Early detection and quantification of verticillium wilt in olive using hyperspectral and thermal imagery over large areas

© 2015 by the authors. Automatic methods for an early detection of plant diseases (i.e., visible symptoms at early stages of disease development) using remote sensing are critical for precision crop protection. Verticillium wilt (VW) of olive caused by Verticillium dahliae can be controlled only if detected at early stages of development. Linear discriminant analysis (LDA) and support vector machine (SVM) classification methods were applied to classify V. dahliae severity using remote sensing at large scale. High-resolution thermal and hyperspectral imagery were acquired with a manned platform which flew a 3000-ha commercial olive area. LDA reached an overall accuracy of 59.0% and a κ of 0.487 while SVM obtained a higher overall accuracy, 79.2% with a similar κ, 0.495. However, LDA better classified trees at initial and low severity levels, reaching accuracies of 71.4 and 75.0%, respectively, in comparison with the 14.3% and 40.6% obtained by SVM. Normalized canopy temperature, chlorophyll fluorescence, structural, xanthophyll, chlorophyll, carotenoid and disease indices were found to be the best indicators for early and advanced stage infection by VW. These results demonstrate that the methods developed in other studies at orchard scale are valid for flights in large areas comprising several olive orchards differing in soil and crop management characteristics.Financial support for this research was provided by Project P08-AGR-03528 from “Consejería de Economía, Innovación y Ciencia” of Junta de Andalucía and the European Social Fund, and projects AGL-2012-37521 and AGL2012-40053-C03-01 from the Spanish “Ministerio de Economía y Competitividad” and the European Social Fund. Rocio Calderón is a recipient of research fellowship BES-2010-035511 from the Spanish “Ministerio de Ciencia e Innovación”.We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).Peer Reviewe

Detection of Verticillium wilt in olive using high-resolution hyperspectral and thermal remote sensing imagery

