Hyperspectral Remote Sensing of Crop Canopy Chlorophyll and Nitrogen: The Relative Importance of Growth Stages

Remote sensing plays an important role in monitoring vegetation dynamics, and has been recognized as a reliable tool for monitoring biochemical and biophysical variations of agricultural crops, such as plant biomass, height, chlorophyll (Chl) and nitrogen (N). Nitrogen is one of the most essential elements in agro-ecosystems because of its direct role in determining crop yield and vegetation productivity, as well as its association with global N and carbon cycles. Canopy remote sensing of plant biochemical (e.g., N) and biophysical parameters (e.g., biomass) is often discussed separately. However, crop canopy structural characteristics and plant morphophysiological variations at different growth stages cause a confounding effect on the analysis and interpretation of the canopy spectral data. This study aimed to (1) understand the underlying mechanisms of canopy structural dynamics (mainly plant biomass and green leaf area) that impact the retrieval of canopy Chl and N at different growth stages, and (2) develop new algorithms and narrow band vegetation indices that may improve the estimation of Chl and N using hyperspectral data collected in the field and simulated by radiative transfer models (RTMs). To achieve the objectives, barley and rice experiments were conducted in Germany and China, respectively, from experimental plots to farmer fields; both empirical and physical models were employed but with an emphasis on the empirical methods. Results suggest that canopy hyperspectral data allow for the estimation of canopy Chl and N. However, with the advance of growth stages, plant growth rate is much faster than the rate at which N is accumulated in the plant mass until the stage of full heading (canopy closure), which results in a decrease of N concentration — the N dilution effect. Thus, growth stages have a significant effect on the correlation between the optical and biological traits of the crop canopy compared to the differences in crop cultivars and types. This effect is confirmed by five years of experimental data of barley and rice crops. Accordingly, empirical models based on different vegetation indices can be calibrated, before and after the canopy closure, which allows for the monitoring of canopy Chl and N status through the entire growing season. This study also suggests that multivariate models such as partial least squares (PLS) and support vector machines (SVM) are relatively resistant to the influence of growth stages and can be used to improve the estimation of canopy Chl and N compared to univariate models based on vegetation indices. To devise a simple approach for the estimation of canopy Chl and N status that is relatively insensitive to the confounding effect of canopy structural characteristics, new vegetation indices, the Ratio of Reflectance Difference Indices (RRDIs), were developed based on the multiple scatter correction (MSC) theory. This type of indices conceptually eliminates the linear influence caused by the confounding effect of multiple scattering and soil background as well as their interactions; therefore, RRDI weakens the effect of canopy structural variations on the analysis of canopy spectra when estimating biochemical variations. For example, the RRDI derived from the red edge (RRDIre) wavelengths proved to be a robust indicator of canopy Chl and N in both barley and rice crops with different cultivars and for the simulated data by RTMs. Therefore, the method is useful for improving the estimation of canopy biochemical parameters. This study improves the understanding of remote estimation of canopy Chl and N status by considering the dynamical co-variations between plant biomass and N across different growth stages and suggests the potential to improve the ability of canopy hyperspectral data to monitor the canopy biogeochemical cycles of agro-ecosystems using remote sensing. Additionally, this study indicates that hyperspectral vegetation indices based on water absorption bands are useful for the detection of crop diseases at the canopy level

Effects of fungicides on physiological parameters and yield formation of wheat assessed by non-invasive sensors

