Abstract occupation across the world in the next


            Agriculture is
one of the main income producing sectors in India. A mixture of biological,
seasonal and economic factors manipulate the production of the crops, but random
changes causes a great loss to the farmers. By applying appropriate
mathematical or statistical methodologies in the soil and weather data, these
risks can be computed. Machine learning helps to forecast the crop yield by
obtaining the valuable information from the agricultural data  to make a decision on the crop for farmers, so
that they can plant for the future which leads to huge profit. In this paper,
we present a detailed study on the various machine learning algorithms that
facilitate to forecast the crop yield.

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Machine Learning, Data Mining, Crop yield, Forecasting, Agriculture


forms the important source for food security. It assists human beings to grow
the best food crops. Rice and wheat is the primary food  in India. Indian farmers grows the following
foods such as rubber, cotton, potatoes, pulses sugarcane, oilseeds. Agriculture
is depended by 70 per cent of the rural family. Total GDP of 17% is contributed
in agriculture.  60% employment is
provided over the population. A machine learning technique helps to make decisions
automatically by detecting pattern from the past data and generalizing it on
the future data. Machine learning
algorithms are anticipated to replace 25% of the occupation across the world in
the next 10 years.

 The following are the 3
major categories of Machine Learning algorithms

In Supervised Learning, we have input
and output variables and the algorithm create a function that calculates the
output based on given input variables. Regression
and Classification are the two parts: Some examples include
Linear Regression, Decision Trees, Random Forest, k nearest neighbours, SVM,
Gradient Boosting Machines (GBM), Neural Network etc.

 In Unsupervised learning, only input data is present
and there is no corresponding output variable. It can also be classified into
two groups, namely Cluster analysis and Association. Some examples would be
k-means clustering, hierarchical clustering, PCA, Apriori algorithm, etc.

 In Reinforcement
learning, the machine is given training to make accurate decisions from these
actions and tries to capture the best possible knowledge. Some  examples are Weather forecast, Speech Recognition, Game playing, face detection/Face
recognition, Genetics and agriculture.

The rest of the paper is organized as
follows: Chapter II explains the methods of Machine Learning. Chapter III
describes about the applications of Machine Learning used in agriculture
domain. Chapter IV analyses the outcomes. Chapter V discusses the conclusion.