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Orbital multispectral imaging: a tool for discriminating management strategies for nematodes in coffee 轨道多光谱成像:鉴别咖啡线虫管理策略的工具
IF 6.2 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Pub Date : 2024-09-04 DOI: 10.1007/s11119-024-10188-z
Vinicius Silva Werneck Orlando, Bruno Sérgio Vieira, George Deroco Martins, Everaldo Antônio Lopes, Gleice Aparecida de Assis, Fernando Vasconcelos Pereira, Maria de Lourdes Bueno Trindade Galo, Leidiane da Silva Rodrigues

Background

Remote sensing based on multispectral imaging may be useful for detecting vegetation stress responses in agriculture.

Objectives

To evaluate the potential of orbital multispectral imaging in discriminating the most effective strategies for reducing plant-parasitic nematode populations, thereby preventing yield losses in coffee production.

Methods

Coffee plants were treated with eleven treatments, including Bacillus spp. isolates, commercial biological products, commercial chemical nematicides, and water (control group). Initial and final nematode populations in the soil were quantified, and surface reflectance data were collected using the Planet orbital multispectral sensor. The data were classified using the random tree algorithm.

Results

The population of plant-parasitic nematodes was reduced by 35.90% and 55.13% following the application of B. amyloliquefaciens isolate B266 and B. subtilis isolate B33, respectively. Under the conditions of this experiment, multispectral imaging accurately discriminated the most nematicidal treatments, with a global accuracy of 80%.

Conclusions

Orbital multispectral imaging can discriminate the most effective treatments used for nematode management in coffee plants, highlighting its potential as a supportive tool in agriculture.

背景基于多光谱成像的遥感技术可能有助于检测农业中的植被应激反应。目的评估轨道多光谱成像技术在鉴别减少植物寄生线虫数量的最有效策略方面的潜力,从而防止咖啡生产中的产量损失。方法用 11 种处理方法处理咖啡植株,包括芽孢杆菌属分离物、商业生物产品、商业化学杀线虫剂和水(对照组)。对土壤中的初始和最终线虫数量进行了量化,并使用 Planet 轨道多光谱传感器收集了表面反射率数据。结果施用淀粉芽孢杆菌分离物 B266 和枯草芽孢杆菌分离物 B33 后,植物寄生线虫的数量分别减少了 35.90% 和 55.13%。结论:轨道多光谱成像技术可以分辨出咖啡植物线虫管理中最有效的处理方法,凸显了其作为农业辅助工具的潜力。
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引用次数: 0
Incorporation of mechanistic model outputs as features for data-driven models for yield prediction: a case study on wheat and chickpea 将机理模型输出结果作为数据驱动模型的特征纳入产量预测:关于小麦和鹰嘴豆的案例研究
IF 6.2 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Pub Date : 2024-09-04 DOI: 10.1007/s11119-024-10184-3
Dhahi Al-Shammari, Yang Chen, Niranjan S. Wimalathunge, Chen Wang, Si Yang Han, Thomas F. A. Bishop

Introduction

Context Data-driven models (DDMs) are increasingly used for crop yield prediction due to their ability to capture complex patterns and relationships. DDMs rely heavily on data inputs to provide predictions. Despite their effectiveness, DDMs can be complemented by inputs derived from mechanistic models (MMs).

Methods

This study investigated enhancing the predictive quality of DDMs by using as features a combination of MMs outputs, specifically biomass and soil moisture, with conventional data sources like satellite imagery, weather, and soil information. Four experiments were performed with different datasets being used for prediction: Experiment 1 combined MM outputs with conventional data; Experiment 2 excluded MM outputs; Experiment 3 was the same as Experiment 1 but all conventional temporal data were omitted; Experiment 4 utilised solely MM outputs. The research encompassed ten field-years of wheat and chickpea yield data, applying the eXtreme Gradient Boosting (XGBOOST) algorithm for model fitting. Performance was evaluated using root mean square error (RMSE) and the concordance correlation coefficient (CCC).

