What is one application of machine learning in agritechnology?

Machine learning plays a significant role in predictive analytics for crop yield forecasting. This application involves using algorithms and models that analyze historical data, such as weather patterns, soil conditions, and crop performance, to predict future yields. By identifying trends and correlations within large datasets, machine learning can provide farmers with actionable insights, helping them to make better-informed decisions regarding planting schedules, resource allocation, and other agricultural practices. This predictive capability enhances efficiency and can lead to more sustainable farming practices, ultimately benefiting both the farmers and the environment.

Other options, while relevant to agritechnology, do not specifically involve machine learning. Crop rotation planning is often based on historical practices and agricultural knowledge rather than complex data analysis. Soil compaction measurement typically relies on physical assessment rather than predictive modeling. Manual pest control is a traditional practice that does not utilize machine learning for decision-making.