Abstract:
There is a significant threat to the global supply of bananas that is posed by the disease known as
Panama wilt, which is also referred to as Banana Fusarium Wilt (BFW) in some instances. A
significant dissatisfaction is caused by the fact that there are currently no viable treatment
options available for BFW. The early monitoring of the disease and the evaluation of its
distribution were the primary focuses of this research project, with intention of making a
contribution to the decrease of BFW. A model that is capable of identifying regions or plants
within a banana plantation that are either infested without BFW or free of BFW was developed
as one of the specific objectives. The other specific objectives include the identification of an
appropriate image processing technique, the determination of sensitive parameters for the
selected technique, and the development of a model. Utilizing an unmanned aerial vehicle at a
flying height of 20 m above the ground, multispectral images were captured over a BFW-affected
banana plantation. A single flight, covering 3 acres, yielded images totaling 639 under standard
operational conditions. The categorization model included two types of spectral features as
inputs: three multispectral band images and one vegetative index (VI). A self-organizing data
analysis approach was utilized to identify canopies that are infected with BFW. Comparative
analysis demonstrated that canopies infected with BFW exhibited higher reflectance in the
Normalized Difference Vegetation Index (NDVI) range and exhibited distinctive color variations
in the NDVI region compared to canopies that were healthy. The research results indicated that
VIs, such as NDVI were successful in accurately detecting BFW Disease. The study employed
binary logistic regression to evaluate the spatial correlation between VIs and the presence or
absence of BFW in plants. The algorithm effectively detected the disease and accurately
delineated specific regions using landmarks. The study further employed Google Maps to quantify
the distances between afflicted plants and nearby landmarks. The research findings provide
valuable information for management of banana plantations, presenting practical methods for
detecting plant diseases and providing recommendations to farmers in reducing the impact of
Panama wilt disease on banana cultivation.
