26 research outputs found
Wykorzystanie danych termalnych pozyskanych z pułapu lotniczego do określania stanu zdrowotnego wybranych gatunków drzew
Celem pracy było sprawdzenie, czy dane termalne z zakresu średniej podczerwieni
(3,6–4,9 μm) pozyskane z pułapu lotniczego mogą być wykorzystane do badań kondycji
zdrowotnej drzew. W tym celu przeprowadzono trzy analizy na niezależnych zbiorach
danych w różnych środowiskach.
Badania wykonane na danych termalnych pozyskanych w ciągu dnia wykazały,
że temperatura korony jest cechą specyficzną dla gatunku i zależy od położenia drzewa
w terenie. Drzewa znajdujące się wewnątrz lasu miały niższą temperaturę koron do 0,70oC
niż te rosnące poza lasem. Gatunkiem o najwyższej temperaturze, niezależnie od godziny
pozyskania danych lotniczych, był Pinus sylvestris. Niskimi temperaturami
charakteryzowały się Alnus glutinosa, Quercus rubra i Quercus petraea.
Badania nad identyfikacją miejsc żerowania kornika drukarza wykazały, że fuzja
danych termalnych i skanowania laserowego umożliwiły wyznaczenie temperatury koron
pojedynczych drzew Picea abies i sklasyfikowanie ich do trzech klas zdrowotnych (drzewa
'zdrowe' o średniej temperaturze 27,70oC; 'o osłabionej kondycji' 28,57oC i 'martwe'
30,17oC). Opracowany został schemat postępowania wykorzystujący automatyczną
segmentację i uczenie maszynowe do identyfikacji drzew 'o osłabionej kondycji'
i 'martwych'.
Badania przeprowadzone w środowisku miejskim wykazały statystycznie istotne
różnice między klasami kondycji zdrowotnej drzew zarówno na danych pozyskanych
w dzień jak i w nocy. Korony drzew zdrowych były chłodniejsze w porównaniu do koron
drzew zamierających. Średnia wartość różnicy wynosiła 3,28oC w ciągu dnia oraz 1,06oC
w nocy.
Podsumowując, lotnicze dane termalne z zakresu średniej podczerwieni mogą być
wykorzystane do badań kondycji zdrowotnej wybranych gatunków drzew. Zmienność
temperatur koron jest cechą zależną od gatunku i może być wskaźnikiem stanu zdrowotnego
w środowisku naturalnym i miejskim."InterDOC-STARt – Interdyscyplinarne Studia Doktoranckie na Wydziale BiOŚ UŁ” – Program Operacyjny Wiedza Edukacja Rozwój 2014-2020, Oś priorytetowa III. Szkolnictwo wyższe dla gospodarki i rozwoju, Działanie 3.2 Studia doktoranckie. Nr projektu: POWR.03.02.00-IP.08-00-DOK/16. Realizowany w latach 2018-2022
Field phenotyping for African crops: overview and perspectives
Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.BBSRC: BB/P016855/
Remote Thermal Signature Point Target Acquisition System Using Continuous PID Algorithm and Thermal Image Filtration for UAV Systems
The aspects relating to unmanned aircraft and their target acquisition systems are continuously being developed. The subject of the study is a target acquisition system with a thermal camera. A control system is presented consisting of three subsystems. Open- and closed-loop control systems are used. Experimental results unambiguously show that this is a promising line of research and form the basis for further efforts on the topic
Estimation of tomato water status with photochemical reflectance index and machine learning: Assessment from proximal sensors and UAV imagery
Tracking plant water status is a critical step towards the adaptive precision irrigation management of processing tomatoes, one of the most important specialty crops in California. The photochemical reflectance index (PRI) from proximal sensors and the high-resolution unmanned aerial vehicle (UAV) imagery provide an opportunity to monitor the crop water status efficiently. Based on data from an experimental tomato field with intensive aerial and plant-based measurements, we developed random forest machine learning regression models to estimate tomato stem water potential (ψstem), (using observations from proximal sensors and 12-band UAV imagery, respectively, along with weather data. The proximal sensor-based model estimation agreed well with the plant ψstem with R2 of 0.74 and mean absolute error (MAE) of 0.63 bars. The model included PRI, normalized difference vegetation index, vapor pressure deficit, and air temperature and tracked well with the seasonal dynamics of ψstem across different plots. A separate model, built with multiple vegetation indices (VIs) from UAV imagery and weather variables, had an R2 of 0.81 and MAE of 0.67 bars. The plant-level ψstem maps generated from UAV imagery closely represented the water status differences of plots under different irrigation treatments and also tracked well the temporal change among flights. PRI was found to be the most important VI in both the proximal sensor- and the UAV-based models, providing critical information on tomato plant water status. This study demonstrated that machine learning models can accurately estimate the water status by integrating PRI, other VIs, and weather data, and thus facilitate data-driven irrigation management for processing tomatoes
Remote Detection of water and nutritional status of soybeans using UAV-based images.