Results and conclusions

The validation results showed that the XGBOOST model had similar predictive power for both crops in Experiments 1, 2, and 3. For chickpeas, the CCC ranged from 0.89 to 0.91 and the RMSE from 0.23 to 0.25 t ha−1. For wheat, the CCC ranged from 0.87 to 0.92 and the RMSE from 0.29 to 0.35 t ha−1. However, Experiment 4 significantly reduced the model's accuracy, with CCCs dropping to 0.47 for chickpeas and 0.36 for wheat, and RMSEs increasing to 0.46 and 0.65 t ha−1, respectively. Ultimately, Experiments 1, 2, and 3 demonstrated comparable effectiveness, but Experiment 3 is recommended for achieving similar predictive quality with a simpler, more interpretable model using biomass and soil moisture alongside non-temporal conventional features.

引言数据驱动模型(DDM)能够捕捉复杂的模式和关系,因此越来越多地用于作物产量预测。DDM 主要依靠数据输入来提供预测。尽管 DDM 非常有效,但仍可通过机理模型(MMs)的输入进行补充。本研究通过将 MMs 输出(特别是生物量和土壤湿度)与卫星图像、天气和土壤信息等传统数据源相结合,研究如何提高 DDM 的预测质量。利用不同的数据集进行了四次预测实验:实验 1 结合了 MM 输出和常规数据;实验 2 排除了 MM 输出;实验 3 与实验 1 相同,但省略了所有常规时间数据;实验 4 仅使用 MM 输出。研究涵盖了十个田间年的小麦和鹰嘴豆产量数据,采用了梯度提升算法(XGBOOST)进行模型拟合。验证结果表明,在实验 1、2 和 3 中,XGBOOST 模型对两种作物具有相似的预测能力。鹰嘴豆的 CCC 为 0.89 至 0.91,RMSE 为 0.23 至 0.25 吨/公顷。小麦的 CCC 为 0.87 至 0.92,RMSE 为 0.29 至 0.35 吨/公顷。然而,实验 4 大大降低了模型的准确性,鹰嘴豆和小麦的 CCC 分别降至 0.47 和 0.36,RMSE 分别增至 0.46 和 0.65 t ha-1。最终,实验 1、2 和 3 的效果相当,但建议进行实验 3,利用生物量和土壤水分以及非时间性常规特征,通过更简单、更易解释的模型实现类似的预测质量。
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引用次数: 0
Estimation of corn crop damage caused by wildlife in UAV images 估算无人机图像中野生动物对玉米作物造成的损害
IF 6.2 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Pub Date : 2024-09-03 DOI: 10.1007/s11119-024-10180-7
Przemysław Aszkowski, Marek Kraft, Pawel Drapikowski, Dominik Pieczyński

Purpose

This paper proposes a low-cost and low-effort solution for determining the area of corn crops damaged by the wildlife facility utilising field images collected by an unmanned aerial vehicle (UAV). The proposed solution allows for the determination of the percentage of the damaged crops and their location.

Methods

The method utilises image segmentation models based on deep convolutional neural networks (e.g., UNet family) and transformers (SegFormer) trained on over 300 hectares of diverse corn fields in western Poland. A range of neural network architectures was tested to select the most accurate final solution.

Results

The tests show that despite using only easily accessible RGB data available from inexpensive, consumer-grade UAVs, the method achieves sufficient accuracy to be applied in practical solutions for agriculture-related tasks, as the IoU (Intersection over Union) metric for segmentation of healthy and damaged crop reaches 0.88.

Conclusion

The proposed method allows for easy calculation of the total percentage and visualisation of the corn crop damages. The processing code and trained model are shared publicly.

