Unmanned aerial vehicles (UAVs) for multi-temporal crop surface modelling. A new method for plant height and biomass estimation based on RGB-imaging

Data collection with unmanned aerial vehicles (UAVs) fills a gap on the observational scale in re-mote sensing by delivering high spatial and temporal resolution data that is required in crop growth monitoring. The latter is part of precision agriculture that facilitates detection and quan-tification of within-field variability to support agricultural management decisions such as effective fertilizer application. Biophysical parameters such as plant height and biomass are monitored to describe crop growth and serve as an indicator for the final crop yield. Multi-temporal crop surface models (CSMs) provide spatial information on plant height and plant growth. This study aims to examine whether (1) UAV-based CSMs are suitable for plant height modelling, (2) the derived plant height can be used for biomass estimation, and (3) the combination of plant height and vegetation indices has an added value for biomass estimation. To achieve these objectives, UAV-flight campaigns were carried out with a red-green-blue (RGB) camera over controlled field experiments on three study sites, two for summer barley in Western Germany and one for rice in Northeast China. High-resolution, multi-temporal CSMs were derived from the images by using computer vision software following the structure from motion (SfM) approach. The results show that plant height and plant growth can be accurately modelled with UAV-based CSMs from RGB imaging. To maximise the CSMs’ quality, accurate flight planning and well-considered data collection is necessary. Furthermore, biomass is successfully estimated from the derived plant height, with the restriction that results are based on a single-year dataset and thus require further validation. Nevertheless, plant height shows robust estimates in comparison with various vegetation indices. As for biomass estimation in early growth stages additional po-tential is found in exploiting visible band vegetation indices from UAV-based red-green-blue (RGB) imaging. However, the results are limited due to the use of uncalibrated images. Combining visible band vegetation indices and plant height does not significantly improve the performance of the biomass models. This study demonstrates that UAV-based RGB imaging delivers valuable data for productive crop monitoring. The demonstrated results for plant height and biomass estimation open new possi-bilities in precision agriculture by capturing in-field variability

Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat

UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R2: 0.75 to 0.84). These results indicate the imaging system’s potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed

Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits-A Case Study for Winter Wheat

UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R-2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R-2: 0.75 to 0.84). These results indicate the imaging system's potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed.Peer reviewe

Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat

UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R2: 0.75 to 0.84). These results indicate the imaging system’s potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed

Aquisição de informações em nível de campo da cana-de-açúcar utilizando dados de um veículo aéreo não tripulado (VANT) sob diferentes metodologias

