The Early Detection of Apple Scab Using Multispectral Imagery Under Natural Illumination Conditions
Creators: name: Bleasdale, Alexander
Publisher: name: Lancaster University
Year: 2024

description: Apples are a nutritious, globally significant crop, yet their production
 has a substantial environmental impact due to the reliance on heavy
 pesticide use within disease management strategies. Apple scab, caused by
 the fungus Venturia inaequalis, is the primary cause of yield losses among
 all the pathogens that affect apples globally, leading to the highest
 protection costs and volumes of fungicides used for control. Conventional
 disease management strategies rely on uniform spraying of protective
 fungicides, repeated frequently throughout the growing season. This
 expensive, chemical-intensive approach contributes to widespread
 environmental contamination in many apple-growing regions worldwide. The
 early detection of apple scab infections offers a promising alternative
 for disease control by enabling targeted fungicide spraying, thereby
 improving the effectiveness of control measures and reducing chemical
 usage. Remote sensing systems are powerful tools for the early detection
 of plant pathogens; however, practical solutions for apple scab monitoring
 in commercial orchards are limited. The challenges of early detection in
 orchards arise from diverse symptom variations, variable illumination
 conditions, tree physiologies, and influences from other stress factors.
 Demonstrating the feasibility of early apple scab detection by remote
 sensing systems in uncontrolled conditions represents a significant step
 towards applying such systems in commercial orchards.
 
 
 This thesis aims to develop a remote sensing strategy for the early
 detection of apple scab infections under natural illumination conditions.
 The research rationale will first be introduced, followed by a review of
 the requirements for early disease detection in orchards and the current
 strategies and technologies available, including imaging sensors,
 classification methods and acquisition platforms. The capabilities of
 several low-cost sensing systems (multispectral, thermal, and 3D cameras)
 for detecting early apple scab infections under natural illumination are
 then assessed. Results indicate that apple scab symptoms could be manually
 identified several days earlier from high-resolution near-infrared (NIR)
 imagery than equivalent RGB imagery due to major differences in NIR
 radiation absorption potential between healthy and infected tissue. RGB
 and NIR time series datasets comprising 150 individual plants were then
 acquired and used to train convolutional neural networks (CNNs) to enable
 rapid, automated classification of apple scab symptoms. These CNNs
 consistently classified apple scab infections earlier and more accurately
 from NIR imagery than RGB imagery. This research shows that NIR imagery is
 effective for early apple scab detection under natural illumination
 conditions, and automated identification of the disease can be achieved
 accurately and rapidly with CNN classification models. This represents a
 significant advancement towards applying remote sensing systems for
 disease monitoring in apple orchards.