Integration of Multiscale Sensing Data for Phenomics Applications

Sensing technologies can be a powerful tool for phenotyping in breeding programs. Plant phenotypes can be assessed non-invasively and repeatedly across the whole population and throughout the plant development period utilizing advanced sensors and remote sensing platforms. In this study, multiscale sensing platforms—satellite, unmanned aerial vehicle (UAV), proximal sensing system, and Internet of Things (IoT) based sensing systems—equipped with sensors such as visible/RGB, multispectral, and hyperspectral systems were utilized for field-based phenomics applications. The applicability of a suitable sensing technology depends on the area of study, specific phenomics application, sensor specification, and data acquisition conditions. Three main phenomics applications were explored: (i) pasture crop health status evaluation, (ii) above-ground biomass quantity and quality evaluation in the field pea, and (iii) evaluating wheat yield potential in winter and spring wheat. The first study demonstrates the reliability of using a high-resolution satellite (ground sampling distance, GSD = 3 m) and UAV imagery for pasture management. The data from multiscale sensing data showed that the grazing density significantly affected pasture biomass (p < 0.05) only in 2019, and the vegetation index (VI) data from the two imagery types were highly correlated (r ≥ 0.78, p < 0.001, 2019). In the second study, the above-ground biomass (AGBM) and biomass quality (12 quality traits) were evaluated using UAV-based RGB and multispectral imaging, and hyperspectral sensing, respectively, in the winter pea breeding program (2019 and 2020 seasons). Three image processing approaches were evaluated for AGBM estimation, where the best results were acquired using the 3D point cloud model at 1.5 alpha shape technique showing high correlation with harvested fresh (r = 0.78–0.81, p < 0.001) and dry (r = 0.70–0.81, p < 0.001) AGBM. Similarly, the selected features from the normalized difference spectral indices and the ratio spectral indices extracted from hyperspectral data with the random forest model provided high predictive accuracy for all 12 biomass quality traits (0.81 < R2 < 0. 93; 0.05 < RMSE (%) < 1.80; 0.03 < MAE (%) < 1.32).In the wheat study, the vegetation indies were highly correlated between satellite (GSD = 0.31 m) and UAV data (0.42 ≤ r ≤ 0.99, p < 0.01) from winter and spring wheat breeding trials (2020 and 2021). The yield prediction using such VIs with the high-resolution satellite imagery (6.26 ≤ RMSE% ≤ 25.49; 5.11 ≤ MAE% ≤ 20.95; 0.17 ≤ r ≤0.78) and UAV imagery (5.53 ≤ RMSE% ≤ 17.20; 4.28 ≤ MAE% ≤ 14.20; 0.43 ≤ r ≤ 0.92) was also high. In addition to these two platforms, an intelligent and compact IoT-based sensor system was developed for independent and automated phenomics applications to measure and monitor plant responses in real-time. The sensor development, improvisation, and implementation encompassed three field seasons (2020, 2021, and 2022 seasons). The developed IoT-based sensor system could be successfully implemented to monitor multiple trials for timely crop management and increased resource efficiency. The system shows a high potential for supporting plant breeding programs for in-field phenotyping applications. All studies demonstrated promising results in monitoring and estimating crop performance and phenotypic traits using multiscale sensing systems

