Avian botulism caused by a bacterium, Clostridium botulinum, causes a paralytic disease in birds often leading to high fatality, and is usually diagnosed using molecular techniques. Diagnostic techniques for Avian botulism include: Mouse Bioassay, ELISA, PCR, all of which are time-consuming, laborious and require invasive sample collection from affected sites. 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 water-body sites important for avian fauna) backed by on-ground reporting evidence of outbreaks. In the current version, BAWD covers a total ground area of 904 sq.km from two open source satellite projects (Sentinel and Landsat). BAWD consists of 17 topographically diverse global sites spanning across 4 continents, with locations monitored over a time-span of 3 years (2016-2020). Using BAWD and state-of-the-art deep-learning techniques 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). AVI-BoT uses fused multi-spectral satellite images of water-bodies (10-bands) as input to generate a spatial prediction map depicting probability of potential Avian botulism outbreaks. We also train and investigate a simpler (5-band) Causative-Factor model (based on prominent physiological factors reported in literature as conducive for outbreak) to predict Avian botulism. Using AVI-BoT, we achieve a training accuracy of 0.94 and validation accuracy of 0.96 on BAWD, far superior in comparison to our Causative factors model. 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.
翻译:细菌(Clostridium botulinum)引起的禽肉肉瘤,在鸟类中造成麻痹性疾病,经常导致高致命性,通常使用分子技术诊断。禽肉病的诊断技术包括:鼠标Bioassay、ELISA、PCRR,所有这些都耗时、劳累,需要从受影响地点采集侵入性样本。在这项研究中,我们建立了一个以Bird-Area 水体数据库为基础的全球多频谱遥感图像(BAWD)(即对禽类动物很重要的水体网站的合合合卫星图像),由地面报告爆发的证据支持。在目前版本中,BAWD覆盖了两个开放源卫星项目(Sentinel和Landat)的904 sq.km整个地面区域。BAWD由17个地形多样性全球地点组成,在3年时间范围内对智能(2016-2020年)的深度(Birdeal-al-commexial-commissional disal dial districal districal) 数据模型监测。利用BAWAWAWAWI-re-de-de-listria-listrial-listria-I-I-list-list-list-listrial-I revial-I revial-I revial-I real-I revial-I revial-I revial-I) 数据,我们提出,我们提出一个数字智能模型,我们提出一个数字-irmatial-irst-ider-ider-ider-ider-Ide-I-I-I-I