Precision Agriculture最新文献_第6页

Estimating corn leaf nitrogen content using airborne VNIR-SWIR hyperspectral imagery 利用机载VNIR-SWIR高光谱图像估算玉米叶片氮含量

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-07-22 DOI: 10.1007/s11119-026-10420-y

Fengkai Tian, Jianfeng Zhou, Timothy Reinbott, Gurbir Singh, Jasmine Neupane, Noel Aloysius

{"title":"Estimating corn leaf nitrogen content using airborne VNIR-SWIR hyperspectral imagery","authors":"Fengkai Tian, Jianfeng Zhou, Timothy Reinbott, Gurbir Singh, Jasmine Neupane, Noel Aloysius","doi":"10.1007/s11119-026-10420-y","DOIUrl":"https://doi.org/10.1007/s11119-026-10420-y","url":null,"abstract":"Purpose Applying nitrogen at the right timing and rate is critical to improving nitrogen use efficiency and maximizing yield. This study aimed to evaluate an airborne hyperspectral imaging system covering the visible–near-infrared (VNIR) and shortwave infrared (SWIR) spectrum (400–2500 nm) for nondestructive quantification of corn leaf nitrogen content (LNC). Methods A field experiment was arranged as a randomized complete block design with six nitrogen rates (0–336 kg ha⁻¹) and two application timings (at-planting or split applied), each replicated four times. Hyperspectral imagery was acquired during the critical vegetative stage, and leaves from five consecutive plants per plot were sampled for laboratory LNC analysis. Images were processed using the first derivative (FD), Savitzky–Golay smoothing, and Gaussian filtering, then followed by six machine learning algorithms to predict LNC. Results Partial least squares regression (PLSR) achieved the highest accuracy (R² = 0.82; RMSE = 0.23%, g per 100 g dry weight), and FD outperformed the other two pre-processing methods. A three-band vegetation index, (R 588 − R 1667 )/(R 588 + R 1667 − R 1205 ), was strongly correlated with LNC ( r = 0.93). LNC at V8–V9 was positively correlated with final grain yield ( r = 0.62 and 0.83 for single and split applications, respectively). Conclusion The results demonstrate that combining VNIR-SWIR hyperspectral imaging with FD-based processing provides a rapid, nondestructive approach for precision nitrogen management in corn.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"57 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148538286","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}

引用次数: 0

The economic viability of spot-spraying for pesticides: the case of Northwest Germany 现场喷洒杀虫剂的经济可行性：以德国西北部为例

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-07-18 DOI: 10.1007/s11119-026-10409-7

Jens Schots, Till Kuhn, Niklas Möhring

{"title":"The economic viability of spot-spraying for pesticides: the case of Northwest Germany","authors":"Jens Schots, Till Kuhn, Niklas Möhring","doi":"10.1007/s11119-026-10409-7","DOIUrl":"https://doi.org/10.1007/s11119-026-10409-7","url":null,"abstract":"Purpose The reliance on pesticides for crop protection poses significant challenges, including biodiversity loss and environmental pollution. Despite the potential of precision farming technologies such as spot-sprayers for reducing pesticide applications, their adoption remains limited. Previous literature on the economic viability of precision spraying is sparse and did not account for relations between technology availability, prices, land use and adoption decisions. Methods We here analyze and compare the economic viability of different types of smart spot-sprayers for site-specific pesticide applications using a mixed-integer linear programming bio-economic farm model. We apply the model to typical cereal and sugar beet farms in North-Western Germany, which is one of the most productive agricultural regions in Europe. In our analysis, we account for key economic and performance parameters of precision sprayers and check the sensitivity of results regarding assumptions on technology and farm characteristics. Results The findings indicate that real-time spot-spraying of herbicides is economically viable for sugar beet farms exceeding 60-172 ha and cereal farms managing 300-400 ha. The large range of results reflects an important challenge for the adoption of spot sprayers. It highlights the sensitivity of economic viability to changes in technology type and performance and pesticide prices, which are often uncertain for farmers in advance. Broadening the use of the spot sprayer to multiple pesticide types and combining smaller and more precise spot-sprayers with broadcast sprayers improves the economic viability. Conclusions Overall, our results underscore the potential of smart spot-sprayers to contribute to sustainable agricultural practices by reducing pesticide use while maintaining profitability for farmers. Based on our results, we discuss how the adoption of these technologies could be facilitated to achieve environmental goals and improve farm economics.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"25 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148538149","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}

引用次数: 0

Assessing deep learning models for regional wheat yield prediction using Sentinel‑2 and environmental data 利用Sentinel‑2和环境数据评估区域小麦产量预测的深度学习模型

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-07-11 DOI: 10.1007/s11119-026-10415-9