Apart from fungicidal effects, some fungicide classes have been reported to induce physiological changes in crops such as increased tolerance to abiotic stress, delayed senescence of the photosynthetic leaf area and modifications in the balance of plant growth regulators. The aim of this study was to investigate the effects of different fungicidal groups on physiological parameters of wheat through the use of non-invasive sensors and imaging techniques. Experiments were conducted under field and also under disease-free conditions in the greenhouse. Under field conditions, application of the azole, carboxamide and strobilurin compounds resulted in low disease incidence. All fungicide treatments delayed the senescence of the uppermost leaf layers; treatments with longer leaf life and lower disease incidence resulted in higher chlorophyll content. The effect of the fungicides on wheat senescence was positively correlated to grain yield and the thousand-kernel weight. However, under field conditions, the presence of the main foliar pathogens of wheat influenced the green leaf area duration as well as the yield, generating a disadvantage for the fungicide treatments with low disease control efficacy. Under disease-free conditions, an effect produced by the pyrazole carboxamide fungicide bixafen was observed. Bixafen delayed the senescence of leaves and ears resulting in a significantly extended green leaf area duration compared to untreated plants. In addition, an effect produced by this compound on morphogenesis was observed. The combination of the positive effects on physiology and morphogenesis of wheat resulted in a yield advantage of bixafen-treated plants. Furthermore, bixafen had a positive effect on plant tolerance to water stress conditions. Different non-invasive sensors and imaging techniques were used and compared to measure the effects of fungicidal compounds on wheat physiology. By using ground-based optical sensors it was possible to detect the influence of fungicidal compounds in crop physiology, i.e. degradation of photosynthetic pigments, photosynthetic activity, leaf reflectance, and transpiration of plant tissue earlier than with destructive and visual methods. Chlorophyll fluorescence of leaves was useful to measure differences in the effective quantum yield of photosystem II. Reflectance measurements of wheat leaves were highly sensitive to changes in plant vitality. The spectral vegetation indices were useful to determine differences between treatments in terms of leaf senescence, pigments and water content. Digital infrared images revealed significant differences between untreated and fungicide-treated plants at different growth stages. Moreover, thermography proved to be a suitable technique for distinguishing the beneficial effects of fungicides on plant senescence under different water supply conditions. Through the use of an image analysis software program, leaf senescence differences were successfully detected, thus allowing an early detection of the effect produced by the fungicide on the senescence status of flag leaves. Using hyperspectral imaging, it was possible to study differences in the senescence status of flag leaves. Furthermore, through the analysis of hyperspectral images it was achievable to study the pattern of the senescence process in flag leaves and to determine a delay of senescence of wheat produced by fungicides. The results of this study demonstrated that non-invasive sensors and imaging techniques are excellent alternatives to conventional screening methods for detecting the beneficial effects of fungicides on plant physiology. Furthermore, among this innovative group of sensors and techniques it was spectrometry, which proved to be the most sensitive and specific method with a high potential for large-scale fungicide screening. Sensors can be incorporated in automatic and reproducible screening of new active ingredients with high efficiency and accuracy. The recent development of hyperspectral imaging techniques will improve future studies to additionally explore plant physiology with high spatial and temporal resolution

Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery

This paper presents a method for mapping the nitrogen (N) status in a maize field using hyperspectral remote sensing imagery. An airborne survey was conducted with an AISA Eagle hyperspectral sensor over an experimental farm where maize (Zea mays L.) was grown with two N fertilization levels (0 and 100 kg N ha-1) in four replicates. Leaf and canopy field data were collected during the flight. The nitrogen (N) status has been estimated in this work based on the Nitrogen Nutrition Index (NNI) defined as the ratio between the leaf actual N concentration (%Na) of the crop and the minimum N content required for the maximum biomass production (critical N concentration (%Nc)) calculated through the dry mass at the time of the flight (Wflight). The inputs required to calculate the NNI (i.e. %Na and Wflight) have been estimated through regression analyses between field data and remotely sensed vegetation indices. MCARI/MTVI2 (Modified Chlorophyll Absorption Ratio Index / Modified Triangular Vegetation Index 2) showed the best performances in estimating the %Na (R2 = 0.59) and MTVI2 in estimating the Wflight (R2 = 0.80). The %Na and the Wflight were then mapped and used to compute the NNI map over the entire field. The NNI map agreed with the NNI estimated using field data through traditional destructive measurements (R2 = 0.70) confirming the potential of using remotely sensed indices to assess the crop N condition. Finally, a method to derive a pixel based variable rate N fertilization map was proposed as the difference between the actual N content and the optimal N content. We think that the proposed operational methodology is promising for precision farming since it represents an innovative attempt to derive from an aerial hyperspectral image a variable rate N fertilization map based on the actual crop N status.JRC.H.4-Monitoring Agricultural Resource

Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery

JRC.H.4-Monitoring Agricultural Resource

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Application of proximal sensing techniques for epidemiological investigations of <em>Fusarium </em>head blight in wheat under field and controlled conditions

