Task Planning on Stochastic Aisle Graphs for Precision Agriculture

This work addresses task planning under uncertainty for precision agriculture applications whereby task costs are uncertain and the gain of completing a task is proportional to resource consumption (such as water consumption in precision irrigation). The goal is to complete all tasks while prioritizing those that are more urgent, and subject to diverse budget thresholds and stochastic costs for tasks. To describe agriculture-related environments that incorporate stochastic costs to complete tasks, a new Stochastic-Vertex-Cost Aisle Graph (SAG) is introduced. Then, a task allocation algorithm, termed Next-Best-Action Planning (NBA-P), is proposed. NBA-P utilizes the underlying structure enabled by SAG, and tackles the task planning problem by simultaneously determining the optimal tasks to perform and an optimal time to exit (i.e. return to a base station), at run-time. The proposed approach is tested with both simulated data and real-world experimental datasets collected in a commercial vineyard, in both single- and multi-robot scenarios. In all cases, NBA-P outperforms other evaluated methods in terms of return per visited vertex, wasted resources resulting from aborted tasks (i.e. when a budget threshold is exceeded), and total visited vertices.Comment: To appear in Robotics and Automation Letter

UAS-based high resolution mapping of evapotranspiration in a Mediterranean tree-grass ecosystem

Este artículo está sujeto a una licencia CC BY 4.0Understanding the impact of land use and land cover change on surface energy and water budgets is increasingly important in the context of climate change research. Eddy covariance (EC) methods are the gold standard for high temporal resolution measurements of water and energy fluxes, but cannot resolve spatial heterogeneity and are limited in scope to the tower footprint (few hundred meter range). Satellite remote sensing methods have excellent coverage, but lack spatial and temporal resolution. Long-range unmanned aerial systems (UAS) can complement these other methods with high spatial resolution over larger areas. Here we use UAS thermography and multispectral data as inputs to two variants of the Two Source Energy Balance Model to accurately map surface energy and water fluxes over a nutrient manipulation experiment in a managed semi-natural oak savanna from peak growing season to senescence. We use energy flux measurements from 6 EC stations to evaluate the performance of our method and achieve good accuracy (RMSD ≈ 60 W m− 2 for latent heat flux). We use the best performing latent heat estimates to produce very high-resolution evapotranspiration (ET) maps, and investigate the drivers of ET change over the transition to the senescence period. We find that nitrogen and nitrogen plus phosphorus treatments lead to significant increases in ET (P < 0.001) for both trees (4 and 6%, respectively) and grass (12 and 9%, respectively) compared to the control. These results highlight that the high sensitivity and spatial and temporal resolution of a UAS system allows the precise estimation of relative water and energy fluxes over heterogeneous vegetation cover.This research was supported by the DAAD/BMBF program Make Our Planet Great Again – German Research Initiative Project MONSOON (grant number 57429870).Peer reviewe

Challenges and best practices for deriving temperature data from an uncalibrated UAV thermal infrared camera

Miniaturized thermal infrared (TIR) cameras that measure surface temperature are increasingly available for use with unmanned aerial vehicles (UAVs). However, deriving accurate temperature data from these cameras is non-trivialsince they are highly sensitive to changes in their internal temperature and low-cost models are often not radiometrically calibrated. We present the results of laboratory and field experiments that tested the extent of the temperature-dependency of a non-radiometric FLIR Vue Pro 640. We found that a simple empirical line calibration using at least three ground calibration points was sufficient to convert camera digital numbers to temperature values for images captured during UAV flight. Although the camera performed well under stable laboratory conditions (accuracy ×0.5 °C), the accuracy declined to ×5 °C under the changing ambient conditions experienced during UAV flight. The poor performance resulted from the non-linear relationship between camera output and sensor temperature, which was affected by wind and temperature-drift during flight. The camera's automated non-uniformity correction (NUC) could not sufficiently correct for these effects. Prominent vignetting was also visible in images captured under both stable and changing ambient conditions. The inconsistencies in camera output over time and across the sensor will affect camera applications based on relative temperature differences as well as user-generated radiometric calibration. Based on our findings, we present a set of best practices for UAV TIR camera sampling to minimize the impacts of the temperature dependency of these system

Digital phenotyping and genotype-to-phenotype (G2P) models to predict complex traits in cereal crops

The revolution in digital phenotyping combined with the new layers of omics and envirotyping tools offers great promise to improve selection and accelerate genetic gains for crop improvement. This chapter examines the latest methods involving digital phenotyping tools to predict complex traits in cereals crops. The chapter has two parts. In the first part, entitled “Digital phenotyping as a tool to support breeding programs”, the secondary phenotypes measured by high-throughput plant phenotyping that are potentially useful for breeding are reviewed. In the second part, “Implementing complex G2P models in breeding programs”, the integration of data from digital phenotyping into genotype to phenotype (G2P) models to improve the prediction of complex traits using genomic information is discussed. The current status of statistical models to incorporate secondary traits in univariate and multivariate models, as well as how to better handle longitudinal (for example light interception, biomass accumulation, canopy height) traits, is reviewe

Carbon exchange in boreal ecosystems: upscaling and the impacts of natural disturbances

