  Avian botulism is a paralytic bacterial disease in birds often leading to
high fatality. In-vitro diagnostic techniques such as Mouse Bioassay, ELISA,
PCR are usually non-preventive, post-mortem in nature, and require invasive
sample collection from affected sites or dead birds. In this study, we build a
first-ever multi-spectral, remote-sensing imagery based global Bird-Area
Water-bodies Dataset (BAWD) (i.e. fused satellite images of warm-water
lakes/marshy-lands or similar water-body sites that are important for avian
fauna) backed by on-ground reporting evidence of outbreaks. BAWD consists of 16
topographically diverse global sites monitored over a time-span of 4 years
(2016-2021). We propose a first-ever Artificial Intelligence based (AI) model
to predict potential outbreak of Avian botulism called AVI-BoT (Aerosol
Visible, Infra-red (NIR/SWIR) and Bands of Thermal). We also train and
investigate a simpler (5-band) Causative-Factor model (based on prominent
physiological factors reported in literature) to predict Avian botulism.
AVI-BoT demonstrates a training accuracy of 0.96 and validation accuracy of
0.989 on BAWD, far superior in comparison to our model based on causative
factors. We also perform an ablation study and perform a detailed feature-space
analysis. We further analyze three test case study locations - Lower Klamath
National Wildlife Refuge and Langvlei and Rondevlei lakes where an outbreak had
occurred, and Pong Dam where an outbreak had not occurred and confirm
predictions with on-ground reportings. The proposed technique presents a
scale-able, low-cost, non-invasive methodology for continuous monitoring of
bird-habitats against botulism outbreaks with the potential of saving valuable
fauna lives.