Sensors can provide valuable insight into studying the physiological disorder due to plant pathogens. Fusarium head blight (FHB) influences the optical properties of wheat (Triticum aestivum L.) at canopy and ear levels. This research aimed to investigate these complex disease situations under field as well as controlled conditions with the application of proximal sensing systems. Observations under field conditions revealed that the presence of foliar diseases is associated with higher Fusarium infection in the wheat canopy (cv. Tobak and Pamier), which might be attributed to reduced defence mechanisms. This was reflected in increased FHB incidence visually assessed at growth stage (GS) 83. Fungicides applied against foliar diseases before anthesis reduced FHB presence, which might be not only due to reducing the available inoculum in the canopy but also due to promoting defenses against Fusarium infection. Furthermore, prediction of FHB through spectral parameters such as blue-green index 2 (BGI2) and photochemical reflection index (PRI) proved to be very promising. At ear level, development of Fusarium infection is dependent on the primary infection site within ears and the prevailing environmental conditions after infection. Such a relationship was verified under controlled conditions after tip, centre and base inoculations, separately, by F. graminearum and F. culmorum of wheat ears (cv. Passat). Symptom dynamics (FHB index) were slower downwards within ears in comparison to the upward development. In contrast to the symptom appearance the infection of Fusarium species proved to be directed basipetally – a rare development of fungal infections. According to these observations it could be revealed that higher temperatures accelerated the ripening of ears and allowed these plants to escape the infection within ears. In contrast, at lower temperatures, higher disease severity was observed even for tip infection. Infrared thermography could predict this primary site of ear infection through temperature span within ears and enabled disease detection before symptoms became visible. The temperature difference between air and ear was negatively correlated to FHB index and allowed disease detection at early senescence stage. Combining the features of thermal measurements and chlorophyll fluorescence images proved to present a high potential in characterising FHB at spikelet level. Discriminating spikelets infected with F. graminearum from those infected with F. culmorum were enabled up to 100% accuracy by fusion of sensor data. This study demonstrated that FHB is influenced by foliar wheat diseases when at low severities of both. The control of leaf pathogens by fungicides can play an important part in integrated disease management – also against Fusarium infections. It could also be confirmed that primary infection sites within ears and the prevailing environmental conditions after infection are key factors which determine the later development of FHB. Sensors proved to be useful in monitoring and assessing FHB under field conditions – detailed investigations under controlled conditions provided more profound insights in this regard. The findings of this research contribute to more efficient control of FHB using the concepts of remote sensing to improve precision plant protection and may be applied in selection processes of breeding for FHB resistance as well.Geeignete Sensoren können einen wertvollen Einblick in die physiologischen Verhältnisse in Pflanzen bieten, wenn diese von pathogenen Organismen heimgesucht werden. Die Infektionen von Getreide durch Fusarium Arten (Fusarium Head Blight = FHB) verändern die optischen Eigenschaften von Wirtspflanzen – vor allem von Weizen (Triticum aestivum L.) – sowohl im Getreidestand als auch auf dem Niveau der einzelnen Ähren. Die vorliegenden Untersuchungen hatten zum Ziel, die näheren Gegebenheiten dieser komplexen Befallssituationen im Freiland und unter kontrollierten Bedingungen durch die Anwendung von zerstörungsfreien Messmethoden zu charakterisieren. Erhebungen im Feld machten deutlich, dass der