Recommender system for animal breeds

Abstract

The main objective of this project was aimed at developing a real-time hy-brid system that recommends good breeds for the farmers , locates and keeps track of breed history that is analyzed so as to easily determine the effectiveness of a successful genetic improvement. Animal genetic improvement offers one of the most efficient and quickest ways of improving the productivity of dairy herds, its effective exploitation has not been achieved due to lack of a well planned and executed breeding program despite the unrelenting attempts to upgrade the national dairy herd. The approaches used to solve this problem included; gathering requirements, designing, implementing, testing and validating the system. Information for system study was gathered using tools such as interviews, observation, questionnaires and study of relevant literature. DFDs and ERDs were used in the analysis and design of the system. The technologies used in the implementation of the objectives of this study included, Java, PHP, CSS, HTML, Django,Python , Android studio) and the Database Management System employed was MySQL. The outcome of the project was an integrated system capable of locating Breeders depending on GPS to determine the location of the animal breed, Recommending a good breed for the farmer,Finding best matches for the breed. Upon successful testing and system validation, we believe that the system can automate Animal Breeding effectively and efficiently. This report describes the development of a Recommender system designed to provide up to date with visualization of the Breed's performance ,location and recommendation. It describes the problem being addressed, objectives intended for the project and a review on the functionality of existing related systems. The report also provides a full description of the design and implementation of the recommender system for animal breeds.