目的 本文提出了一种低成本、低功耗的解决方案,利用无人飞行器(UAV)采集的田间图像,确定野生动物设施损坏的玉米作物面积。方法该方法利用基于深度卷积神经网络(如 UNet 系列)和变换器(SegFormer)的图像分割模型,这些模型在波兰西部超过 300 公顷的不同玉米田中经过训练。测试结果表明,尽管该方法仅使用了廉价消费级无人机上易于获取的 RGB 数据,但其准确性足以应用于农业相关任务的实际解决方案中,因为用于分割健康和受损作物的 IoU(Intersection over Union)指标达到了 0.88。处理代码和训练有素的模型已公开共享。
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引用次数: 0
Adoption of internet of things-enabled agricultural systems among Chinese agro-entreprises 中国农业企业采用物联网农业系统的情况
IF 6.2 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Pub Date : 2024-08-22 DOI: 10.1007/s11119-024-10182-5
Qing Yang, Abdullah Al Mamun, Mohammad Masukujjaman, Zafir Khan Mohamed Makhbul, Xueyun Zhong

Purpose

The adoption of the Internet of Things (IoT) technology in the agricultural sector has enormous potential for improving productivity, efficiency, and sustainability. Understanding the predictors affecting the acceptance of IoT-enabled agricultural systems (IAS) is crucial for policymakers, researchers, and industry practitioners.

Methods

This study adopted a cross-sectional design, collected quantitative data from 458 agro-entrepreneurs through structured interviews during July 2022, and applied partial least squares structural equation modeling for data analysis.

Results

The findings revealed that perceived need for IAS (β=0.187) and tolerance of diversity (β=0.166) positively linked with the attitude towards IAS, whereas attitude towards IAS (β=0.262), knowledge about IAS (β=0.309), industry influence (β=0.223), and IoT compatibility (β=0.274) have a positive effect on agroentrepreneurs’ intentions to adopt IAS at the 1% level of significance. Finally, the intention to adopt IAS shows a positive effect (β=0.442) on the adoption of IAS among the Chinese agro-entrepreneurs at the 1% level of significance. Using a multigroup analysis, this study also examined the associations based on the respondents’ age, gender, education level, land size, and monthly income.

Conclusion

This study establishes its originality by examining the relationship between original constructs derived from the theory of planned behavior and contextual factors, such as perceived need, industry influence, tolerance of diversity, innovativeness, knowledge, and compatibility, and investigating the relevant factors, thereby enhancing the comprehension of technology adoption processes in the agricultural sector. The results provide guidance to policymakers and professionals in formulating approaches to encourage the use of IoT in agriculture, supporting the objectives of the "Agriculture 4.0 Policy" and "Digital Rural Development Strategy" in China, and promoting sustainable development goals (SDG 13).

目的 农业部门采用物联网(IoT)技术在提高生产力、效率和可持续性方面潜力巨大。本研究采用横断面设计,在 2022 年 7 月通过结构化访谈收集了 458 名农业企业家的定量数据,并采用偏最小二乘结构方程模型进行数据分析。结果研究结果显示,农业企业主对农业企业信息化系统的感知需求(β=0.187)和多样性容忍度(β=0.166)与农业企业主对农业企业信息化系统的态度呈正相关,而对农业企业信息化系统的态度(β=0.262)、对农业企业信息化系统的认知(β=0.309)、行业影响力(β=0.223)和物联网兼容性(β=0.274)对农业企业主采用农业企业信息化系统的意向有正向影响,显著性水平为1%。最后,在 1%的显著性水平上,采用 IAS 的意愿对中国农业企业家采用 IAS 有正向影响(β=0.442)。通过多组分析,本研究还考察了基于受访者年龄、性别、教育水平、土地面积和月收入的相关性。 结论 本研究通过考察计划行为理论的原始建构与感知需求、行业影响、多样性容忍度、创新性、知识和兼容性等背景因素之间的关系,并对相关因素进行调查,从而增强了对农业部门技术采用过程的理解,从而确立了本研究的原创性。研究结果为政策制定者和专业人士制定鼓励在农业中使用物联网的方法提供了指导,支持了中国 "农业 4.0 政策 "和 "数字农村发展战略 "的目标,并促进了可持续发展目标(SDG 13)的实现。
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引用次数: 0
Evaluating the utility of combining high resolution thermal, multispectral and 3D imagery from unmanned aerial vehicles to monitor water stress in vineyards 评估结合无人驾驶飞行器提供的高分辨率热成像、多光谱成像和三维成像监测葡萄园水分胁迫的实用性
IF 6.2 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Pub Date : 2024-08-21 DOI: 10.1007/s11119-024-10179-0
V. Burchard-Levine, J. G. Guerra, I. Borra-Serrano, H. Nieto, G. Mesías-Ruiz, J. Dorado, A. I. de Castro, M. Herrezuelo, B. Mary, E. P. Aguirre, J. M. Peña