Befall mit Fusarium Arten durch das Auftreten weiterer Blattkrankheiten im Weizenbestand gefördert wurde. Dies wurde beispielhaft an den Sorten „Tobak“ und „Pamier“ ermittelt und deutet auf eine geringere Widerstandsfähigkeit der Pflanzen gegenüber Fusariosen bei multiplem Befall hin. Dies konnte im Wachstumsstadium 83 (BBCH-Skala) auch makroskopisch festgestellt werden. Fungizidanwendungen, die vor der Blüte durchgeführt wurden, konnten das Auftreten der Fusarien am Weizen reduzieren. Dies war zweifellos auf die Reduktion des verfügbaren Inokulums der Fusarium-Arten zurückzuführen. Zugleich kann angenommen werden, dass durch die Gesunderhaltung der Blattfläche auch eine erhöhte Widerstandsfähigkeit der Pflanzen gegenüber Fusariosen hervorgerufen werden. Die Möglichkeit zur Vorhersage von FHB durch spektrale Parameter konnte bestätigt werden – vor allem an Hand des Blue- Green-Index 2 (BGI2) und des photochemischen Reflektionsindexes (PRI) erwiesen sich als besonders geeignet. An der Getreideähre ist die Entwicklung der Fusariosen in hohem Maße abhängig von dem primären Infektionsort und den nachfolgenden Umweltbedingungen im Anschluss an die Primärinfektion der Ähre. Dies konnte unter kontrollierten Bedingungen am Weizen der anfälligeren Sorte „Passat“ für Primärinokulationen an der an der Spitze, in der Mitte und an der Basis der Ähren nachgewiesen werden. Es zeigte sich, dass die Symptomentwicklung (FHB index) in der Ähre deutlich weniger nach unten gerichtet war, als in der Zone oberhalb des Inokulationspunktes. Dies galt sowohl für Infektionen durch F. graminearum als auch für F. culmorum. Im Gegensatz zur Symptomentwicklung entwickeln sich die Fusariosen vor allem abwärts in der Ähre – ein durchaus eher seltener Prozess für pflanzenpathogene Organismen. Erhöhte Temperaturen beschleunigen die Reifung der Ähren – obwohl günstig für das Auftreten von Fusariosen ermöglichen diese Bedingungen auch ein „disease escape“ gegenüber Fusarium-Arten. Bei niedrigeren Temperaturen führen die Infektionen zu deutlich höheren Infektionsraten, weil mehr Zeit zur Ausbreitung besteht – selbt in den Ährenspitzen. Mit Hilfe der Infrarot-Thermographie gelang es, die Primärinfektionen in der Ähre durch die Temperaturdifferenz zwischen Umwelt und den biologisch relevanten Zonen zu charakterisieren bevor bereits makroskopisch Symptome erkennbar wurden. Die Gewebetemperaturdifferenzen waren negativ korreliert mit dem FHB index – sie erlaubten aber auch eine Bestimmung des Reifestatus der Ähren. Wurden die Infrarotmessungen mit der Messung von Chlorophyllkorrelierten Messungen zerstörungsfrei kombiniert, lies sich damit eine hohe Korrelation identifizieren – insbesondere auf dem Niveau der einzelnen Ährchen. Wurden diese Beziehungen betrachtet, dann zeigte sich, dass sich sogar Unterschiede zwischen F. graminearum und F. culmorum erkennen ließen. Die vorliegenden Untersuchungen zeigen, dass das Auftreten von Fusariosen an Getreide auch in besonderem Umfang durch andere Blattkrankheiten gefördert wird – erkennbar allerdings nur bei geringen Befallsintensitäten. Die Förderung der Pflanzengesundheit – auch durch Fungizide – kann zu einer wichtigen Funktion im Integrierten Pflanzenschutz führen – sicher auch zum Schutz vor Ährenfusariosen. Es konnte nachgewiesen und durch Anwendung geeigneter Sensoren genutzt werden, dass die Fusarium-Infektionen eine besondere Rolle spielen. Vor allem die Primärinfektionsorte, die sehr umweltabhängig sind – haben großen Einfluss auf die Schadwirkung. Sensoren können offenbar sehr hilfreich bei dem Erkennen und der Befallsbestimmung – und das bereits unter Freilandbedingungen. Dies wurde durch Erhebungen unter Feldbedingungen bestätigt – ergänzt durch weitere Untersuchungen unter praktischen Bedingungen. Die vorliegenden Ergebnisse und Erkenntnisse können eine effizientere Unterdrückung von FHB ermöglichen. Dabei geht es darum, dass die Elemente des „sensing of diseases“ einerseits in den Integrierten Pflanzenschutz eingebunden werden können und zudem auch für Selektionsprozesse in der Züchtung zur Vermeidung von FHB genutzt werden kann

