Prediction of the kiwifruit decline syndrome in diseased orchards by remote sensing

Eight years after the first record in Italy, Kiwifruit Decline (KD), a destructive disease causing root rot, has already affected more than 25% of the area under kiwifruit cultivation in Italy. Diseased plants are characterised by severe decay of the fine roots and sudden wilting of the canopy, which is only visible after the season's first period of heat (July-August). The swiftness of symptom appearance prevents correct timing and positioning for sampling of the disease, and is therefore a barrier to aetiological studies. The aim of this study is to test the feasibility of thermal and multispectral imaging for the detection of KD using an unsupervised classifier. Thus, RGB, multispectral and thermal data from a kiwifruit orchard, with healthy and diseased plants, were acquired simultaneously during two consecutive growing seasons (2017-2018) using an Unmanned Aerial Vehicle (UAV) platform. Data reduction was applied to the clipped areas of the multispectral and thermal data from the 2017 survey. Reduced data were then classified with two unsupervised algorithms, a K-means and a hierarchical method. The plant vigour (canopy size and presence/absence of wilted leaves) and the health shifts exhibited by asymptomatic plants between 2017 and 2018 were evaluated from RGB data via expert assessment and used as the ground truth for cluster interpretation. Multispectral data showed a high correlation with plant vigour, while temperature data demonstrated a good potential use in predicting health shifts, especially in highly vigorous plants that were asymptomatic in 2017 and became symptomatic in 2018. The accuracy of plant vigour assessment was above 73% when using multispectral data, while clustering of the temperature data allowed the prediction of disease outbreak one year in advance, with an accuracy of 71%. Based on our results, the unsupervised clustering of remote sensing data could be a reliable tool for the identification of sampling areas, and can greatly improve aetiological studies of this new disease in kiwifruit

Evaluating the Use of sUAS-Derived Imagery for Monitoring Flood Protection Infrastructure

In the US there are approximately 33,000 miles of levees. This includes 14,500 miles of levee systems associated with US Army Corps of Engineers programs and approximately 15,000 miles from other state and federal agencies. More than 14 million people live behind levees and associated flood prevention infrastructure. Monitoring and risk assessment are an on-going process, especially during times of flood conditions. The city of New Orleans was heavily impacted by Hurricane Katrina in 2005 by storm surges and intense rainfall. The impact of the hurricane was substantial enough to cause levee failure and I-wall toppling where many of the levees were breached and waters flooded the city. Subsidence and increasing population are likely to make flooding events more frequent and costly. As new technologies emerge, monitoring and risk assessment can benefit to increase community resiliency. In this research, I investigate the use of the structure from motion photogrammetric method to monitor positional changes in invariant objects such as levees, specifically, I-walls. This method uses conventional digital images from multiple view locations and angles by either a moving aerial platform or terrestrial photography. Using parallel coded software and accompanying hardware, 3D point clouds, digital surface models and orthophotos can be created. By providing comparisons of similar processing workflows with a variety of imaging acquisition criteria using commercially available unmanned aerial systems (UAS), we created multiple image sets of a simulated I-wall at various flight elevations, look angles, and effective overlap. The comparisons can be used for sensor selection and mission planning to improve the quality of the final product

Developing thermal infrared imaging systems for monitoring spatial crop temperatures for precision agriculture applications

Master of ScienceDepartment of Biological & Agricultural EngineeringAjay ShardaPrecise water application conserves resources, reduces costs, and optimizes plant performance and quality. Existing irrigation scheduling utilizes single, localized measurements that do not account for spatial crop water need; but, quick, single-point sensors are impractical for measuring discrete variations across large coverage areas. Thermography is an alternate approach for measuring spatial temperatures to quantify crop health. However, agricultural studies using thermography are limited due to previous camera expense, unfamiliar use and calibration, software for image acquisition and high-throughput processing specifically designed for thermal imagery mapping and monitoring spatial crop water need. Recent advancements in thermal detectors and sensing platforms have allowed uncooled thermal infrared (TIR) cameras to become suited for crop sensing. Therefore, a small, lightweight thermal infrared imaging system (TIRIS) was developed capable of radiometric temperature measurements. One-time (OT) and real-time (RT) radiometric calibrations methods were developed and validated for repeatable, temperature measurements while compensating for strict environmental conditions within a climate chamber. The Tamarisk® 320 and 640 analog output yielded a measurement accuracy of ±0.82°C or 0.62ºC with OT and RT radiometric calibration, respectively. The Tamarisk® 320 digital output yielded a measurement accuracy of ±0.43 or 0.29ºC with OT and RT radiometric calibration, respectively. Similarly, the FLIR® Tau 2 analog output yielded a measurement accuracy of ±0.87 or 0.63ºC with OT and RT radiometric calibration, respectively. A TIRIS was then built for high-throughput image capture, correction, and processing and RT environmental compensation for monitoring crop water stress within a greenhouse and temperature mapping aboard a small unmanned aerial systems (sUAS). The greenhouse TIRIS was evaluated by extracting plant temperatures for monitoring full-season crop water stress index (CWSI) measurements. Canopy temperatures demonstrated that CWSI explained 82% of the soil moisture variation. Similarly, validation aboard a sUAS provided radiometric thermal maps with a ±1.38°C (α=0.05) measurement accuracy. Due to the TIR cameras’ performance aboard sUAS and greenhouse platforms, a TIRIS provides unparalleled spatial coverage and measurement accuracy capable of monitoring subtle crop stress indicators. Further studies need to be conducted to produce spatial crop water stress maps at scales necessary for variable rate irrigation systems

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

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

NIR Imagery-based Grass Fire Detection and Metrics Measurement using Small UAS

This thesis focuses on the generation of a new grass fire aerial image dataset and development of novel methods for near-infrared (NIR) imagery-based fire front identification and fire depth estimation using small unmanned aircraft systems (sUAS). The procedure for collection and creation of the Grass Fire Front and near-Infrared (NIR) and Thermal Imagery (GRAFFITI) dataset is introduced first including two levels of data: synced raw thermal and red, green and near-infrared (RGNIR) image pairs and processed image pairs of the same overlapping field-of-view. A novel NIR imagery-based fire detection and fire front identification algorithm is then proposed and validated against manually labeled ground truth, using the GRAFFITI dataset. A comparative study is further performed on the problem of grass fire front location and flame depth estimation using thermal and NIR imagery. Finally, recommendations are made to future researchers who are interested in wildland fire sensing using thermal or NIR imagery

Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution