Paper Key : IRJ************234
Author: Prajot Dhanpal Kumbhar
Date Published: 12 Nov 2024
Abstract
The detection of fowl diseases has traditionally depended on expert knowledge and visual inspections, a process that is often time-consuming and prone to human error. Recent advancements in machine learning (ML) present new opportunities to automate and improve disease detection methods. This paper investigates the application of ML techniques for fowl disease detection using symptom datasets formatted in text. We review various methodologies for analyzing text-based symptoms, focusing on their effectiveness in identifying different fowl diseases. The study compares the performance of multiple ML models, including both traditional algorithms and advanced techniques. Furthermore, we identify the key challenges faced in implementing these models, such as data quality, interpretability, and integration into existing diagnostic workflows. By outlining future research directions, this paper aims to contribute to the ongoing efforts in enhancing poultry health management through innovative machine learning applications. Our findings suggest that leveraging ML for analyzing textual symptom data not only improves the accuracy of disease detection but also reduces the reliance on expert assessments, making the process more efficient. Overall, this research highlights the potential of machine learning to revolutionize fowl disease detection and offers insights for further exploration in this vital area of agricultural health.
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