Stability and Toxicity of Cerium Oxide Nanoparticles in Water-Soil-Plant Systems

Cerium oxide nanoparticles (CeO2 NPs) are being increasingly used in industrial and consumer products and the release of CeO2 NPs into the air, water, and soil is an inevitable consequence. Once released into the environment, CeO2 NPs can have adverse impacts on the environment and human health. Thus, characterizing the environmental behavior and toxic effects of CeO2 NPs is important to protect the environment and human health. To assure such protection, the stability and phytotoxicity of CeO2 NPs need to be known because they are key to understanding their transport, toxicity and bioavailability in water and soil. The objective of this dissertation is to characterize the stability and phytotoxicity of CeO2 NPs in water-soil-plant systems. The approach was to analyze the colloidal stability and aggregation kinetics of CeO2 NPs under the influence of pH, ionic composition (monovalent NaCl and divalent CaCl2 salts), and Suwannee river humic and fulvic acids. The root system architecture and plant growth of seedlings of three different sorghum cultivars (BTx 623, Grassl, and Rio) as well as composition traits, plant growth, and Ce accumulation of sweet sorghum Grassl, were studied to evaluate the phytotoxicity of CeO2 NPs to plants. To analyze the stability of CeO2 NPs, the hydrodynamic diameter and zeta potential were measured over time at three different electrostatic scenarios related to pHPZC (pH \u3e pHPZC, pH = pHPZC, and pH \u3c pHPZC) in the first study and at different concentrations of NaCl and CaCl2 and different concentrations of humic acid (HA) and fulvic acid (FA) in the second study. To quantify the phytotoxicity of CeO2 NPs to sorghum, root system architecture, seedling size, and biomass of three sorghum cultivars (BTx 623, Grassl, and Rio) at four CeO2 NP treatment levels (0, 100, 500, and 1000 mg/kg CeO2 NPs) were determined in the third study. In addition, composition traits, Ce accumulation, plant size, and biomass of sweet sorghum Grassl at four CeO2 NP treatment levels (0, 100, 500, and 1000 mg/kg CeO2 NPs) were determined in the fourth study. Results of the first study show that (1) the zeta potential of CeO2 NPs, with a point of zero charge (pHPZC) of 10.2, decreased (from positive to negative) with increasing solution pH; and (2) the impacts of Na+ and Ca2+ cations and HA and FA on the levels and rates of aggregation were pH-dependent. Furthermore, in the presence of salts, CeO2 NPs were stable at pH \u3c pHPZC (except 1 mM of NaCl/CaCl2) and pH \u3e pHPZC (except 0.5 mM CaCl2), but aggregation was enhanced at pH = pHPZC, with the diameter of CeO2 NPs in the 1300 to 3600 nm range. The study also showed that (3) HA stabilized CeO2 NPs under pH \u3e pHPZC, but aggregation was enhanced at pH = pHPZC with the diameter of CeO2 NPs in the 1500 to 1900 nm range (in the presence of 0 and 1 mM of NaCl/CaCl2 at pH \u3c pHPZC); and (4) FA (0.14 mg/L) showed more efficiency in stabilizing the CeO2 NPs than HA (5 mg/L) at three pH levels (8.2, 10.2, and 12.2) and under all different electrolyte concentrations (0 – 1 mM of NaCl or CaCl2). Results of the second study show that (1) homoaggregation of CeO2 NPs occured in the presence of Na+ (\u3e 1 mM NaCl) and in the presence of Ca2+ (\u3e 5 mM CaCl2); (2) the critical coagulation concentration (CCC) of CeO2 NPs in the presence of monovalent Na+ (30 mM) was twice as large as the CCC in the presence of the divalent Ca2+ (15 mM) at pH 5; (3) the influence of the divalent cation Ca2+ was more efficient than the monovalent cation Na+ in enhancing the aggregation of CeO2 NPs; (4) heteroaggregation of CeO2 NPs and HA was enhanced at higher NaCl concentrations (\u3e 100 mM NaCl) due to electrostatic attraction and at higher CaCl2 concentrations (\u3e 1 mM CaCl2) due to the bridging effect; and (5) when compared to FA, HA was not only more reactive in inhibiting the heteroaggregation of CeO2 NPs in the presence of NaCl, but also more efficient in enhancing the heteroaggregation of CeO2 NPs in the presence of CaCl2 (\u3e 10 mM CaCl2). Results of the third study show that (1) when the CeO2 NP treatment levels increased, a threshold of 500 mg/kg CeO2 NPs and a decreasing trend were found in values of primary root length, primary root surface area, number of lateral roots, surface area/volume ratios of lateral root, total root length, and total root surface area in BTx 623 and primary root length, surface area/volume ratios of lateral root, and total leaves weight in Grassl; and (2) the parameters, significantly greater at 100 mg/kg CeO2 NPs than the control, were a ratio of wet root weight/total wet weight in BTx 623; total wet root weight, total wet weight, and number of lateral roots in Grassl; and top leaf length and water content in Rio. Results also show that (3) a dose-response phenomenon—low CeO2 NP treatment level (100 mg/kg CeO2 NPs) stimulations and higher CeO2 NP treatment level (500 and 1000 mg/kg CeO2 NPs) reductions—was observed for number of lateral roots, total wet root weight, and total wet weight; and (4) Rio was more CeO2 NP tolerant, and BTx 623 and Grassl were more CeO2 NP sensitive at 500 mg/kg CeO2 NPs threshold. Results of the fourth study show that (1) the growth parameters were inhibited at 1000 mg/kg CeO2 NP treatment; (2) Ce accumulation was promoted at 1000 mg/kg CeO2 NPs in Noncut; (3) CeO2 NPs, at some level, can improve the forage quality of sweet sorghum Grassl in terms of digestibility, energy, and minerals; Furthermore, the study shows that (4) the growth stage and cultivation period also showed positive impacts on the bioenergy quality of sorghum in terms of starch and sugar