Purpose

High resolution imagery from unmanned aerial vehicles (UAVs) has been established as an important source of information to perform precise irrigation practices, notably relevant for high value crops often present in semi-arid regions such as vineyards. Many studies have shown the utility of thermal infrared (TIR) sensors to estimate canopy temperature to inform on vine physiological status, while visible-near infrared (VNIR) imagery and 3D point clouds derived from red–green–blue (RGB) photogrammetry have also shown great promise to better monitor within-field canopy traits to support agronomic practices. Indeed, grapevines react to water stress through a series of physiological and growth responses, which may occur at different spatio-temporal scales. As such, this study aimed to evaluate the application of TIR, VNIR and RGB sensors onboard UAVs to track vine water stress over various phenological periods in an experimental vineyard imposed with three different irrigation regimes.

Methods

A total of twelve UAV overpasses were performed in 2022 and 2023 where in situ physiological proxies, such as stomatal conductance (gs), leaf (Ψleaf) and stem (Ψstem) water potential, and canopy traits, such as LAI, were collected during each UAV overpass. Linear and non-linear models were trained and evaluated against in-situ measurements.

Results

Results revealed the importance of TIR variables to estimate physiological proxies (gs, Ψleaf, Ψstem) while VNIR and 3D variables were critical to estimate LAI. Both VNIR and 3D variables were largely uncorrelated to water stress proxies and demonstrated less importance in the trained empirical models. However, models using all three variable types (TIR, VNIR, 3D) were consistently the most effective to track water stress, highlighting the advantage of combining vine characteristics related to physiology, structure and growth to monitor vegetation water status throughout the vine growth period.

Conclusion

This study highlights the utility of combining such UAV-based variables to establish empirical models that correlated well with field-level water stress proxies, demonstrating large potential to support agronomic practices or even to be ingested in physically-based models to estimate vine water demand and transpiration.

目的 无人飞行器(UAVs)提供的高分辨率图像已被确定为进行精确灌溉的重要信息来源,尤其适用于葡萄园等半干旱地区常见的高价值作物。许多研究表明,热红外(TIR)传感器可以估算树冠温度,为葡萄树的生理状态提供信息,而可见光-近红外(VNIR)图像和红-绿-蓝(RGB)摄影测量法生成的三维点云也很有希望更好地监测田间树冠特征,为农艺实践提供支持。事实上,葡萄树通过一系列生理和生长反应对水分胁迫做出反应,这些反应可能发生在不同的时空尺度上。因此,本研究旨在评估无人机搭载的 TIR、VNIR 和 RGB 传感器的应用情况,以跟踪采用三种不同灌溉制度的实验葡萄园中不同物候期的葡萄树水分胁迫情况。方法 在 2022 年和 2023 年共进行了 12 次无人机飞越,在每次无人机飞越期间收集原位生理代用指标,如气孔导度(gs)、叶片(Ψ叶)和茎(Ψ茎)水势以及冠层特征,如 LAI。结果结果表明,TIR 变量对估算生理代用指标(gs、Ψ叶、Ψ茎)非常重要,而 VNIR 和 3D 变量对估算 LAI 至关重要。近红外和三维变量与水分胁迫代用指标基本不相关,在训练的经验模型中重要性较低。然而,使用所有三种变量类型(TIR、VNIR、3D)的模型在跟踪水分胁迫方面一直是最有效的,这凸显了结合与生理、结构和生长相关的藤蔓特征来监测整个藤蔓生长期植被水分状况的优势。
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引用次数: 0
A decision-supporting system for vineyard management: a multi-temporal approach with remote and proximal sensing 葡萄园管理决策支持系统:利用遥感和近距离传感的多时空方法
IF 6.2 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Pub Date : 2024-08-20 DOI: 10.1007/s11119-024-10177-2
A. Deidda, A. Sassu, L. Mercenaro, G. Nieddu, C. Fadda, P. F. Deiana, F. Gambella