Digital aerial images obtained by cameras embedded in remotely piloted aircraft (RPA) have been used to detect and monitor abiotic stresses in soybeans, such as water and nutritional deficiencies. This study aimed to evaluate the ability of vegetation indexes (VIs) from RPA images to remotely detect water and nutritional status in two soybean cultivars for nitrogen. The soybean cultivars BONUS and BRS-8980 were evaluated at the phenological stages R5 and R3 (beginning of seed enlargement), respectively. To do so, plants were subjected to two water regimes (100% ETc and 50% ETc) and two nitrogen (N) supplementation levels (with and without). Thirty-five VIs from multispectral aerial images were evaluated and correlated with stomatal conductance (gs) and leaf N content (NF) measurements. Near-infrared (NIR) spectral band, enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), and renormalized difference vegetation index (RDVI) showed linear correlation (p<0.001) with gs, standing out as promising indexes for detection of soybean water status. In turn, simplified canopy chlorophyll content index (SCCCI), red-edge chlorophyll index (RECI), green ratio vegetation index (GRVI), and chlorophyll vegetation index (CVI) were correlated with NF (p<0.001), thus being considered promising for the detection of leaf N content in soybeans
Remote Sensing in Agriculture: State-of-the-Art
The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue
Termografía con aeronaves remotamente pilotadas en palma de aceite (Elaeis guineensis)
En el presente proyecto se establecen condiciones de confiabilidad para la adquisición de medidas térmicas en palma de aceite, Se evaluaron las variables altura, hora del día y condición climática, para determinar lineamientos que permitan maximizar la confiabilidad de la medición de temperatura por medio de sensores soportados en aeronaves remotamente pilotadas para las condiciones de palma de aceite. La temperatura del dosel en palma de aceite a través de la medición de la radiación térmica (rango del espectro electromagnético), es una variable que permite dar alerta de condiciones anormales en la planta, indicadores como déficit hídrico, deficiencia nutricional y problemas fitosanitarios, entre otras. En la actualidad, existe la medición sobre la termometría infrarroja como un método de diagnóstico temprano de palmas afectadas por la Marchitez Letal (ML), pues esta enfermedad es presentada como uno de los problemas fitosanitarios más importantes en el cultivo de palma de aceite en Colombia, donde se obtuvieron resultados de variables que permitieron complementar que la temperatura infrarroja y delta, presenta diferencias significativas entre las palmas sanas y enfermas. Como estudio se utilizó una cámara térmica Flir Duo 2 en tierra, como referencia, la cual ha sido utilizada en proyectos de investigación en palma de aceite, con el fin de evaluar la Cámara térmica FLIR Bosón soportada en un drone Autel Evo II pro, para determinar la precisión de las medidas y consideraciones para el uso de estas tecnologías.In the present project, reliability conditions are established for the acquisition of thermal measurements in oil palm. The variables height, time of day and climatic condition were evaluated, to determine guidelines that allow maximizing the reliability of temperature measurement by means of sensors. supported in remotely piloted aircraft for oil palm conditions. The temperature of the canopy in oil palm through the measurement of thermal radiation (range of the electromagnetic spectrum), is a variable that allows warning of abnormal conditions in the plant, indicators such as water deficit, nutritional deficiency and phytosanitary problems, among others. At present, there is the measurement of infrared thermometry as a method of early diagnosis of palms affected by Lethal Wilt (ML), since this disease is presented as one of the most important phytosanitary problems in oil palm cultivation in Colombia. Where results of variables that allowed to complement that the infrared and delta temperature were obtained, present significant differences between healthy and diseased palms. As a study, a ground-based Flir Duo 2 thermal camera was used, as a reference, which has been used in oil palm research projects, in order to evaluate the FLIR Boson thermal camera supported on an Autel Evo II pro drone, to determine the precision of the measurements and considerations for the use of these technologies
Mapping evapotranspiration to disaggregate eddy-covariance footprints
Land-atmosphere interactions are commonly quantified using eddy-covariance (EC) equipment. This technique provides fluxes which are attributed to an area-averaged two-dimensional flux footprint. Although source flux heterogeneity is present within these footprints, current EC footprint models are unable to distinguish the variable flux contributions (although they are incorporated into the bulk EC flux). A disaggregation method would increase the value of EC data by allowing users to isolate individual fluxes from features within the flux footprint; furthermore, this may extend the useability of the EC method to more complex terrain of mixed land classifications. It remains unexplored how high-resolution surface energy balance (SEB) models can be used to disaggregate these EC footprints. This thesis presents a SEB workflow using Unoccupied Aerial Vehicles (UAVs) to generate high-resolution patterns of evapotranspiration (ET) to disaggregate EC footprints in a novel disaggregated flux footprint prediction (disFFP) method.