Tree Structure Retrieval for Apple Trees from 3D Pointcloud

3D reconstruction is a challenging problem and has been an important research topic in the areas of remote sensing and computer vision for many years. Existing 3D reconstruction approaches are not suitable for orchard applications due to complicated tree structures. Current tree reconstruction has included models specific to trees of a certain density, but the impact of varying Leaf Area Index(LAI) on model performance has not been studied. To better manage an apple orchard, this thesis proposes methods for evaluating an apple canopy density mapping system as an input for a variable-rate sprayer for both trellis-structured (2D) and standalone (3D) apple orchards using a 2D LiDAR (Light Detection and Ranging). The canopy density mapping system has been validated for robustness and repeatability with multiple scans. The consistency of the whole row during multiple passes has a correlation R^2 = 0.97. The proposed system will help the decision-making in a variable-rate sprayer. To further study the individual tree structure, this thesis proposes a novel and fast approach to reconstruct and analyse 3D trees over a range of Leaf Area Index (LAI) values from LiDAR for morphology analysis for height, branch length and angles of real and simulated apple trees. After using Principal Component Analysis (PCA) to extract the trunk points, an improved Mean Shift algorithm is introduced as Adapted Mean Shift (AMS) to classify different branch clusters and extract the branch nodes. A full evaluation workflow of tree parameters including trunk and branches is introduced for morphology analysis to investigate the accuracy of the approach over different LAI values. Tree height, branch length, and branch angles were analysed and compared to the ground truth for trees with a range of LAI values. When the LAI is smaller than 0.1, the accuracy for height and length is greater than 90\% and the accuracy for the angles is around 80\%. When the LAI is greater than 0.1, the branch accuracy reduces to 40\%. This analysis of tree reconstruction performance concerning LAI values, as well as the combination of efficient and accurate structure reconstruction, opens the possibility of improving orchard management and botanical studies on a large scale. To improve the accuracy of traditional tree structure analysis, a deep learning approach is introduced to pre-process and classify unbalanced, in-homogeneous, and noisy point cloud data. TreeNet is inspired by 3D U-Net, adding classes and median filters to segment trunk, branch, and leave parts. TreeNet outperformed 3D U-Net and SVM in the case of Kappa, Matthews Correlation Coefficient(MCC), and F1-score value in segmentation. The TreeNet-AMS combined method also showed improvement in tree structure analysis than the traditional AMS method mentioned above. Following on from this research, efficient tree structure analysis on tree height, trunk length, branch position, and branch length could be conducted. Knowing the tree morphology is proved to be closely relevant to thinning, spraying and yield, the proposed work will then largely benefit the relevant studies in agriculture and forestry

