Machine Learning in Agriculture
According to the United Nations, the world's population will increase by 2 billion people by 2050, and food productivity will need to rise by approximately 60 per cent in order to meet population demands. With only one growing season per year, farmers are on an accelerated timeline to meet the demand for food — all while addressing the issues of food sustainability, plant and animal welfare, and climate change. In order to keep up, the agriculture industry needs to adopt new innovations including machine learning and data driven solutions.
What is Machine Learning?
Machine Learning is the application of data and algorithms to help machines mimic the way that humans learn and make decisions. It works by identifying 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. Machine learning is a subset of Artificial Intelligence (AI) which describes the intelligence demonstrated by machines, as opposed to natural human intelligence.
Though it is not always apparent, machine learning assists in plenty of our everyday tasks. It is the underlying technology that powers most of our smartphone apps including virtual assistants like Siri, traffic prediction patterns on Google Maps, and Netflix recommendations. It controls autonomous vehicles, machines that can diagnose medical conditions, and robo advisors who manage our financial portfolios. We use machine learning to make existing human tasks better, faster, and easier than before.
How does Machine Learning work?
At its core, machine learning is using 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 is provided to the model to make predictions based on knowledge it learned during training.
Machine Learning can be simplified into the following steps:
Machine Learning Applications in Agriculture
Recent years have seen rapid changes in methods of data collection, analysis, and interpretation in the agriculture industry. Technology such as electronic sensors, processors, and drones are becoming increasingly accessible and affordable, providing much more insight into the farm environment and data which can be analyzed. As the volume of farm data continues to increase, there is a growing opportunity to adopt machine learning and data driven models to all stages of agricultural production from pre-harvest to post-harvest.
Smart farming and precision agriculture is the first step towards farm management that utilizes this data to increase farm productivity and decision making. Rather than basing decisions per field, these technologies allow decisions to be made per square meter, management zone, or even as granular as per plant and animal. Precision agriculture predicated on machine learning technology has the potential to increase production, improve product quality, reduce labour time, lower yield variation, and lessen the impact on the environment. The more data analytics a farmer has access to, the better the opportunity to make informed decisions that will affect the profitability and long term viability of the farm.
Some examples of machine learning applications within agriculture include yield prediction and mapping, satellite imagery used to analyze crop conditions, soil property analysis, estimating risk of diseases, crop identification, and autonomous farming equipment operation.
This article was originally written by Algo-Rythmn Corp. for publication in Old’s College Horizon Magazine - you can read the original version HERE.