Boreal forests and peatlands are globally significant carbon stores but they are threatened by rising air temperatures and changes in precipitation regimes and the frequency of natural disturbances. Predicting how the boreal biome will respond to climate change depends on being able to accurately model and upscale the greenhouse gas fluxes between these ecosystems and the atmosphere. This thesis focuses on developing simple, empirical, remote sensing models of ecosystem respiration (ER) and assessing how ER varies over space and in response to natural disturbances such as drought and wildfire. Paper I and II tested the opportunities and limitations offered by newly available, miniaturized thermal cameras, which can capture images of surface temperature at sub-decimeter resolution. Since temperature is one of the most important factors driving ER, these cameras provide an opportunity to map ER in unprecedented detail. In Paper III, satellite land surface temperature (LST) data was used to model ER across several Nordic peatland sites to examine whether remote sensing models can capture variations in ER between sites. In addition, Paper II and III highlighted the impacts of the extreme 2018 drought that affected large parts of Europe on peatland CO2 fluxes. The drought also led to a severe wildfire season in Sweden and Paper IV investigated how the effects of fire on forest soils depended on burn severity, salvage-logging and stand age.Producing reliable surface temperature measurements from miniaturized thermal cameras requires careful data collection and processing and one of the main outcomes of this thesis is a set of best practices for thermal camera users. Despite including larger uncertainties than traditional soil or air temperature measurements, thermal data from these cameras is suitable for modeling ER in peatland ecosystems. The ER maps that can be produced using UAV thermal cameras offer a unique resource for evaluating how ER varies within a flux tower footprint and could reveal potential biases in flux tower measurements. Indeed, this thesis demonstrated that there is substantial spatial variation in ER, both within and between peatlands. Vegetation composition played a significant role in driving this spatial variation as well as the response of peatlands to drought. Nevertheless, using only LST and the Enhanced Vegetation Index (EVI2) as model inputs captured a large proportion of the variability of daily ER across multiple peatlands. With further developments, such a modeling approach could represent a simple and effective way of estimating peatland ER across Scandinavia. In terms of wildfire impacts on boreal forest soils, stand age had a significant impact on soil respiration, nutrient availability and microclimate, whereas salvage-logging did not, in the first year after fire. Furthermore, the effects of drought and wildfire on ER depended on their severity, but during both extreme water stress in peatlands and after severe burning in forests, ER decreased. It is important that this negative feedback is accounted for in ER models to avoid overestimating carbon loss from northern ecosystems in response to disturbances and climate change

Using a Remotely Piloted Aircraft System to Investigate the Relationship between Canopy Temperature Depression and American Beech Health in Southern Ontario

The unprecedented spread of invasive pest and pathogens, along with climate change and human activity/development is degrading forest system health. American beech trees are a principal tree species that dominates Ontario’s hardwood forests, yet are declining in numbers, primarily due to diseases such as beech bark disease and beech leaf disease, but also because of human development in environmentally sensitive forests. To better monitor American beech (Fagus grandifolia) health and identify severely deteriorated trees, innovative technologies such as Remotely Piloted Aircrafts (RPAs) can be utilized to complement the data collected by mid-altitude aerial aircrafts and ground-based surveys. Existing research has demonstrated the potential for RPA based thermography, which measure individual canopy temperature readings, to identify trees that are under water stress because of factors such as drought and foliar, stem and/or root diseases. However, whether American beech trees displaying noticeable signs in decline in health, due to factors such as foliar, stem and/or root diseases, can be differentiated from trees showing little to no sign in decline is yet to be determined by RPA-borne thermal imaging. This paper investigates whether RPA-borne thermal imaging can be a useful tool to monitor American beech tree health in Southern Ontario forests. The study was located at rare Charitable Research Reserve in Cambridge, Ontario, in semi-naturalized forests. A total of 29 American beech trees across eight different plots were included in the sample for the study and were given a health level of either “healthy”, “fair” or “poor” based on the presence/severity of beech bark disease, severity of bark deterioration and limb loss, and canopy coverage estimated as a percentage based on RPA visual imagery. In August of 2020, thermal imagery was collected on five different days: August 6th, 7th, 15th, 19th, and 26th, and in the following year was collected on three different days: August 2nd, 3rd, and 4th. Canopy temperatures of each individual beech tree was retrieved, normalized based on air temperature (canopy temperature depression) and analyzed to determine whether canopy temperature readings significantly differed based on health level. This study found that increasing American beech tree canopy temperatures were not correlated with deteriorating health. The one-way ANOVA performed for most flights showed that canopy temperature readings did not significantly change based on the recorded tree health level

An assessment of the potential for cloud computing and satellite thermal infrared sensing to produce meaningful river temperature insights for hydropower operations

Hydropower interacts heavily with river temperature to; meet regulations, maximise profits, and maintain dam safety. Often the operational decisions that dictate this interaction are made without monitoring of river temperature, and so it is proposed that satellite remote sensing may provide a quasi-regular cost-effective method to improve this. This dissertation assesses the viability of using Google Earth Engine cloud computing and Landsat 8 Thermal Infrared satellite measurements to provide actionable insights for hydropower managers. The method was tested in three large rivers (the Saint John River in Canada, the Colorado River in the USA, and the Ganges in India) to assess transferability. No previous study has attempted to extract river temperature from multiple sites in a single study. Three different methods were tested to find the most accurate atmospheric correction algorithm for the task of river temperature measurement. The Statistical Mono-Window algorithm was found to produce the most accurate comparison to kinetic temperature loggers on the Saint John River (±2oc) with a R2 value of 0.96 (n=40, p<0.001). However, this method was not transferable to the Colorado River indicating application in rivers without validation data should be carried out with caution. A Python Package named SatTemp (Valman, 2021b) was developed to assist hydropower operators in implementing the method along with a dashboard app to disseminate results (Valman, 2021a). Concerns were raised with the “black box” nature of Google Earth Engine and this App, meaning that errors and nuances in the method may be missed. These would need to be addressed before this method can be provided to hydropower operators