El olivo (Olea europaea L.) es el cultivo leñoso no tropical que ocupa mayor superficie en todo el mundo, con el 95% de la producción mundial localizada en la cuenca Mediterránea. España es el país con mayor superficie de olivar del mundo con 2.5 MHa y aproximadamente el 39% de la producción mundial. Durante las últimas décadas, la Verticilosis, causada por el hongo de suelo Verticillium dahliae Kleb., ha ocasionado severas pérdidas de rendimiento en el olivar, convirtiéndose en la enfermedad más limitante causada por patógenos de suelo de este cultivo a nivel mundial. Este patógeno coloniza el sistema vascular de la planta, bloqueando el flujo del agua y finalmente induciendo estrés hídrico. El desarrollo de la Verticilosis en el olivo puede estar influenciado por factores bióticos y abióticos, sin embargo, poco se sabe sobre la influencia del medio físico en él. Actualmente, ninguna medida de control aplicada individualmente es completamente efectiva para el tratamiento de la Verticilosis del olivo, no obstante, una estrategia de control integrado es la mejor forma de manejar la enfermedad, combinando el uso de medidas de control previas y posteriores a la plantación. Las medidas de control posteriores a la plantación serían más efectivas si las zonas del terreno con árboles afectados por Verticilosis fueran identificadas en etapas tempranas del desarrollo de la enfermedad con el objetivo de disminuir la expansión del patógeno y sucesivas infecciones a árboles o plantaciones vecinas. Sin embargo, la inspección visual en campo de síntomas de la enfermedad en estadios tempranos de su desarrollo es costosa en tiempo y recursos. Por lo tanto, la teledetección puede ser una herramienta muy útil para detectar el estrés hídrico inducido por la infección de V. dahliae en olivos en etapas tempranas del desarrollo de la enfermedad. Los principales objetivos de la presente Tesis Doctoral fueron: (i) evaluar el efecto de la temperatura del suelo en el desarrollo de la Verticilosis teniendo en cuanta diferentes patotipos de V. dahliae y cultivares de olivo; (ii) valorar el uso de la teledetección térmica e hiperespectral de alta resolución como herramienta para detectar la infección y severidad por Verticilosis en parcelas de olivar y áreas de mayor extensión, evaluando la temperatura e índices fisiológicos desde escala foliar a escala de cubierta. El primer objetivo se llevó a cabo con plantas de olivo de los cultivares (cv.) Arbequina y Picual que crecieron en suelo infestado con los patotipos defoliante (D) y no defoliante (ND) de V. dahliae bajo condiciones climáticas controladas en tanques de suelo con temperaturas de 16 a 32ºC. El desarrollo de la Verticilosis en plantas infectadas por el patotipo D fue más rápido y severo en cv. Picual que en cv. Arbequina. La temperatura de suelo óptima para el desarrollo de la infección del patotipo D fue de 16 a 24ºC para cv. Picual y de 20 a 24ºC para cv. Arbequina. Para el patotipo ND el rango de temperatura más favorable para la infección por V. dahliae fue de 16 a 20ºC. Estos resultados permiten...Olive (Olea europaea L.) is the most cultivated non-tropical fruit tree in the world, with 95% of the world production located in the Mediterranean Basin. Spain is the leading olive-producing country with 2.5 MHa and nearly 39% of the world production. During the last few decades, Verticillium wilt, caused by the soil-borne fungus Verticillium dahliae Kleb., has caused severe olive yield losses, becoming the most limiting soil-borne disease of this crop worldwide. This pathogen colonizes the vascular system of plants, blocking water flow and eventually inducing water stress. Development of Verticillium wilt in olive can be influenced by biotic and abiotic factors, nevertheless, little is known about the influence of the physical environment on it. Currently, no control measure applied singly is fully effective for the management of Verticillium wilt of olive; therefore an integrated disease management strategy is needed to manage the disease, combining the use of pre-planting and post-planting control measures. Post-planting control measures would be more efficient if Verticillium wiltaffected trees patches within fields are identified at early stages of disease development in order to mitigate the spread of the pathogen and successive infections to neighboring trees. However, visual inspection of disease symptoms at early stages of development in the field is time-consuming and expensive. Thus, remote sensing is thought to be a useful tool to detect water stress induced by V. dahliae infection in olive trees at early stages of disease development. The main objectives of this PhD Thesis were: (i) to assess the effect of soil temperature on Verticillium wilt development taking into account different V. dahliae pathotypes and olive cultivars; and (ii) to evaluate the use of high-resolution thermal and hyperspectral remote sensing imagery as a tool to detect Verticillium wilt infection and severity in olive orchards and larger areas, assessing temperature and physiological indices from leaf to canopy scale. The first objective was conducted with olive plants of cultivar (cv.) Arbequina and cv. Picual grown in soil infested with the defoliating (D) or non-defoliating (ND) pathotype of V. dahliae under controlled climatic conditions in soil tanks with a range of soil temperatures from 16 to 32ºC. Verticillium wilt development in plants infected by the D pathotype was faster and more severe on cv. Picual than on cv. Arbequina. Models estimated that infection by the D pathotype was promoted by soil temperature in a range of 16 to 24°C for cv. Picual and of 20 to 24ºC for cv. Arbequina. For the ND pathotype a range of 16 to 20ºC was estimated as the most favorable for infection. These results provide a better understanding of the differential geographic distribution of V. dahliae pathotypes and assess the potential effect of climate change on Verticillium wilt... development..

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

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers

Automated early plant disease detection and grading system: Development and implementation

As the agriculture industry grows, many attempts have been made to ensure high quality of produce. Diseases and defects found in plants and crops, affect the agriculture industry greatly. Hence, many techniques and technologies have been developed to help solving or reducing the impact of plant diseases. Imagining analysis tools, and gas sensors are becoming more frequently integrated into smart systems for plant disease detection. Many disease detection systems incorporate imaging analysis tools and Volatile Organic Compound (VOC) profiling techniques to detect early symptoms of diseases and defects of plants, fruits and vegetative produce. These disease detection techniques can be further categorized into two main groups; preharvest disease detection and postharvest disease detection techniques. This thesis aims to introduce the available disease detection techniques and to compare it with the latest innovative smart systems that feature visible imaging, hyperspectral imaging, and VOC profiling. In addition, this thesis incorporates the use of image analysis tools and k-means segmentation to implement a preharvest Offline and Online disease detection system. The Offline system to be used by pathologists and agriculturists to measure plant leaf disease severity levels. K-means segmentation and triangle thresholding techniques are used together to achieve good background segmentation of leaf images. Moreover, a Mamdani-Type Fuzzy Logic classification technique is used to accurately categorize leaf disease severity level. Leaf images taken from a real field with varying resolutions were tested using the implemented system to observe its effect on disease grade classification. Background segmentation using k-means clustering and triangle thresholding proved to be effective, even in non-uniform lighting conditions. Integration of a Fuzzy Logic system for leaf disease severity level classification yielded in classification accuracies of 98%. Furthermore, a robot is designed and implemented as a robotized Online system to provide field based analysis of plant health using visible and near infrared spectroscopy. Fusion of visible and near infrared images are used to calculate the Normalized Deference Vegetative Index (NDVI) to measure and monitor plant health. The robot is designed to have the functionality of moving across a specified path within an agriculture field and provide health information of leaves as well as position data. The system was tested in a tomato greenhouse under real field conditions. The developed system proved effective in accurately classifying plant health into one of 3 classes; underdeveloped, unhealthy, and healthy with an accuracy of 83%. A map with plant health and locations is produced for farmers and agriculturists to monitor the plant health across different areas. This system has the capability of providing early vital health analysis of plants for immediate action and possible selective pesticide spraying