Purpose

Site-specific field management operations represent one of the fundamental principles of precision viticulture. The purpose of the research is to observe and analyse the evolution of a vineyard over three consecutive years to understand which factors most significantly influence the quality of the vineyard’s production.

Methods

The research involved technologically advanced tools for crop monitoring, such as remote and proximal sensors for vegetation surveys. In association, grape quality analyses were performed through laboratory analysis, constructing geostatistical interpolation maps and matrix correlation tables.

Results

Both remote and proximal sensing instruments demonstrated their ability to effectively estimate the spatial distribution of vegetative and quality characteristics within the vineyard. Information obtained from GNDVI and CHM proved to be valuable and high-performance tools for assessing field variability. The differentiated plant management resulted in uniform production quality characteristics, a change evident through the monitoring techniques.

Conclusion

The research highlights the effectiveness of using advanced technological instruments for crop monitoring and their importance in achieving uniformity in production quality characteristics through differentiated plant management. From the results obtained, it was possible to observe how differentiated plant management led to a uniformity of production quality characteristics and how the monitoring techniques can observe their evolution. This result represents a positive accomplishment in field management during the three monitoring years, responding to the principles and objectives of precision agriculture.

目的因地制宜的田间管理操作是精准葡萄栽培的基本原则之一。这项研究的目的是观察和分析葡萄园连续三年的变化情况,以了解哪些因素对葡萄园的产量质量影响最大。研究方法这项研究采用了技术先进的作物监测工具,如用于植被调查的远程和近距离传感器。结果两种遥感和近距离传感仪器都证明了它们能够有效估计葡萄园内植被和质量特征的空间分布。从 GNDVI 和 CHM 中获得的信息被证明是评估田间变异性的有价值的高性能工具。研究强调了使用先进技术手段进行作物监测的有效性,以及这些手段在通过差异化植物管理实现生产质量特征统一方面的重要性。从获得的结果中,我们可以观察到差异化植物管理是如何实现生产质量特征的统一性的,以及监测技术是如何观察到它们的变化的。这一结果表明,在三个监测年份中,田间管理取得了积极成果,符合精准农业的原则和目标。
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引用次数: 0
Simulating within-field spatial and temporal corn yield response to nitrogen with APSIM model 利用 APSIM 模型模拟玉米产量对氮素的时空响应
IF 6.2 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Pub Date : 2024-08-13 DOI: 10.1007/s11119-024-10178-1
Laura J. Thompson, Sotirios V. Archontoulis, Laila A. Puntel

Context

Process-based crop growth models can explain soil and crop dynamics that influence the optimal N rate for crop production. Currently, there is a lack of understanding regarding the accuracy of process-based models for site-specific zones within fields, as well as the key factors that need to be considered when calibrating these models for zone-specific economic optimum N rate (EONR).

Objective

We calibrated the Agricultural Production Systems sIMulator (APSIM) model in contrasting zones within fields, quantified the model performance, and used the calibrated model to develop long-term corn yield response to N to assess the temporal variability between zones and sites to assist decision making.

