Abstract:
Sugarcane white leaf phytoplasma (white leaf disease) in sugarcane crops is caused by a
phytoplasma transmitted by leafhopper vectors. White leaf disease (WLD) occurs predominantly in
some Asian countries and is a devastating global threat to sugarcane industries, especially Sri Lanka.
Therefore, a feasible and an effective approach to precisely monitoring WLD infection is important,
especially at the early pre-visual stage. This work presents the first approach on the preliminary
detection of sugarcane WLD by using high-resolution multispectral sensors mounted on small
unmanned aerial vehicles (UAVs) and supervised machine learning classifiers. The detection pipeline
discussed in this paper was validated in a sugarcane field located in Gal-Oya Plantation, Hingurana,
Sri Lanka. The pixelwise segmented samples were classified as ground, shadow, healthy plant, early
symptom, and severe symptom. Four ML algorithms, namely XGBoost (XGB), random forest (RF),
decision tree (DT), and K-nearest neighbors (KNN), were implemented along with different python
libraries, vegetation indices (VIs), and five spectral bands to detect the WLD in the sugarcane field.
The accuracy rate of 94% was attained in the XGB, RF, and KNN to detect WLD in the field. The
top three vegetation indices (VIs) for separating healthy and infected sugarcane crops are modified
soil-adjusted vegetation index (MSAVI), normalized difference vegetation index (NDVI), and excess
green (ExG) in XGB, RF, and DT, while the best spectral band is red in XGB and RF and green in
DT. The results revealed that this technology provides a dependable, more direct, cost-effective, and
quick method for detecting WLD
