Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements

© 2014 by the authors. Spectral Vegetation Indices (SVIs) have been widely used to indirectly detect plant diseases. The aim of this research is to evaluate the effect of different disease symptoms on SVIs and introduce suitable SVIs to detect rust disease. Wheat leaf rust is one of the prevalent diseases and has different symptoms including yellow, orange, dark brown, and dry areas. The reflectance spectrum data for healthy and infected leaves were collected using a spectroradiometer in the 450 to 1000 nm range. The ratio of the disease-affected area to the total leaf area and the proportion of each disease symptoms were obtained using RGB digital images. As the disease severity increases, so does the scattering of all SVI values. The indices were categorized into three groups based on their accuracies in disease detection. A few SVIs showed an accuracy of more than 60% in classification. In the first group, NBNDVI, NDVI, PRI, GI, and RVSI showed the highest amount of classification accuracy. The second and third groups showed classification accuracies of about 20% and 40% respectively. Results show that few indices have the ability to indirectly detect plant disease

Developing two spectral disease indices for detection of wheat leaf rust (Pucciniatriticina)

Spectral vegetation indices (SVIs) have been widely used to detect different plant diseases. Wheat leaf rust manifests itself as an early symptom with the leaves turning yellow and orange. The sign of advancing disease is the leaf colour changing to brown while the final symptom is when the leaf becomes dry. The goal of this work is to develop spectral disease indices for the detection of leaf rust. The reflectance spectra of the wheat's infected and non-infected leaves at different disease stages were collected using a spectroradiometer. As ground truth, the ratio of the disease-affected area to the total leaf area and the fractions of the different symptoms were extracted using an RGB digital camera. Fractions of the various disease symptoms extracted by the digital camera and the measured reflectance spectra of the infected leaves were used as input to the spectral mixture analysis (SMA). Then, the spectral reflectance of the different disease symptoms were estimated using SMA and the least squares method. The reflectance of different disease symptoms in the 450~1000 nm were studied carefully using the Fisher function. Two spectral disease indices were developed based on the reflectance at the 605, 695 and 455 nm wavelengths. In both indices, the R2 between the estimated and the observed was as highas 0.94. © 2014 by the authors; licensee MDPI, Basel, Switzerland

Hyperspectral Remote Sensing of Vegetation and Agricultural Crops

There are now over 40 years of research in hyperspectral remote sensing (or imaging spectroscopy) of vegetation and agricultural crops (Thenkabail et al., 2011a). Even though much of the early research in hyperspectral remote sensing was overwhelmingly focused on minerals, now there is substantial literature in characterization, monitoring, modeling, and mapping of vegetation and agricultural crops using ground-based, platform-mounted, airborne, Unmanned Aerial Vehicle (UAV) mounted, and spaceborne hyperspectral remote sensing (Swatantran et al., 2011; Atzberger, 2013; Middleton et al., 2013; Schlemmer et al., 2013; Thenkabail et al., 2013; Udelhoven et al., 2013; Zhang et al., 2013). The state-of-the-art in hyperspectral remote sensing of vegetation and agriculture shows significant enhancement over conventional remote sensing, leading to improved and targeted modeling and mapping of specific agricultural characteristics such as: (a) biophysical and biochemical quantities (Galvão, 2011; Clark and Roberts, 2012), (b) crop type\species (Thenkabail et al., 2013), (c) management and stress factors such as nitrogen deficiency, moisture deficiency, or drought conditions (Delalieux et al., 2009; Gitelson, 2013; Slonecker et al., 2013), and (d) water use and water productivities (Thenkabail et al., 2013). At the same time, overcoming Hughes’ phenomenon or curse of dimensionality of data and data redundancy (Plaza et al., 2009) is of great importance to make rapid advances in a much wider utilization of hyperspectral data. This is because, for a specific application, a large number of hyperspectral bands are redundant (Thenkabail et al., 2013). Selecting the relevant bands will require the use of data mining techniques (Burger and Gowen, 2011) to focus on utilizing the optimal or best ones to maximize the efficiency of data use and reduce unnecessary computing..

