How is ML used in agriculture?

ML is used in agriculture to help farmers more accurately predict crop yields, optimize irrigation systems, and choose the most efficient agricultural production strategies. In recent years, machine learning algorithms have been used to develop new ways to identify pests and diseases and to map crops more accurately.

What is the ML process?

The ML process is a data-driven approach to finding patterns in data that can be used to make predictions. The process starts with collecting data, cleaning and preparing it for analysis. Next, various ML algorithms are applied to the data in order to find patterns. Finally, the results of the analysis are used to make predictions.

What does IoT stand for in agriculture?

IoT stands for Internet of Things, the growing network of physical objects connected to the internet. It includes everything from farm equipment to sensors to animals. In agriculture, IoT devices can track data and optimize production.

Machine Learning In Agriculture: 13 Use Cases, Core Technologies & Benefits