Methods

We conducted four N rate experiments (2 fields × 2 zones within a field) over two years in southeast Nebraska. Experimental data were used to calibrate and test the APSIM model. APSIM simulated corn yield response to N for each zone and site was obtained by running numerous iterations of the calibrated model at different N rates. Observed and simulated corn yield response to N rate were analyzed with statistical models to estimate the EONR.

Results and conclusions

The APSIM model predicted corn yield over 11 historical years with a relative root mean square error (RRMSE) of 12% and yield at EONR in the N studies with RRMSE of 8.8%. The simulated EONR was lower than the observed EONR across sites, years, and zones with greater error than yield. The simulated yield increase with N fertilization was under-estimated in fine textured soils and over-estimated in medium textured soils. Long-term corn yield response to N showed that temporal variation in simulated EONR was greater than spatial variation. Long-term EONR and yield at EONR increased with increasing rainfall, while yield at zero N was greatest in normal years. Temporal variation was driven primarily by year-to-year variation in N loss (CV of 67% ± 9.5). Soil texture, hydrological properties, water table, and tile drainage were key variables for accurate site-specific model calibration. Improvements in simulating site-specific EONR may be realized by including in-situ or remotely sensed data for better estimation of N dynamics. We concluded that APSIM can provide valuable insights into systems dynamics in this region, but it can’t provide precise N-rate estimates. Our study contributes to understanding of the within-field variability using simulation modeling.

背景基于过程的作物生长模型可以解释影响作物生产最佳氮肥用量的土壤和作物动态。目前,人们对基于过程的模型在田间特定地点特定区域的准确性以及在校准这些模型时需要考虑的关键因素还缺乏了解。方法我们在内布拉斯加州东南部进行了为期两年的四次氮肥率实验(2 块田 × 田内 2 个区)。实验数据用于校准和测试 APSIM 模型。通过在不同氮肥施用率下对校准模型进行多次迭代,获得了 APSIM 模拟的每个区域和地点的玉米产量对氮肥的响应。结果和结论APSIM 模型预测了 11 个历史年份的玉米产量,相对均方根误差 (RRMSE) 为 12%,而氮研究中 EONR 的产量相对均方根误差 (RRMSE) 为 8.8%。不同地点、年份和区域的模拟 EONR 均低于观测到的 EONR,误差大于产量。在细粒度土壤中,氮肥的模拟增产效果被低估,而在中等粒度土壤中则被高估。玉米对氮的长期产量响应表明,模拟 EONR 的时间变化大于空间变化。长期 EONR 和 EONR 时的产量随着降雨量的增加而增加,而零 N 时的产量在正常年份最大。时间变化主要由氮损失的年际变化(CV 为 67% ± 9.5)驱动。土壤质地、水文特性、地下水位和瓦片排水是准确校准特定地点模型的关键变量。通过纳入原位或遥感数据,更好地估算氮的动态变化,可以改进特定地点的 EONR 模拟。我们的结论是,APSIM 可以为该地区的系统动力学提供有价值的见解,但它无法提供精确的氮速率估算。我们的研究有助于利用模拟建模了解田间变化。
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引用次数: 0
Predicting on-farm soybean yield variability using texture measures on Sentinel-2 image 利用 "哨兵-2 "图像的纹理测量方法预测农场大豆产量变化
IF 6.2 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Pub Date : 2024-08-12 DOI: 10.1007/s11119-024-10176-3
Rodrigo Greggio de Freitas, Henrique Oldoni, Lucas Fernando Joaquim, João Vítor Fiolo Pozzuto, Lucas Rios do Amaral