Chlorophyllfluoreszenz als sensorischer Parameter in der Apfellagerung

Die Lagerung von Früchten unter kontrollierter Atmosphäre ermöglicht es die Fruchtqualität und Haltbarkeit zu bewahren. Insbesondere die dynamisch kontrollierte Lagerung mit Messung der Chlorophyllfluoreszenz (Dynamic Controlled Atmosphere - Chlorophyll Fluorescence = DCA-CF) von Äpfeln (Malus x domestica, BORKH.) bietet großes Potential. Bei diesem Typ der DCA-Lagerung wird die Chlorophyllfluoreszenz gemessen, um das Stressverhalten von Äpfeln als Folge von zu niedrigen O2-Konzentrationen in der Lagerraumatmosphäre zu bewerten. Gegenwärtig werden in der DCA-CF-Lagerung nur nicht-bildgebende Fluoreszenzmessverfahren verwendet. Jedoch kann das Potential der DCA-CF-Lagerung nur teilweise genutzt werden, weil Äpfel gleicher Sorte und Herkunft bei völlig identischen Lagerungsbedingungen ein unterschiedliches Stressverhalten hinsichtlich niedriger O2-Konzentrationen zeigen können. Daher ist die Auswahl einer repräsentativen Probe für die Fluoreszenzmessung beeinträchtigt. Ziel dieser Dissertation war es, die Ursachen für das unterschiedliche Stressverhalten von einzelnen Äpfeln hinsichtlich niedriger O2-Konzentrationen zu finden, um die Auswahl einer repräsentativen Probe für die Fluoreszenzmessung zu erreichen. Darüber hinaus wurde mit einem bildgebenden Messverfahren die Chlorophyllfluoreszenz auf der Apfelschale gemessen, um die Fluoreszenzheterogenität sowie die Reaktion verschiedener Fluoreszenzparamter bezüglich O2-Mangel zu untersuchen. Unter Verwendung eines nicht-bildgebenden Fluoreszenzmessverfahrens wurde die kritische O2-Konzentration (Lower Oxygen Limit = LOL) einzelfruchtweise identifiziert. Der Reifegrad der Apfelfrüchte, insbesondere kurz nach der Ernte, wenn der Stärkeabbau noch nicht abgeschlossen war, beeinflusste den LOL signifikant. An einem Teil der Apfelfrüchte, die zuvor vier Monate gelagert wurden, konnte der LOL nicht identifiziert werden (variierend von 12,5 % bis 41,7 % der untersuchten Äpfel; n = 96). Zudem zeigte sich, dass der Chlorophyllgehalt in der Apfelschale die Fluoreszenzmessmethode massiv beeinflusste. Des Weiteren konnte mit einem bildgebenden Fluoreszenzmessverfahren die räumliche Verteilung der Chlorophyllfluoreszenz auf der Apfelschale dargestellt werden. Der Fluoreszenzanstieg infolge von niedrigen O2-Konzentrationen in der Lagerraumatmosphäre wurde visualisiert. Ferner wurde eine neue Methode für die Identifizierung des LOL mit einem bildgebenden Fluoreszenzmessverfahren entwickelt, welches die Heterogenität der Fluoreszenz berücksichtigt und die gemessenen Daten als Histogramm bündelt. Darüber hinaus zeigte die Fluoreszenzkinetik spezifische Änderungen, wenn Äpfel anaeroben Bedingungen ausgesetzt wurden. Insbesondere die Parameter Fv/Fm, ɸPSII_D1, ɸPSII_D2 und ɸPSII_D3 waren für die frühzeitige Detektierung von Stress aufgrund von O2-Mangel geeignet.Storing fruits under controlled atmosphere makes preserving fruit quality and shelf life possible. In particular, the Dynamic Controlled Atmosphere storage with Chlorophyll Fluorescence (DCA-CF) measurement of apples (Malus x domestica, BORKH.) offers great potential. In this type of DCA storage, chlorophyll fluorescence is measured to evaluate the stress behavior of apples due to low-O2 concentrations in the storage room atmosphere. Currently, only non-imaging fluorescence measurement methods are used in DCA-CF storage. However, the potential of DCA-CF storage can only be partially used because apples of the same variety and origin can show different stress behavior with regard to low-O2 concentrations under completely identical storage conditions. Therefore, the selection of a representative sample for fluorescence measurement is hindered. The aim of this dissertation was to find the causes of the different stress behavior of individual apples with regard to low-O2 concentrations in order to select a representative sample for fluorescence measurement. In addition, the chlorophyll fluorescence on the apple skin was measured using an imaging measurement system in order to investigate the fluorescence heterogeneity and the reaction of various fluorescence parameters to low-O2 conditions. The Lower Oxygen Limit (LOL) was identified on individual fruit using a non-imaging fluorescence measurement method. The ripeness of the apples influenced the LOL significantly, especially after harvest when starch degradation had not yet been completed. The LOL could not be identified in some of the apples stored for four months (varying from 12.5 % to 41.7 % of the examined apples; n = 96). It was also shown that the chlorophyll content in the apple skin had a massive influence on the fluorescence measurement method. Furthermore, the spatial distribution of chlorophyll fluorescence on the apple skin could be shown using an imaging fluorescence system. The increase in fluorescence due to low-O2 concentrations in the storage room was visualized. Additionally, a new method for identifying the LOL using an imaging fluorescence system was developed, which considers the heterogeneity of the fluorescence and bundles the measured fluorescence data as a histogram. Also, the fluorescence kinetics showed specific changes when apples were exposed to anaerobic conditions. In particular, the parameters Fv/Fm, ɸPSII_D1, ɸPSII_D2, and ɸPSII_D3 were suitable for early stress detection due to low-O2 conditions

