Digital Ag Collaborations
Machine Learning on the Smart Farm According to the United Nations, the world’s population will increase by two billion people by 2050, and food productivity will need to rise by approximately 60 per cent. With only one growing season per year, farmers are on an accelerated timeline to meet the demand for food — all while addressing food sustainability, plant and animal welfare, labour shortages, supply chain challenges, and climate change. Throughout this growing season, Olds College Center for Innovation (OCCI) is collaborating with industry partner AlgoRythmn Corp. to apply machine learning and data-driven solutions to enhance market and financial risk management innovations, so producers can make informed decisions to maximize profitability and minimize risk.
Machine Learning Explained Machine learning is the application of data and algorithms to help machines mimic the way humans learn and make decisions. It identifies patterns in the data to generate structure and predictions without the need for human intervention. Using various datasets, machine learning algorithms autonomously improve their performance.
Smart farms and technology innovation have a critical role to play in this ‘global grand challenge’ of feeding a growing population with fewer resources. This creates opportunities for technology, data, and artificial intelligence to help crop and livestock producers sustainably meet increasing demands and manage their risk.
12 Olds College Horizons
Machine learning already enhances many everyday tasks. It is the underlying technology powering most smartphone apps including virtual assistants like Siri, traffic prediction patterns on Google Maps, and Netflix recommendations. It also controls autonomous vehicles, machines that diagnose medical conditions, and robo advisors who manage financial portfolios. At its core, machine learning uses data to answer questions. ‘Using data’ is typically referred to as ‘training’, while ‘answering questions’ is referred to as ‘making predictions’. What connects these two parts together is the model. The model is trained to identify trends and correlations using a dataset. Then, a new unseen dataset makes predictions based on what it learned during training.