Development of a Low-Cost Livestock Sorting Information Management System Leveraging Deep Learning, AI, and IoT Technologies

Abstract

The implementation of effective livestock management methods is crucial to optimize agricultural operations. However, conventional livestock sorting and data management approaches encounter several obstacles regarding precision, labor requirements, and financial implications. The process exhibits inefficiency, increased labor costs, and an elevated risk of zoonotic infections. Housing livestock in extensive groups might intensify the transmission of diseases and complicate the surveillance and management of diseased animals. This study attempted to develop a Low-Cost Livestock Sorting Information Management System (LC-LSIMS) using a dataset enriched with crucial metrics and curated images collected over 24 months with the Internet of Things (IoT) and Artificial Intelligence (AI). The design of edge-cloud computing facilitates the redistribution of computational resources, leading to enhanced computational speed. The LC-LSIMS would have a predictive module to assist agricultural practitioners in safeguarding their crops during flood occurrences. This module will empower farmers to proactively anticipate natural phenomena, including floods, during intense rainfall. LC-LSIMS presents a multi-level design plan that facilitates attaining the specified goals. The findings obtained from the execution of the implemented system demonstrate a sorting accuracy of 91.47%, computational speed of 27.42 frames per second (fps), labor cost reduction of 50.84%, production efficiency improvement of 29.59%, and an average reduction in data input errors of 37.59%.