Author(s)

Runbo Zhang

Corresponding Author

Runbo Zhang

Abstract

This article focuses on the field of smart farming, and is committed to building a health early warning and decision support model based on ML (Machine learning) algorithm, IoT (Internet of Things) and AI (Artificial intelligence) to cope with the dilemma of traditional farming mode. Through IoT technology, multi-source big data such as breeding environment and animal signs are collected comprehensively, and the data are preprocessed by statistical analysis and feature engineering. Then, the health early warning model is built with the help of random forest algorithm, and the decision support model is built with decision tree algorithm as the core and multi-source information. Verified by the actual data of several farms, the accuracy of the health early warning model is 90.5% and the recall rate is 88.2%. The decision support model can reduce the average feed cost of farms by 10% and increase the breeding income by 15%. The research shows that the fusion model is highly accurate and practical in smart farming, which can provide effective decision support for aquaculture practitioners and help the smart farming industry to achieve efficient and scientific development.

Keywords

Smart Farming; Machine Learning Algorithm; Internet of Things; Health Early Warning Model; Decision Support Model