Machine learning techniques are being used in agriculture to analyze large amounts of data and make predictions about various agricultural operations without being explicitly programmed. There are two main types of machine learning used - supervised learning is used for crop yield prediction by training models on labeled datasets, while unsupervised learning is used for soil analysis by finding patterns in unlabeled datasets. Machine learning has the potential to revolutionize agriculture by improving efficiency, reducing costs, and increasing yields.

1. MACHINE LEARNING
TECHNIQUES IN
AGRICULTURE

2. Machine learning is a subfield of artificial intelligence that
involves the development of algorithms and models that
can learn from data to make predictions or decisions
without being explicitly programmed to do so.
 In agriculture, machine learning is used to analyze large
amounts of data and make predictions or decisions about
various aspects of agricultural operations, including crop
yield, soil analysis, pest and disease control, irrigation
management, livestock monitoring, and precision
agriculture.

3. • There are several types of machine learning,
supervised learning, unsupervised learning
In supervised learning, a model is trained on a
labeled dataset, where the desired output is already
known. This type of machine
learning is often used for crop yield prediction
In unsupervised learning, the model is trained on an
unlabeled dataset and must find patterns and
relationships in the data without any prior knowledge
of the desired output.
This type of machine learning is often used for soil
analysis

4. machine learning has the potential to revolutionize
agriculture by improving efficiency, reducing costs, and
increasing yields, making it an exciting area of research
and development.

5. THANK YOU