Can Machine Learning (ML) algorithm help to increase agriculture produce? Machine learning (ML) methods have developed as alternatives as well as complementary tools for agricultural and related modelling. Machine learning techniques are rapidly being applied in yield forecasting, agricultural production, as well as forest management studies. In artificial intelligence, machine learning refers to the capability of a computer to learn from examples and past experiences on its own, without any prior instruction from a human.
In computer science, machine learning refers to a set of techniques that allow software programmes to get better at predicting the outcomes of research-related systems. Making algorithms that utilise statistical analysis to anticipate an output and then update those predictions when new data becomes available is central to machine learning. Two components of ML that are widely employed to address complicated challenges when human expertise fails to do so are the ability to continually improve with increasing precision and the extraction of more information from enormous data sets.