This workflow begins by using novel UAV Light Ranging And Detection (LiDAR) techniques to derive detailed maps of canopy height (h), effective leaf area index (LAI_e), and canopy viewing fraction (f_c), consistent with known vegetation patterns and field-average observations (LAI_e RMSE=0.08-0.81 m^2 m^(-2)). It then follows an atmospheric correction (path transmissivity and upward-welling radiance) and an ensemble emissivity adjustment (brightness to radiometric temperature), testing these with UAV thermal data to determine their absolute (magnitude) and relative (spatial pattern) effects on temperature. A modified t-test - considering autocorrelation - showed that a raw brightness temperature has similar spatial patterns (r>.99, p_val≫0.001) to brightness and radiometric temperatures. Combining these thermal and LiDAR UAV inputs with a high-resolution SEB model (HRMET), the model performance was then compared against EC fluxes. It followed that HRMET tended to overestimate latent heat over full canopies when surface-air temperature differences exceed 4-5°C. Overall, HRMET succeeded at replicating EC latent heat flux within RMSE of 79-136 W m^(-2) using raw brightness and ensemble emissivity corrected (observed radiometric) temperatures. The resultant relative ET maps (ET_R) provided a coherent chronology of the changing flux landscape. Furthermore, the ET_R trials using corrected temperatures (brightness, radiometric, and observed radiometric) had similar spatial patterns to those found using just raw brightness temperature (r>.93, p_val≫0.001). This implies that a raw brightness temperature is sufficient for determining relative patterns of evapotranspiration.
The next part of the workflow uses ET_R to disaggregate a well-known parameterization of a backwards-Lagrangian flux footprint model. The proposed disaggregation method (disFFP) uses the concept of ET period to describe an interval of time (day scale) where the EC flux environment remains relatively constant (constant-rate cumulative ET rate). ET periods were determined using a piece-wise regression of the cumulative day-over-day EC latent flux rate. Each season was divided into five ET periods and compared with the two other seasons to discover potential metrics for further classifying ET periods. It was found that ET rates were consistent across comparable ET periods, 2-38 % (standard deviation as a percentage of sample mean), and that additional metrics (plant phenology, growing degree day, standard precipitation index) played an important role in characterizing ET periods for inter-seasonal work. Eddy-covariance and UAV data were coupled using climatology footprints of ET periods and the coinciding ET patterns (ET_R and coefficient of variation). The disFFP combination of these two products (footprint-weighted factoring) provided disaggregated footprints (EC bulk ET of 144 mm) that reflected increased contributions (180-200 mm) over high ET areas and diminished values (90-100 mm) over lower ET areas. These preliminary findings present an exciting new opportunity to connect discrete UAV data with continuous EC flux monitoring
Advances in Evaporation and Evaporative Demand
The importance of evapotranspiration is well-established in different disciplines such as hydrology, agronomy, climatology, and other geosciences. Reliable estimates of evapotranspiration are also vital to develop criteria for in-season irrigation management, water resource allocation, long-term estimates of water supply, demand and use, design and management of water resources infrastructure, and evaluation of the effect of land use and management changes on the water balance. The objective of this Special Issue is to define and discuss several ET terms, including potential, reference, and actual (crop) ET, and present a wide spectrum of innovative research papers and case studies
Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum
Screening for drought tolerance is critical to ensure high biomass production of bioenergy sorghum in arid or semi-arid environments. The bottleneck in drought tolerance selection is the challenge of accurately predicting biomass for a large number of genotypes. Although biomass prediction by low-altitude remote sensing has been widely investigated on various crops, the performance of the predictions are not consistent, especially when applied in a breeding context with hundreds of genotypes. In some cases, biomass prediction of a large group of genotypes benefited from multimodal remote sensing data; while in other cases, the benefits were not obvious. In this study, we evaluated the performance of single and multimodal data (thermal, RGB, and multispectral) derived from an unmanned aerial vehicle (UAV) for biomass prediction for drought tolerance assessments within a context of bioenergy sorghum breeding. The biomass of 360 sorghum genotypes grown under well-watered and water-stressed regimes was predicted with a series of UAV-derived canopy features, including canopy structure, spectral reflectance, and thermal radiation features. Biomass predictions using canopy features derived from the multimodal data showed comparable performance with the best results obtained with the single modal data with coefficients of determination (R2) ranging from 0.40 to 0.53 under water-stressed environment and 0.11 to 0.35 under well-watered environment. The significance in biomass prediction was highest with multispectral followed by RGB and lowest with the thermal sensor. Finally, two well-recognized yield-based drought tolerance indices were calculated from ground truth biomass data and UAV predicted biomass, respectively. Results showed that the geometric mean productivity index outperformed the yield stability index in terms of the potential for reliable predictions by the remotely sensed data. Collectively, this study demonstrated a promising strategy for the use of different UAV-based imaging sensors to quantify yield-based drought tolerance