Machine learning technology is growing day by day in different sectors for analysis and prediction with the help of training data. So, training data are a key factor for machine learning. It tells us about the use of AI, so it is also used in agriculture.
Before applying any data for prediction through machine learning, some basic factors are needed.
First factor
Machine learning needs knowledge about around-the-world activities and tasks for training computers. It can explore themselves to educate themselves.
Second factor
This factor is digital data or information collected and made accessible for the analytics process.
Third factor
The third factor is where digital changes are made available for all technology-based environments and devices.
See one example of the latest technology. Technologies and deep learning algorithms on a drone are used to collect the data of crops and soil monitored by a software. Fertility of the soil is controlled using the software. Some companies are developing robots and automation tools for agriculture field to form effective ways to save a crop and also to protect them from weeds. Agricultural spray machines are designed for spraying accurate weedicides on the plants and in accurate amount to reduce expenditures.
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