Orientadores: Rubens Augusto Camargo Lamparelli, Jansle Vieira RochaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia AgrícolaResumo: A aplicação do sensoriamento remoto como ferramenta na agricultura de precisão, tornou-se cada vez mais comum devido à sua capacidade de fornecer informações espacialmente e temporalmente distribuídas. Neste contexto, o sensoriamento remoto de baixa altitude é um conceito relativamente novo para a aquisição de imagens. Sensores colocados em um Veículo Aéreo Não Tripulado (VANT) podem fornecer dados que atendam especialmente os requisitos críticos de resolução espacial e temporal para aplicações agrícolas. Portanto, uma produção agrícola de importância econômica, ambiental e energética, como a cana-de-açúcar, pode se beneficiar de informações fornecidas por esta tecnologia. Assim, o principal objetivo deste trabalho foi explorar as potencialidades e limitações do uso de um VANT no monitoramento da cana-de-açúcar. Através do desenvolvimento de metodologias para utilizar os dados de VANT, foi extraído informações qualitativas e quantitativas e comparamos com referências de campo e de satélite, para verificar a hipótese do estudo. Dessa maneira, a primeira parte desta tese, descreve um processo de análise de imagens orientada a objetos (OBIA) para imagens VANT, projetado para mapear e extrair informações sobre falhas em linhas de plantio de cana-de-açúcar. O método obteve bons resultados com uma relação entre as falhas estimadas e observadas de 97%. A segunda parte descreve a geração de modelos de superfície da cultura (MSC) derivadas das imagens de alta resolução do VANT para a estimativa de altura em canaviais. Além disso, foi investigada a influência de diferentes linhas de voo sobre a estimativa de altura e sua precisão, comparando os mapas gerados com as referências terrestres. Este método mostrou-se ideal para estimar a altura média de um talhão de cana-de-açúcar, em vez de realizar medidas pontuais em campo. Na terceira parte, os dados do VANT (RGB) e os dados de satélite (multiespectral, WorldView-2) foram analisados, a fim de avaliar a capacidade de cada sistema em representar a variabilidade dentro do talhão da produtividade da cana-de-açúcar estimada em campo. Os resultados mostraram que os dados de VANT produziram erros médios semelhantes, mas com poder de explicação inferior em comparação os dados do WorldView-2. Além disso, a incorporação de ambos os dados (WorldView-2 + VANT) melhorou a precisão. Em resumo, foi concluído que um sistema VANT é capaz de fornecer dados úteis de apoio a tomada de decisão para a produção de cana-de-açúcar. Essas plataformas tem a capacidade de fornecer imagens de alta resolução no momento ideal de aquisiçãoAbstract: The application of remote sensing as tool in precision agriculture has become increasingly common due to its ability to provide spatially and temporally distributed information. In this context, low-altitude remote sensing is a relatively new concept for the acquisition of images. Sensors placed on an unmanned aerial vehicle (UAV) can provide data that attend especially to the critical requirements of spatial and temporal resolution for agricultural applications. Therefore, crop production with economic, environmental and energy importance, such as sugarcane, can benefit from the information provided by this technology. Thus, the main objective of this research was exploring the potential and limitations of the use of UAVs in monitoring sugarcane. Through methodological developments to use the UAV data, was extracted qualitative and quantitative data and compared them with field and satellite data to test the study¿s hypothesis. The first part of this thesis describes an object-based image analysis (OBIA) procedure for UAV images, designed to map and extract information about skips in sugarcane planting rows. The approach achieved good results with a relationship of estimated versus observed skip length of 97%. The second part describes the generation of crop surface models (CSMs) derived from high-resolution images from the UAV to estimate the height of sugarcane fields. Also, was investigated the influence of different flight lines on the height estimation and the accuracies by comparing the generated maps with ground references. This method was ideal for estimating the average height of an entire field at once, instead of using point-wise ground measurements. In the third part, the UAV data (RGB) and the orbital platform data (multispectral, WorldView-2) were analyzed, to assess the capability of each system to represent the intra-field variability of sugarcane yield estimates. The results showed that the UAV data produced mean errors similarly, but with lower explanatory power compared to the WorldView-2 data. Moreover, the incorporation of both datasets (WorldView-2 + UAV) improved the accuracy. In summary, was concluded that a UAV system can provide useful decision-support data for improving sugarcane production. These platforms have the capability of providing very-high resolution images with near real-time acquisitionDoutoradoGestão de Sistemas na Agricultura e Desenvolvimento RuralDoutor em Engenharia Agrícola12/50048-7CAPESFAPES

Sugarcane Yield Estimation by UAV Photogrammetry Survey

Sugarcane is a giant tropical grass in the botanical genus, Saccharum, the stalks of which are the world’s primary source of sugar (sucrose). After wheat, sugarcane is the second largest export crop in Australia with a total annual revenue of about $2.5 billion AUD. There is a need for accurate and efficient yield estimation models for sugarcane crops, primarily because most of the cane is forward sold in the months leading up to harvest, and for logistical reasons including equipment allocation and harvest scheduling. Existing methods rely on hyperspectral satellite imagery and grower’s estimates, both of which have some limitations. Unmanned aerial vehicles (UAVs), mounted with a visual spectrum (red-green-blue, i.e. RGB) camera may present an efficient and cost-effective method for capturing spatial and spectral data about sugarcane crops and, if processed and analysed properly, this data could be used to estimate the quantity of usable cane stalks in a canefield. Such a technique would be valuable for the sugarcane industry. In this research project, the existing literature relating to crop height determination by UAV photogrammetry survey, visible-band spectral analysis of vegetation, and sugarcane yield estimation has been reviewed. A methodology was developed and a field study carried out to survey sugarcane crops using a consumer-grade UAV at approximately monthly intervals for three months leading up to harvest, to process the data into 3D digital models using photogrammetry software, to analyse the spatial and spectral properties of the data to find correlations with empirical yield data as recorded during a monitoring survey of the harvest, and to develop yield prediction models using linear regression and multiple linear regression techniques. The results demonstrate that UAV-based photogrammetry is a suitable method to create digital models of the crop’s surface, and that the height of this surface model correlates strongly with empirical yield at all survey epochs. Such a technique is useful for assessing crop variability within fields. Unfortunately, however, mature cane is vulnerable to damage by wind and rain, which can affect its height and subsequently thwart observations about growth rate and yield predictions that are based on height. Visible-band vegetation indices exhibited low or erratic correlations with yield and were subject to influence from many factors including changing ambient light conditions and yellowing of the cane due to frost, thus rendering them an unreliable predictor of yield. The conclusions of this project indicate promising potential for UAV photogrammetry survey in the sugarcane industry, with recommendations for future research to improve the yield prediction models by input of additional independent variables to overcome the obstacles discovered in this project