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

Hyperspectral Reflectance and Fluorescence Imaging to Detect Scab Induced Stress in Apple Leaves

Apple scab causes significant losses in the production of this fruit. A timely and more site-specific monitoring and spraying of the disease could reduce the number of applications of fungicides in the fruit industry. The aim of this leaf-scale study therefore lies in the early detection of apple scab infections in a non-invasive and non-destructive way. In order to attain this objective, fluorescence- and hyperspectral imaging techniques were used. An experiment was conducted under controlled environmental conditions, linking hyperspectral reflectance and fluorescence imaging measurements to scab infection symptoms in a susceptible apple cultivar (Malus x domestica Borkh. cv. Braeburn). Plant stress was induced by inoculation of the apple plants with scab spores. The quantum efficiency of Photosystem II (PSII) photochemistry was derived from fluorescence images of leaves under light adapted conditions. Leaves inoculated with scab spores were expected to have lower PSII quantum efficiency than control (mock) leaves. However, besides scab-induced, also immature leaves exhibited low PSII quantum efficiency. Therefore, this study recommends the simultaneous use of fluorescence imaging and hyperspectral techniques. A shortwave infrared narrow-waveband ratio index (R1480/R2135) is presented in this paper as a promising tool to identify scab stress before symptoms become visible to the naked eye. Low PSII quantum efficiency attended by low narrow waveband R1480/R2135 index values points out scab stress in an early stage. Apparent high PSII quantum efficiency together with high overall reflectance in VIS and SWIR spectral domains indicate a severe, well-developed scab infection

The Feasibility, Practicality and Uses of Detecting Crop Water Stress in Southern Ontario Apple Orchards with a UAS

UAS (Unmanned Aerial Systems) are becoming more common place in agricultural sites around the world. While the accuracy of achieving NDVI (Normalized Difference Vegetation Index) from a UAS is well understood, few studies have attempted to acquire other plant health attributes such as CWSI (Crop Water Stress Index), particularly in horticulture such as apple orchards. In addition, no academic studies up to the time of this writing have explored the perceived usefulness of data obtained from a UAS for the average farmer. This study explored the practicality and feasibility of using UAS for apple orchards in Southern Ontario. This study sought to find out if NDVI and CWSI can be accurately obtained from a UAS for apple orchards and if this data can be feasibly obtained and is practical for the average Ontario apple farmer. By flying a UAS over a volunteer orchard and conducting charrette style interviews with orchard owners with the obtained data, the results showed that data is indeed useful to the farmers, despite improvements needed for CWSI accuracy. However, this data is only useful during key times of the growing season and obtaining this data, while feasible, requires planning and logistics around weather and government red tape. This study has laid the ground work for future studies to use as a staging point to improve CWSI estimate accuracy, create new methods of observing health attributes or diseases in apple orchards, and obtain more information on the usefulness of UAS data for Ontario farmers