Yield forecasting and within-field yield variation is essential information that helps farmers develop sustainable agriculture. However, such information still needs to be included for most of them, and remote sensing is an alternative to provide it. Our objective was to assess Random Forest regression models composed of unique GLCM texture measures as an alternative to usual empirical models that use spectral response and auxiliary data, which is complex and reaches varied results. Eleven GLCM texture models based on eight texture measures of a single spectral layer were assessed to represent soybean field yield variation in two sites and seasons. Several models achieved satisfactory results, reaching R2 from 0.90 to 0.95 and RMSE from 0.06 to 0.26 t/ha. Models above 15-window size are recommended for the soybean yield prediction as window size is an essential attribute to GLCM performance. Models derived from the bands individually (red, red-edge, near-infrared, and short wavelength infrared) were more sensitive to the window size than those derived from vegetation indices (EVI, GNDVI, GRNDVI, NDMI, NDRE, NDVI, SFDVI). The data aggregated by texture measures improve the individual spectral responses, providing alternatives to predict soybean within-field yield variation using random forest models.

产量预测和田间产量变化是帮助农民发展可持续农业的基本信息。然而,对于大多数农民来说,这些信息仍需包括在内,而遥感则是提供这些信息的替代方法。我们的目标是评估由独特的 GLCM 纹理度量组成的随机森林回归模型,以替代使用光谱响应和辅助数据的常规经验模型,后者非常复杂,结果也各不相同。我们评估了 11 个基于单个光谱层的 8 个纹理测量值的 GLCM 纹理模型,以表示两个地点和两个季节的大豆田间产量变化。几个模型取得了令人满意的结果,R2 从 0.90 到 0.95 不等,RMSE 从 0.06 到 0.26 吨/公顷不等。由于窗口大小是影响 GLCM 性能的一个重要因素,因此建议采用 15 个窗口以上的模型进行大豆产量预测。与植被指数(EVI、GNDVI、GRNDVI、NDMI、NDRE、NDVI、SFDVI)相比,单独从波段(红、红边、近红外和短波红外)得出的模型对窗口大小更为敏感。通过纹理度量汇总的数据改善了单个光谱响应,为使用随机森林模型预测大豆田间产量变化提供了替代方法。
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引用次数: 0
Crop stress detection from UAVs: best practices and lessons learned for exploiting sensor synergies 无人机作物胁迫检测:利用传感器协同作用的最佳做法和经验教训
IF 6.2 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Pub Date : 2024-08-11 DOI: 10.1007/s11119-024-10168-3
Erekle Chakhvashvili, Miriam Machwitz, Michal Antala, Offer Rozenstein, Egor Prikaziuk, Martin Schlerf, Paul Naethe, Quanxing Wan, Jan Komárek, Tomáš Klouek, Sebastian Wieneke, Bastian Siegmann, Shawn Kefauver, Marlena Kycko, Hamadou Balde, Veronica Sobejano Paz, Jose A. Jimenez-Berni, Henning Buddenbaum, Lorenz Hänchen, Na Wang, Amit Weinman, Anshu Rastogi, Nitzan Malachy, Maria-Luisa Buchaillot, Juliane Bendig, Uwe Rascher

Introduction

Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking.

Materials and methods

This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control.

Results

Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls.

Conclusion

Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.

引言 检测和监控作物胁迫对于确保作物的充足和可持续生产至关重要。无人飞行器(UAV)技术的最新进展为绘制指示作物胁迫的关键作物特征图提供了一种前景广阔的方法。虽然使用安装在无人飞行器上的单个光学传感器就足以监测一般意义上的作物状况,但采用覆盖不同光谱光学域的多个传感器,可以更精确地描述作物与生物或非生物胁迫因素之间的相互作用。材料与方法 本研究探讨了获取高质量多传感器数据的关键方面,包括任务规划、传感器特性和辅助数据的重要性。结果多传感器数据的成功获取取决于最佳的时间安排、适当的传感器校准以及辅助数据(如地面控制点和气象站信息)的使用。在融合不同传感器数据时,应在物理单位层面上进行,并使用质量标志排除不稳定或有偏差的测量结果。本文强调了使用核对表、考虑照明条件和进行试飞以发现潜在隐患的重要性。本文提供了关于如何结合不同的无人机光学传感器的实用信息,并讨论了在作物胁迫监测中图像数据采集和后处理的成熟科学实践。
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引用次数: 0
Assessing grapevine water status in a variably irrigated vineyard with NIR/SWIR hyperspectral imaging from UAV 利用无人飞行器的近红外/西红外高光谱成像技术评估不同灌溉条件葡萄园的葡萄水分状况
IF 6.2 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Pub Date : 2024-08-06 DOI: 10.1007/s11119-024-10170-9
E. Laroche-Pinel, K. R. Vasquez, L. Brillante

