil Compaction Risk Assessment (SaSCiA) model assessed technology applicability based on wheel load carrying capacity and topsoil field capacity over nine years. Results SSWM technologies outperformed broadcast spraying in certain crops. However, at the farm level, costs of spot spraying based on UAV field mapping nearly offset herbicide savings, while real-time spot spraying increased costs by 24%, making it uncompetitive. Hoe plus band spraying raises costs by 10% and significantly exceeds wheel load limits in edge-season operations, posing agronomic challenges for winter oilseed rape. Conclusion A farm-level approach is essential for evaluating SSWM adoption. The combination of typical farm modeling, detailed plant protection data and soil compaction risk assessment proved effective for scenario analysis. Future research should refine weed pressure assessments, herbicide-saving potential and agronomic feasibility factors.
{"title":"A comparative study of three weed management technologies on a typical farm in Western Pomerania, Germany: integrating economic analysis and soil compaction risk modeling","authors":"Jannik Aaron Dresemann, Leon Ranscht, Michael Kuhwald, Marco Lorenz","doi":"10.1007/s11119-025-10285-7","DOIUrl":"https://doi.org/10.1007/s11119-025-10285-7","url":null,"abstract":"Purpose EU policies aim to reduce pesticide use, yet the on-farm competitiveness of site-specific weed management (SSWM) technologies remains unclear. This study evaluates the economic performance of three SSWM technologies in Western Pomerania, Germany, at both crop and whole-farm levels, integrating soil compaction risk and workability assessments resulting from practice changes. Methods A typical farm model representing regional production systems served as a reference. Data on plant protection, machinery, costs and capacities were collected for a hoe plus band-spraying system, spot spraying based on unmanned aerial vehicle (UAV) field mapping and real-time spot spraying. Ex-ante scenario calculations and break-even assessments evaluated economic viability. The Spatially Explicit Soil Compaction Risk Assessment (SaSCiA) model assessed technology applicability based on wheel load carrying capacity and topsoil field capacity over nine years. Results SSWM technologies outperformed broadcast spraying in certain crops. However, at the farm level, costs of spot spraying based on UAV field mapping nearly offset herbicide savings, while real-time spot spraying increased costs by 24%, making it uncompetitive. Hoe plus band spraying raises costs by 10% and significantly exceeds wheel load limits in edge-season operations, posing agronomic challenges for winter oilseed rape. Conclusion A farm-level approach is essential for evaluating SSWM adoption. The combination of typical farm modeling, detailed plant protection data and soil compaction risk assessment proved effective for scenario analysis. Future research should refine weed pressure assessments, herbicide-saving potential and agronomic feasibility factors.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"22 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145427959","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 : 2025-05-25DOI: 10.1007/s11119-025-10251-3
Yongxian Wang, Jingwei An, Mingchao Shao, Jianshuang Wu, Dong Zhou, Xia Yao, Xiaohu Zhang, Weixing Cao, Chongya Jiang, Yan Zhu
Purpose
This review synthesizes advancements in proximal spectral sensing devices—including portable, vehicle-based, UAV-based, and IoT-based—for monitoring field crop growth traits. By evaluating their technical capabilities, applications, and limitations, it addresses critical challenges in scalability, data integration, and environmental adaptability to advance precision agriculture (PA) practices.
Methods
A systematic analysis of literature (2001–2024) was conducted using keywords such as “proximal remote sensing,” “spectral sensors,” and “crop growth monitoring” in the Web of Science database, yielding 1,278 publications. The performance, sensing mechanisms, and practical applications of these devices were analyzed across platforms, with a focus on their ability to estimate key growth indicators (e.g., biomass, leaf area index, nitrogen content) and resolve PA-related challenges.
Results
Portable spectral sensors excel in capturing high-resolution, targeted measurements but face limitations in accuracy during early crop growth stages and under complex field conditions. Vehicle-based systems enable efficient large-area scanning but encounter synchronization challenges between sensors and machinery, alongside susceptibility to environmental interference. UAV-based devices deliver rapid, high-throughput data collection but require enhanced endurance and integration with satellite imagery to achieve regional scalability. IoT-based networks support continuous monitoring but are constrained by a lack of specialized spectral sensors and insufficient durability in harsh agricultural environments. Cross-platform data fusion remains impeded by heterogeneity in data types, spatial scales, and storage protocols, while device durability, algorithmic robustness, and environmental resilience emerge as critical factors for reliable field deployment.
Conclusions
Proximal spectral sensing devices hold transformative potential for multi-scale crop growth monitoring, yet persistent technical gaps hinder their widespread adoption. Future research should prioritize the development of lightweight hyperspectral imaging systems paired with advanced computational algorithms, unified frameworks for cross-platform data fusion, and durable IoT sensors tailored for harsh field conditions. Additionally, integrating UAV-based data with satellite observations will enhance regional insights, while standardized protocols and interdisciplinary collaboration are essential to bridge ground-to-space monitoring networks. These advancements will foster intelligent, sustainable crop management systems, ultimately addressing global agricultural productivity and sustainability challenges.
目的综述了近端光谱传感设备的研究进展，包括便携式、车载、无人机和物联网等。通过评估它们的技术能力、应用和局限性，它解决了可扩展性、数据集成和环境适应性方面的关键挑战，以推进精准农业（PA）实践。方法利用Web of Science数据库中“近端遥感”、“光谱传感器”、“作物生长监测”等关键词对2001-2024年的文献进行系统分析，共收录文献1278篇。研究人员跨平台分析了这些设备的性能、传感机制和实际应用，重点关注了它们估算关键生长指标（如生物量、叶面积指数、氮含量）和解决pa相关挑战的能力。结果便携式光谱传感器在捕获高分辨率、有针对性的测量方面具有优势，但在作物早期生长阶段和复杂的田间条件下，其精度存在局限性。基于车辆的系统能够实现高效的大面积扫描，但会遇到传感器和机械之间的同步挑战，以及对环境干扰的敏感性。基于无人机的设备提供快速、高通量的数据收集，但需要增强耐用性，并与卫星图像集成，以实现区域可扩展性。基于物联网的网络支持持续监测，但受到缺乏专业光谱传感器和恶劣农业环境耐久性不足的限制。跨平台数据融合仍然受到数据类型、空间尺度和存储协议异质性的阻碍，而设备耐用性、算法鲁棒性和环境弹性成为可靠现场部署的关键因素。结论近端光谱传感装置在多尺度作物生长监测中具有变革性的潜力，但持续的技术差距阻碍了其广泛应用。未来的研究应优先发展轻型高光谱成像系统，与先进的计算算法、跨平台数据融合的统一框架以及为恶劣现场条件量身定制的耐用物联网传感器相结合。此外，将基于无人机的数据与卫星观测相结合将增强区域洞察力，而标准化协议和跨学科合作对于连接地对空监测网络至关重要。这些进步将促进智能、可持续的作物管理系统，最终解决全球农业生产力和可持续性挑战。
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