Hyperspectral, thermal and LiDAR remote sensing for red band needle blight detection in pine plantation forests

PhD ThesisClimate change indirectly affects the distribution and abundance of forest insect pests and pathogens, as well as the severity of tree diseases. Red band needle blight is a disease which has a particularly significant economic impact on pine plantation forests worldwide, affecting diameter and height growth. Monitoring its spread and intensity is complicated by the fact that the diseased trees are often only visible from aircraft in the advanced stages of the epidemic. There is therefore a need for a more robust method to map the extent and severity of the disease. This thesis examined the use of a range of remote sensing techniques and instrumentation, including thermography, hyperspectral imaging and laser scanning, for the identification of tree stress symptoms caused by the onset of red band needle blight. Three study plots, located in a plantation forest within the Loch Lomond and the Trossachs National Park that exhibited a range of red band needle blight infection levels, were established and surveyed. Airborne hyperspectral and LiDAR data were acquired for two Lodgepole pine stands, whilst for one Scots pine stand, airborne LiDAR and Unmanned Aerial Vehicle-borne (UAV-borne) thermal imagery were acquired alongside leaf spectroscopic measurements. Analysis of the acquired data demonstrated the potential for the use of thermographic, hyperspectral and LiDAR sensors for detection of red band needle blight-induced changes in pine trees. The three datasets were sensitive to different disease symptoms, i.e. thermography to alterations in transpiration, LiDAR to defoliation, and hyperspectral imagery to changes in leaf biochemical properties. The combination of the sensors could therefore enhance the ability to diagnose the infection.Natural Environment Research Council (NERC) for funding this PhD program (studentship award 1368552) and providing access to specialist equipment through a Field Spectroscopy Facility loan (710.114). I would like to thank NERC Airborne Research Facility for providing airborne data (grant: GB 14-04) that made the PhD a challenge, to say the least. My sincere gratitude goes to the Douglas Bomford Trust for providing additional funds, which allowed for completion of the UAV-borne part of this research

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

Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery

Large-scale monitoring of vegetation dynamics by remote sensing is key to detecting early signs of vegetation decline. Spectral-based indicators of phys-iological plant traits (PTs) have the potential to quantify variations in pho-tosynthetic pigments, chlorophyll fluorescence emission, and structural changes of vegetation as a function of stress. However, the specific response of PTs to disease-induced decline in heterogeneous canopies remains largely unknown, which is critical for the early detection of irreversible damage at different scales. Four specific objectives were defined in this research: i) to assess the feasibility of modelling the incidence and severity of Phytophthora cinnamomi and Xylella fastidiosa based on PTs and biophysical properties of vegetation; ii) to assess non-visual early indicators, iii) to retrieve PT using radiative transfer models (RTM), high-resolution imagery and satellite observations; and iv) to establish the basis for scaling up PTs at different spatial resolutions using RTM for their retrieval in different vegetation co-vers. This thesis integrates different approaches combining field data, air- and space-borne imagery, and physical and empirical models that allow the retrieval of indicators and the evaluation of each component’s contribution to understanding temporal variations of disease-induced symptoms in heter-ogeneous canopies. Furthermore, the effects associated with the understory are introduced, showing not only their impact but also providing a compre-hensive model to account for it. Consequently, a new methodology has been established to detect vegetation health processes and the influence of biotic and abiotic factors, considering different components of the canopy and their impact on the aggregated signal. It is expected that, using the presented methods, existing remote sensors and future developments, the ability to detect and assess vegetation health globally will have a substantial impact not only on socio-economic factors, but also on the preservation of our eco-system as a whole