Remote sensing is now a valued solution for more accurately budgeting water supply by identifying spectral and spatial information. A study was put in place in a Vitis vinifera L. cv. Cabernet-Sauvignon vineyard in the San Joaquin Valley, CA, USA, where a variable rate automated irrigation system was installed to irrigate vines with twelve different water regimes in four randomized replicates, totaling 48 experimental zones. The purpose of this experimental design was to create variability in grapevine water status, in order to produce a robust dataset for modeling purposes. Throughout the growing season, spectral data within these zones was gathered using a Near InfraRed (NIR) - Short Wavelength Infrared (SWIR) hyperspectral camera (900 to 1700 nm) mounted on an Unmanned Aircraft Vehicle (UAV). Given the high water-absorption in this spectral domain, this sensor was deployed to assess grapevine stem water potential, Ψstem, a standard reference for water status assessment in plants, from pure grapevine pixels in hyperspectral images. The Ψstem was acquired simultaneously in the field from bunch closure to harvest and modeled via machine-learning methods using the remotely sensed NIR-SWIR data as predictors in regression and classification modes (classes consisted of physiologically different water stress levels). Hyperspectral images were converted to bottom of atmosphere reflectance using standard panels on the ground and through the Quick Atmospheric Correction Method (QUAC) and the results were compared. The best models used data obtained with standard panels on the ground and allowed predicting Ψstem values with an R2 of 0.54 and an RMSE of 0.11 MPa as estimated in cross-validation, and the best classification reached an accuracy of 74%. This project aims to develop new methods for precisely monitoring and managing irrigation in vineyards while providing useful information about plant physiology response to deficit irrigation.

目前,通过识别光谱和空间信息,遥感技术已成为更准确地预算供水量的重要解决方案。在美国加利福尼亚州圣华金河谷的一个葡萄园中进行了一项研究,在该葡萄园中安装了一个变速自动灌溉系统,在四个随机重复区(共 48 个实验区)中对葡萄树进行 12 种不同水量的灌溉。这种实验设计的目的是创造葡萄水分状况的可变性,以便为建模目的提供可靠的数据集。在整个生长季节,使用安装在无人飞行器(UAV)上的近红外(NIR)-短波红外(SWIR)高光谱相机(900 至 1700 纳米)收集这些区域内的光谱数据。鉴于该光谱域的高吸水性,该传感器被用于从高光谱图像中的纯葡萄像素评估葡萄茎干水势Ψstem(植物水分状况评估的标准参考值)。从葡萄串闭合到采收,Ψ茎在田间被同步采集,并通过机器学习方法利用遥感近红外-西伯利亚红外数据作为回归和分类模式下的预测因子进行建模(类别包括生理上不同的水分胁迫水平)。使用地面标准面板和快速大气校正法(QUAC)将高光谱图像转换为大气底部反射率,并对结果进行比较。最佳模型使用地面标准面板获得的数据,预测Ψ干值的 R2 为 0.54,交叉验证估计的 RMSE 为 0.11 MPa,最佳分类的准确率达到 74%。该项目旨在开发精确监测和管理葡萄园灌溉的新方法,同时提供植物生理对亏缺灌溉反应的有用信息。
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引用次数: 0
期刊
Precision Agriculture
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