Feature Papers of Drones - Volume II

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 24–41 are focused on drone applications, but emphasize two types: firstly, those related to agriculture and forestry (articles 24–35) where the number of applications of drones dominates all other possible applications. These articles review the latest research and future directions for precision agriculture, vegetation monitoring, change monitoring, forestry management, and forest fires. Secondly, articles 36–41 addresses the water and marine application of drones for ecological and conservation-related applications with emphasis on the monitoring of water resources and habitat monitoring. Finally, articles 42–54 looks at just a few of the huge variety of potential applications of civil drones from different points of view, including the following: the social acceptance of drone operations in urban areas or their influential factors; 3D reconstruction applications; sensor technologies to either improve the performance of existing applications or to open up new working areas; and machine and deep learning development

Assessing water stress of desert vegetation using remote sensing : the case of the Tamarugo forest in the Atacama Desert (Northern Chile)

Water stress assessment of natural vegetation plays a key role in water management of desert ecosystems. It allows scientists and managers to relate water extraction rates to changes in vegetation water condition, and consequently to define safe water extraction rates for maintaining a healthy ecosystem. Previous research has shown that optical remote sensing constitutes a powerful tool for assessing vegetation water stress due to its capability of quantitatively estimating important parameters of vegetation such as leaf area index (LAI), green canopy fraction (GCF), and canopy water content (CWC). However, the estimation of these parameters using remote sensing can be challenging in the case of desert vegetation. Desert plants have to cope with high solar irradiation and limited water. In order to maintain an adequate water balance and to avoid photoinhibition, desert plants have evolved different adaptations. A common one is heliotropism or ‘solar tracking’, an ability of many desert species to move their leaves to avoid facing direct high solar irradiation levels during the day and season. This adaptation (paraheliotropism) can have an important effect on the canopy spectral reflectance measured by satellites as well as on vegetation indices such as the normalized difference vegetation index (NDVI). In this thesis, I propose a remote sensing based approach to assess water stress of desert vegetation, exemplified in the case of the Tamarugo (Prosopis tamarugo Phil) tree in the Atacama Desert (Northern Chile), a ‘solar tracker’ species, which is threatened by groundwater overexploitation. In the first chapter of this thesis (general introduction), I explained the motivation of the PhD project and elaborated four research questions, which are later discussed in chapters 2, 3, 4, and 5. The thesis concluded with chapter 6, where I provide a synthesis of the main results, general conclusions and a final reflection and outlook. In the second chapter, I studied the effects of water stress on Tamarugo plants under laboratory conditions and modelled the light-canopy interaction using the Soil-Leaf-Canopy radiative transfer model. I described for the first time pulvinar movement of Tamarugo and quantified its effects on canopy spectral reflectance with and without stress. I showed that different spectral indices have potential to assess water stress of Tamarugo by means of LAI and CWC. In the third chapter, I measured the effects of pulvinar movements on canopy reflectance for Tamarugos under field conditions and used high spatial resolution images to assess water stress at the tree level. I developed an automated process to first identify single trees and delineate their crowns, and secondly, to estimate LAI and GCF using spectral vegetation indices. These indices (NDVI and chlorophyll red-edge index) were negatively correlated to diurnal values of solar irradiation as a consequence of leaf pulvinar movements. For this reason, higher values of both vegetation indices are expected to occur in the morning and in winter (low solar radiation) than at midday or summer. In the fourth chapter I studied the effects of diurnal pulvinar movements on NDVI time series from the MODIS-Terra satellite (acquired in the morning) and the MODIS-Aqua satellite (acquired at midday) for the period 2003-2012 and the seasonal effects of pulvinar movements on NDVI time series of Landsat images for the period 1998-2012 for Tamarugo areas with and without water stress. NDVI values measured by MODIS-Terra (morning) were higher than the NDVI values measured by MODIS-Aqua (afternoon) and the difference between the two, the ΔNDVImo-mi, showed good potential as water stress indicator. In a similar way, I observed a strong seasonal effect on the Landsat NDVI signal, attributed to pulvinar movements, and the difference between winter and summer, the ΔNDVIW-S, also showed good potential for detecting and quantifying water stress. The ΔNDVImo-mi, the ΔNDVIW-S and the NDVI itself measured systematically in winter time (NDVIW) were negatively correlated with in situ groundwater depth measurements. In chapter five I used a dense NDVI time series of Landsat images for the period 1989-2013, combined with high spatial resolution satellite imagery and hydrogeological records, to provide a quantitative assessment of the water status of Tamarugo vegetation after 50 years of increasing groundwater extraction. The results showed that the NDVIW and ΔNDVIW-S of the Tamarugo vegetation declined 19% and 51%, respectively, as groundwater depleted (3 meters on average) for the period 1989-2013. Both variables were negatively correlated to groundwater depth both temporally and spatially. About 730.000 Tamarugo trees remained in the study area by 2011, from which 5.2% showed a GCF The main conclusions of this PhD thesis are summarized as follows: Heliotropism or leaf ‘solar tracking’, a common adaptation among desert plants, has an important impact on canopy spectral reflectance. As shown in the case of the Tamarugo trees, widely used vegetation indices such as the NDVI were negatively correlated to solar irradiation (the stimulus for leaf solar tracking), showing a distinct diurnal and seasonal cycle.An early symptom of water stress in paraheliotropic plants (leaves facing away the sun) is the decline of the amplitude of the diurnal and seasonal NDVI cycles. Thus, remote sensing estimations of this amplitude (e.g. the NDVI difference between winter and summer or the difference between midday and morning) can be used to detect and map early water stress of paraheliotropic vegetation.At the tree level, very high spatial resolution images combined with object based image analysis and in-situ data provided accurate estimations of the water status of small desert vegetation features, such as isolated trees. For monitoring purposes, careful consideration of the time during the day and the season at which the images are taken needs to be taken to avoid misleading interpretations.Time series analysis of historical satellite images combined with very high spatial resolution images and hydrogeological records can provide a quantitative spatio-temporal assessment of the effects of long-term groundwater extraction on desert vegetation

