Machine learning algorithms are being increasingly used in agricultural production, yield prediction, and forest management research. Machine learning is an artificial intelligence application that allows a machine to learn from examples and experiences without explicit programming.
Machine learning refers to a class of methods that enable software programs to become increasingly accurate in predicting outcomes from research-relevant systems. The fundamental idea of machine learning is to create algorithms that can collect input data and apply statistical analysis to predict an output while updating results as new data becomes available.
The primary principle of machine learning is to create algorithms that can take input data and use statistical analysis to predict an output while updating outcomes as new data becomes available.
Extracting additional information and identifying or spotting trends from vast data sets are two components of machine learning that are commonly used to address complex challenges when human expertise fails because they can be continually improved with higher precision.
Together with big data technology and high-speed computers, the developing idea of Machine Learning has offered the new potential to quantify and comprehend data-heavy processes in the new generation of smart farming.
Nowadays, machine learning is used across the agricultural industry, beginning with soil preparation, seed breeding, and water feed monitoring, and ending with robots picking up the crop and judging readiness with the use of computer vision.
Source: krishijagran.com
