December 16, 2022
Here are the Top Applications
With the global population expected to reach 8 billion by the end of 2022, it is imperative that more efficient ways of farming are discovered to fulfill man’s basic need — food. Farmers are thus put under a lot of pressure to adopt better methods and minimize risks in order to meet the growing demand. When there is a need to move beyond traditional farming, the answer lies in modern agriculture.
Modern Agriculture: Using Technology in Today’s Farming
Modern agriculture generally refers to the implementation of farming innovations and techniques with the use of technological tools and reliable data in order to boost efficiency. By analyzing real-time sensor data and comparing these with historical trends, agriculturists can make insightful decisions on tasks like performing intelligent spraying, implementing crop rotation, developing better tillage practices, and in general, improving crop yield.
Automated agriculture, also known as farm automation or smart farming, is one approach to modern agriculture that utilizes drones and robotics to create autonomous tractors, seeders, harvesters, and waterers in crop production. These technologies work together with AI subfields such as deep learning, computer vision, and machine learning, not only to automate farming processes but also to make better predictions.
For instance, deep learning in agriculture can process years’ worth of raw field data such as crops’ performance in various climates or identifying certain leaf defects — and use this to create a probability model. Then machine learning can utilize the available information to further narrow the search, and develop a more accurate model that helps predict improved crop yields, identify plant diseases, and more.
Key Applications of Machine Learning in Agriculture
The AgTech market in North America reached a noteworthy 6.2 billion US$ in the year 2021 alone. This isn’t surprising at all considering how beneficial machine learning has proven to be in modern agriculture. Here are five of its top applications:
Greater Possibilities with AI in Agriculture
The contributions of deep learning, computer vision, and (especially) machine learning in agriculture are already recognized, and the applications can only become more groundbreaking and complex moving forward. Data-driven approaches promote accurate and insightful decision making that, in turn, leads to improved efficiency and higher productivity.