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Avian botulism is a paralytic bacterial disease in birds often leading tohigh fatality. In-vitro diagnostic techniques such as Mouse Bioassay, ELISA,PCR are usually non-preventive, post-mortem in nature, and require invasivesample collection from affected sites or dead birds. In this study, we build afirst-ever multi-spectral, remote-sensing imagery based global Bird-AreaWater-bodies Dataset (BAWD) (i.e. fused satellite images of warm-waterlakes/marshy-lands or similar water-body sites that are important for avianfauna) backed by on-ground reporting evidence of outbreaks. BAWD consists of 16topographically diverse global sites monitored over a time-span of 4 years(2016-2021). We propose a first-ever Artificial Intelligence based (AI) modelto predict potential outbreak of Avian botulism called AVI-BoT (AerosolVisible, Infra-red (NIR/SWIR) and Bands of Thermal). We also train andinvestigate a simpler (5-band) Causative-Factor model (based on prominentphysiological factors reported in literature) to predict Avian botulism.AVI-BoT demonstrates a training accuracy of 0.96 and validation accuracy of0.989 on BAWD, far superior in comparison to our model based on causativefactors. We also perform an ablation study and perform a detailed feature-spaceanalysis. We further analyze three test case study locations - Lower KlamathNational Wildlife Refuge and Langvlei and Rondevlei lakes where an outbreak hadoccurred, and Pong Dam where an outbreak had not occurred and confirmpredictions with on-ground reportings. The proposed technique presents ascale-able, low-cost, non-invasive methodology for continuous monitoring ofbird-habitats against botulism outbreaks with the potential of saving valuablefauna lives.

Bird-Area Water-Bodies Dataset (BAWD) and Predictive AI Model for Avian Botulism Outbreak (AVI-BoT)