Cattle Farm Management System Using Machine Learning and Image Processing

Abstract

Livestock production in Sri Lanka faces multi- faceted challenges in disease management, milk production optimization, dietary enhancement, and financial performance. This research paper encompasses four significant components aimed at revolutionizing the cattle farming industry. The first segment introduces an innovative image processing-based solution to accurately identify and manage cattle diseases. By analyzing photos of cattle, this system diagnoses ailments and offers tailored treatment recommendations, thereby curbing disease spread and improving overall herd health. The second segment addresses milk production challenges, focusing on predicting and managing milk yields. Through comprehensive data analysis and predictive modeling, the study aims to mitigate disparities between forecasted and actual harvests, providing practical strategies for farmers to enhance productivity. The third segment concentrates on optimizing cattle diets. By collecting and analyzing data on nutritional needs, growth rates, and environmental factors, machine learning models are developed to enhance feeding schedules, ensuring improved health, productivity, and reduced feed costs.Lastly, the fourth segment aims to optimize financial performance in cattle operations. Leveraging machine learning and data analysis, the study focuses on cost tracking, cost estimation, and revenue optimization. Continuous monitoring allows for the implementation of money-saving techniques to increase overall profitability. These research initiatives collectively provide valuable insights and practical strategies for Sri Lankan cattle farmers, ultimately fostering a sustainable and thriving livestock industry. The integration of advanced technology and data- driven approaches holds the promise of elevating productivity, disease management, and financial sustainability within the cattle farming sector.