Fire Blight Disease Detection for Apple Trees: Hyperspectral Analysis of Healthy, Infected and Dry Leaves

Abstract

<jats:p>The effective and rapid detection of Fire Blight, an important bacterial disease caused by the quarantine pest E.amylovora, is crucial for today’s horticulture. This study explored the application of non-invasive proximal hyperspectral remote sensing (RS) in order to differentiate the healthy (H), infected (I) and dry (D) leaves of apple trees. Analysis of variance was employed in order to determine which hyperspectral narrow spectral bands exhibited the most significant differences. Spectral signatures for the range of 400–2500 nm were acquired with Thermo Scientific Evolution 220 and iS50NIR spectrometers. The selected spectral bands were then used to evaluate several RS indices, including ARI (Anthocyanin Reflectance Index), RDVI (Renormalized Difference Vegetation Index), MSR (Modified Simple Ratio) and NRI (Nitrogen Reflectance Index), for Fire Blight detection in apple tree leaves. Furthermore, a new index was proposed, namely QFI. The spectral indices were tested on apple trees infected by Fire Blight in a quarantine greenhouse. Results indicated that the short-wavelength infrared (SWIR) band located at 1450 nm was able to distinguish (I) and (H) leaves, while the SWIR band at 1900 nm differentiated all three leaf types. Moreover, tests using the Pearson correlation indicated that ARI, MSR and QFI exhibited the highest correlations with the infection progress. Our results prove that our hyperspectral remote sensing technique is able to differentiate (H), (I) and (D) leaves of apple trees for the reliable and precise detection of Fire Blight.</jats:p>

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

“…Hyperspectral remote sensing, which mainly concentrates on visible-near infrared (400-1000 nm) light and sometimes contains short-wave infrared ranges (1000-2500 nm), offers some alternative methods to monitor biochemical properties such as chl [21][22][23][24]. Besides the biochemical properties, some narrow wavebands possess high sensitivity to subtle changes in plants caused by stress or diseases, effectively detecting various stress or disease indicators [25][26][27][28].…”

Monitoring the southern corn rust based on hyperspectral remote sensing

“…The sensitive bands of grape leaf spot are 520~550nm and 700nm [6] . When apple leaves are infected by disease, the 1450 nm spectrum of short-wave infrared band changes [7]. The sensitive band of maize leaf blight is 725~740nm [8] .…”

Classification of Rice Leaf Blast Severity Using Hyperspectral Imaging

“…Hyperspectral imaging has been used for the assessment of plant disease severity in wine grapes, barley, and sugar beet among others [13,15,16]. A series of data processing methods have been used in previous studies, such as raw spectra [17,18], difference spectra [19,20], ratio spectra [21], derivative spectra [22,23] and vegetation indices [24][25][26]. All of them have achieved good results.…”