Framework design for Machine Learning Integrated Mobile Based Livestock Disease Data Management, Diagnosis, and Treatment

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Mohammed Kemal Ahmed, Durga Prasad Sharma, Hussein Seid Worku, Ravindra Babu. B


The advancement of ICT opens the exciting potential to improve and maintain the quality of healthcare systems. However, the lack of detailed consideration of inclusiveness, usability, completeness, and localization with societal issues is still lagging in designing the next-generation intelligent systems. Upon the rigorous survey of the existing state-of-the-art studies, it was observed that the intelligent veterinary services with support the experts specifically in the far-flung rural areas over hand-held small mobile devices are still not fully explored. This approach in a country such as Ethiopia can significantly be a game-changing support system to assist in the medication of livestock. The prime goal of this paper is to investigate and analyze the problems associated with the existing systems and practices available in the field of livestock disease data collection, management, diagnosis, and detection and explore the possible intervention of machine learning with smartphone/mobile technologies in livestock disease data collection, management, diagnosis, treatment, and predictions Upon findings of the survey, interview, and technical observations, this paper proposes a machine learning-based mobile livestock disease data collection,  data management, diagnosis, prediction, and treatment system framework for livestock using selected parameters to support and alleviate the existing challenges in veterinary service delivery systems.

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