Haoran Meng, Joel Segarra, Zhiwei Wang, Jose Luis Araus, Shawn C. Kefauver

{"title":"Assessing deep learning models for regional wheat yield prediction using Sentinel‑2 and environmental data","authors":"Haoran Meng, Joel Segarra, Zhiwei Wang, Jose Luis Araus, Shawn C. Kefauver","doi":"10.1007/s11119-026-10415-9","DOIUrl":"https://doi.org/10.1007/s11119-026-10415-9","url":null,"abstract":"Purpose Accurate and timely prediction of wheat yield is crucial to apply precision agriculture management and ensure food security. Since large-scale regional studies integrating multi-source data with deep learning remain limited, this study aims to propose a deep learning framework combining Sentinel-2 with environmental data for large-scale, field-level wheat yield prediction across central Spain. Methods Based on wheat yield data from central Spain over two seasons (2022–2023), we evaluated the potential of the Transformer model and compared its performance against partial least squares regression (PLSR), K-Nearest Neighbors Regression (KNN), and deep neural networks (DNN). Additionally, we assessed the relative importance of environmental variables versus spectral features and examined how single and combined phenological stages influence prediction accuracy. Results The Transformer achieved the highest accuracy when integrating all phenological stages and multi-source inputs (R² = 0.93, RMSE = 200 kg/ha), compared with DNN (R² = 0.91, RMSE = 225 kg/ha), KNN (R² = 0.88, RMSE = 257 kg/ha), and PLSR (R² = 0.77, RMSE = 358 kg/ha). Models using environmental variables performed better than those based on spectral data. Moreover, combining the tillering and heading stages provided a balance of high accuracy and timeliness, with the Transformer achieving R² = 0.91 and RMSE = 230 kg/ha. Conclusion This study demonstrates the improved performance of the Transformer model and underscores the critical importance of integrating multi-source environmental data and phenological information for robust crop yield forecasting. Impact This study proposes a data-driven precision agriculture framework that combines Sentinel-2 and environmental data for accurate, early-season wheat yield forecasting with relevant applications in precision agriculture.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"43 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148428817","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}

引用次数: 0

Plant area index estimation from UAV LiDAR time-series over cherry orchards 基于无人机激光雷达时间序列的樱桃园植物面积指数估算

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-07-09 DOI: 10.1007/s11119-026-10408-8

Marcel M. El Hajj, Kasper Johansen, Oliver M. Lopez Valencia, Fabio Veiga de Camargo, Yu-Hsuan Tu, Samer K. Al Mashaharawi, Omar A. López Camargo, Victor Angulo, Dominique Courault, Matthew F. McCabe

引用次数: 0

Characterization and impact of agricultural machinery traffic on alfalfa forage harvested 农机交通对苜蓿牧草收获的特性及影响

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-04-27 DOI: 10.1007/s11119-026-10355-4

Beatriz E. Bizzuti, Parker J. Williams, Marta M. Kohmann, Francisco J. Arriaga, Jessica L. Drewry, Dennis Hancock, Brian D. Luck

引用次数: 0

A lightweight two-stage framework for headland navigation area localization and path extraction in cornfields 玉米田海岬导航区域定位与路径提取的轻量级两阶段框架

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-04-27 DOI: 10.1007/s11119-026-10356-3

Yuting Zhai, Jian Li, Xiuling Yu, Yang Zhou, Yanlei Xu

引用次数: 0

Does farming make us human? a report on two Australian public deliberations 农业使我们成为人类吗？一份关于澳大利亚两次公开审议的报告

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-04-27 DOI: 10.1007/s11119-026-10339-4

Angie Sassano, Robert Sparrow, Christopher Mayes, Cheryl Travers, Megan Moss, Ashley Rootsey, Leland Glenna, Chris Degeling

{"title":"Does farming make us human? a report on two Australian public deliberations","authors":"Angie Sassano, Robert Sparrow, Christopher Mayes, Cheryl Travers, Megan Moss, Ashley Rootsey, Leland Glenna, Chris Degeling","doi":"10.1007/s11119-026-10339-4","DOIUrl":"https://doi.org/10.1007/s11119-026-10339-4","url":null,"abstract":"Purpose Farmers in Australia are currently facing profound challenges due to soil degradation, biodiversity loss, climate change, biosecurity threats, and difficulties in securing labour; farmers elsewhere face similar challenges. Increased use of robotics and artificial intelligence (AI) has been proposed as a possible solution to these problems. These technologies have the potential to radically transform the practice of farming: according to some authorities they hold out the prospect of a future in which, at least in some forms of farming, human farmers are no longer necessary. There is a strong case that such a radical transformation of the nature of agriculture would require social license. Methods In this paper, we report on the conduct and outcomes of public deliberations conducted in community juries in metropolitan and regional communities in Victoria, Australia, on the question: Under what conditions (if any) should we fully automate food and fibre production without human labour and decision-making? Findings The two juries came to different verdicts: full automation was rejected by consensus in metropolitan Melbourne but seen as being acceptable (under specific conditions) by a clear majority of jurors in the regional town of Mildura. Conclusion These findings, the existential concerns expressed by participants about the prospect of “farmerless farms”, and the quality of the deliberations in the juries, demonstrate the value of engaging with the public about technological trajectories and are highly relevant to the project of attempting to ensure that the use of robots and AI in agriculture has a social license.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"31 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147751524","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}

引用次数: 0

Spatio-temporal 4D phenotyping for automated morphological genotype differentiation of sugar beet 甜菜形态基因型自动分化的时空4D表型研究

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-04-25 DOI: 10.1007/s11119-026-10337-6

Jonas Bömer, Elias Marks, Facundo Ramón Ispizua Yamati, Cyrill Stachniss, Stefan Paulus, Anne-Katrin Mahlein

{"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}

引用次数: 0

Conventional management vs. precision viticulture: A comparison of different levels of mechanization and their impact on vineyard profitability 传统管理与精确葡萄栽培：不同机械化水平的比较及其对葡萄园盈利能力的影响

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-04-24 DOI: 10.1007/s11119-026-10345-6

Riccardo Testa, Gianluca Brunori, Antonino Galati

{"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 − 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.","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}

引用次数: 0

Multisource data and multitask deep learning for predicting alfalfa growth indicators 多源数据和多任务深度学习预测紫花苜蓿生长指标

IF 6.2 2区农林科学

Precision Agriculture Pub Date : 2026-03-27 DOI: 10.1007/s11119-026-10347-4

Jiali Guo, He Zhao, Haibin Tan, Yunling Wang, Haijun Yan

引用次数: 0