Terrestrial laser scanning for crop monitoring. Capturing 3D data of plant height for estimating biomass at field scale

Terrestrial laser scanning (TLS) is a young remote sensing method, but the trustworthiness of such measurements offers great potential for accurate surveying. TLS allows non-experts to rapidly acquire 3D data of high density. Generally, this acquisition of accurate geoinformation is increasingly desired in various fields, however this study focuses on the application of TLS for crop monitoring. The increasing cost and efficiency pressure on agriculture induced the emergence of site specific crop management, which requires a comprehensive knowledge about the plant development. An important parameter to evaluate this development or rather the actual plant status is the amount of plant biomass, which is however directly only determinable with destructive sampling. With the aim of avoiding destructive measurements, interest is increasingly directed towards non-contact remote sensing surveys. Nowadays, different approaches address biomass estimations based on other parameters, such as vegetation indices (VIs) from spectral data or plant height. Since the plants are not taken it is feasible to perform several measurements across a field and across the growing season. Hence, the change of spatial and temporal patterns can be monitored. This study applies TLS for objectively measuring and monitoring plant height as estimator for biomass at field scale. Overall 35 TLS campaigns were carried out at three sites over four growing seasons. In each campaign a 3D point cloud, covering the surface of the field, was obtained and interpolated to a crop surface model (CSM). A CSM represents the crop canopy in a very high spatial resolution on a specific date. By subtracting a digital terrain model (DTM) of the bare ground from each CSM, plant heights were calculated pixel-wise. Manual measurements aligned well with the TLS data and demonstrated the main benefit of CSMs: the highly detailed acquisition of the entire crop surface. The plant height data were used to estimate biomass with empirically developed biomass regression models (BRMs). Validation analyses against destructive measurements were carried out to confirm the results. The spatial and temporal transferability of crop-specific BRMs was shown. In one case study, the estimations from plant height and six VIs were compared and the benefit of fusing both parameters was investigated. The analyses were based on the TLS-derived CSMs and spectral data measured with a field spectrometer. The important role of plant height as a robust estimator was shown in contrast to a varying performance of BRMs based on the VIs. A major benefit through the fusion of both parameters in multivariate BRMs could not be concluded in this study. Nevertheless, further research should address this fusion, with regard to the capability of VIs to assess information about the vegetation cover or biochemical and biophysical parameters

The acquisition of Hyperspectral Digital Surface Models of crops from UAV snapshot cameras

This thesis develops a new approach to capture information about agricultural crops by utilizing advances in the field of robotics, sensor technology, computer vision and photogrammetry: Hyperspectral digital surface models (HS DSMs) generated with UAV snapshot cameras are a representation of a surface in 3D space linked with hyperspectral information emitted and reflected by the objects covered by that surface. The overall research aim of this thesis is to evaluate if HS DSMs are suited for supporting a site-specific crop management. Based on six research studies, three research objectives are discussed for this evaluation. Firstly the influences of environmental effects, the sensing system and data processing of the spectral data within HS DSMs are discussed. Secondly, the comparability of HS DSMs to data from other remote sensing methods is investigated and thirdly their potential to support site-specific crop management is evaluated. Most data within this thesis was acquired at a plant experimental-plot experiment in Klein-Altendorf, Germany, with six different barley varieties and two different fertilizer treatments in the growing seasons of 2013 and 2014. In total, 22 measurement campaigns were carried out in the context of this thesis. HS DSMs acquired with the hyperspectral snapshot cameras Cubert UHD 185-Firefly show great potential for practical applications. The combination of UAVs and the UHD allowed data to be captured at a high spatial, spectral and temporal resolution. The spatial resolution allowed detection of small-scale heterogeneities within the plant population. Additionally, with the spectral and 3D information contained in HS DSMs, plant parameters such as chlorophyll, biomass and plant height could be estimated within individual, and across different growing stages. The techniques developed in this thesis therefore offer a significant contribution towards increasing cropping efficiency through the support of site-specific management