Application of Machine Learning in Agriculture and Livestock Production

Machine learning is the field of study that gives computers the ability to learn from data without being explicitly programmed (Arthur Samuel, AI pioneer, 1959).

Today, applications of machine learning are everywhere and are increasingly growing. Machine learning approaches don’t require any assumption about the distribution of data. They are very robust to missing values and outliers and are performing well in non-linear systems where complex relationships exist between predictor features and the outcome.

In this introductory lecture, we aim to introduce a few machine learning methods and briefly show their applications in agriculture and livestock production.

Bio

Dr Saleh Shahinfar has over 15 years of experience in machine learning and predictive models in the livestock industry. He has worked in Iran, the United States and Australia. His speciality is machine learning applied to the dairy industry, and to a lesser extent in wildlife conservation, beef, sheep, and wheat industry. He is currently working on the prediction of wide range of traits in wheat and dairy cattle via Machine/Deep Learning using various high-throughput data sources such as SNP, NIR and LC-MS proteomics data.

Zoom link – link will be provided by a calendar invite