IPM2.0: PRECISION AGRICULTURE FOR SMALL-SCALE CROP PRODUCTION

In order to manage pests impacting New England crop production integrated pest management (IPM) practices should be reevaluated or updated regularly to ensure that effective control of crop pests is being achieved. Three fungal taxa, Colletotrichum gloeosporioides, C. acutatum, and Glomerella cingulata, are currently associated with bitter-rot of apple (Malus domestica), with C. acutatum typically being the dominant species found in the northeastern United States. However, a recent phylogenetic study demonstrated that both C. gloeosporioides and C. acutatum are species complexes with over 10 distinct species being recovered from apple between the two studies. Based on this recent information, the objectives of this study were 1) to complete a phylogenetic analysis to determine species diversity and distribution of Colletotrichum isolates associated with bitter-rot and Glomerella leaf spot in the northeastern United States and 2) to evaluate the sensitivity of these isolates to several commercially used fungicides. A multi-gene phylogenetic analysis was completed using ITS, GADPH and BT gene sequences in order to determine which species and how many species of Colletotrichum were infecting apples in the northeastern U.S. The results of this study demonstrated that C. fioriniae is the primary pathogen causing both bitter rot and Glomerella leaf spot in the northeastern U.S. A second experiment was conducted in order to update management practices for apple scab, caused by the ascomycete Venturia inaequalis. The objective of this project was to evaluate the ability of RIMpro, an apple scab warning system, to control apple scab in New England apple orchards in addition to evaluating the performance of potassium bicarbonate + sulfur as a low-cost alternative spray material for the control of apple scab suitable for organic apple production. Use of RIMpro allowed for the reduction in the total number of spray applications made during the primary scab season by two sprays in 2013 and one spray in 2014 (28% and 25% reductions, respectively). Also, the potassium bicarbonate + sulfur treatment was shown to provide the same level of control as Captan. Finally, disease outbreaks, insect infestation, nutrient deficiencies, and weather variation constantly threaten to diminish annual yields and profits in orchard crop production systems. Automated crop inspection with an unmanned aerial vehicle (UAV) can allow growers to regularly survey crops and detect areas affected by disease or stress and lead to more efficient targeted applications of pesticides, water and fertilizer. The overall goal of this project was to develop a low cost aerial imaging platform coupling imaging sensors with UAVs to be used for monitoring crop health. Following completion of this research, we have identified a useful tool for agricultural and ecological applications

Multi-sensor and data fusion approach for determining yield limiting factors and for in-situ measurement of yellow rust and fusarium head blight in cereals

The world’s population is increasing and along with it, the demand for food. A novel parametric model (Volterra Non-linear Regressive with eXogenous inputs (VNRX)) is introduced for quantifying influences of individual and multiple soil properties on crop yield and normalised difference vegetation Index. The performance was compared to a random forest method over two consecutive years, with the best results of 55.6% and 52%, respectively. The VNRX was then implemented using high sampling resolution soil data collected with an on-line visible and near infrared (vis-NIR) spectroscopy sensor predicting yield variation of 23.21%. A hyperspectral imager coupled with partial least squares regression was successfully applied in the detection of fusarium head blight and yellow rust infection in winter wheat and barley canopies, under laboratory and on-line measurement conditions. Maps of the two diseases were developed for four fields. Spectral indices of the standard deviation between 500 to 650 nm, and the squared difference between 650 and 700 nm, were found to be useful in differentiating between the two diseases, in the two crops, under variable water stress. The optimisation of the hyperspectral imager for field measurement was based on signal-to-noise ratio, and considered; camera angle and distance, integration time, and light source angle and distance from the crop canopy. The study summarises in the proposal of a new method of disease management through suggested selective harvest and fungicide applications, for winter wheat and barley which theoretically reduced fungicide rate by an average of 24% and offers a combined saving of the two methods of £83 per hectare

Multitemporal assessment of crop parameters using multisensorial flying platforms

UAV sensors suitable for precision farming (Sony NEX-5n RGB camera; Canon Powershot modified to infrared sensitivity; MCA6 Tetracam; UAV spectrometer) were compared over differently treated grassland. The high resolution infrared and RGB camera allows spatial analysis of vegetation cover while the UAV spectrometer enables detailed analysis of spectral reflectance at single points. The high spatial and six-band spectral resolution of the MCA6 combines the opportunities of spatial and spectral analysis, but requires huge calibration efforts to acquire reliable data. All investigated systems were able to provide useful information in different distinct research areas of interest in the spatial or spectral domain. The UAV spectrometer was further used to assess multiangular reflectance patterns of wheat. By flying the UAV in a hemispherical path and directing the spectrometer towards the center of this hemisphere, the system acts like a large goniometer. Other than ground based goniometers, this novel method allows huge diameters without any need for infrastructures on the ground. Our experimental results shows good agreement with models and other goniometers, proving the approach valid. UAVs are capable of providing airborne data with a high spatial and temporal resolution due to their flexible and easy use. This was demonstrated in a two year survey. A high resolution RGB camera was flown every week over experimental plots of barley. From the RGB imagery a time series of the barley development was created using the color values. From this analysis we could track differences in the growth of multiple seeding densities and identify events of plant development such as ear pushing. These results lead towards promising practical applications that could be used in breeding for the phenotyping of crop varieties or in the scope of precision farming. With the advent of high endurance UAVs such as airships and the development of better light weight sensors, an exciting future for remote sensing from UAV in agriculture is expected

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