The Importance of Feedforward Neural Network in Developing Small Ruminant Breed Lineage Prediction System

Sheep are crucial to Malaysian Muslims, which accounts to 60% of the population. Yet there is not enough supply due to a high death rate brought on by diseases like Tetanus and Foot and Mouth Disease (FMD), among others. Objective of the study is to create a uniform data collection system that can be adopted across farms. The system will be developed using a standard web-development framework and will be utilizing the Feedforward Artificial Neural Network (FANN) to process the data. The outcome of the system will be based on accuracy, time consumption and reliability. The results of this study will be displayed in diagrams and user interface of the system. Although certain farms recorded their data digitally in separate database tables and Excel sheets, bulk of the data was recorded in one master sheet and one master table from a certain point in time. This means that there is movement in staffing where previous worker resigns and new worker is hired without any proper handover between the two which creates inconsistent data being recorded. History could not be established and data from old records could not be synced with newly recorded data as the values differ from each other. This paper focuses of sheep farms around Peninsular Malaysia. Future areas of research may include adopting our system on other types of farm animals. Analyzed data from our study will be integrated with our Feedforward Neural Network algorithm.