Ecological restoration of lead/zinc/copper mine tailings: Phytomanagement and amendment strategies to enhance substrate functionality and biomass production

“The extreme physiochemical characteristics of mine tailings inhibit microbial processes and natural plant growth. Consequently, vast and numerous tailings sites remain barren for decades and highly susceptible to wind and water erosion. Phytostabilization is a cost-effective and ecologically productive remediation approach; however, tailings revegetation is generally challenging and must often be assisted with appropriate soil amendments. Amendments applied individually in greenhouse studies discussed herein revealed notable improvement in bioenergy crops growth only with biosolids treatments. Recalcitrant carbon amendments (biochar and humus) showed notable impact only on tailings physichochemical and hydraulic properties. Nevertheless, biosolids may not support sustained vegetation due to their nutrient lability and rapid decomposition. Therefore, strategies to sustain phytostabilization were evaluated by co-applying biosolids with recalcitrant carbon or biological amendments to synergistically ameliorate tailings characteristics while supporting sustainable growth to stimulate soil formation. Co-applying with biochar exhibited efficient nutrient release while concurrently reducing metal availability and uptake. Co-applying with mycorrhizal fungi further improved biomass production, increased organic matter input, and reduced metal bioavailability and uptake. To non-destructively assess plant health, a rapid screening approach was also developed utilizing computer vision and imaging techniques. A wide range of native species was also screened for potential to revegetate mine tailings for greater ecosystem benefit and utilizing the developed approach greatly facilitated quantification of plant responses to phytomanagement strategies for mine-impacted sites”--Abstract, page iv