Журнал компьютерных наук и системной биологии

Using machine learning techniques in smart farming is gaining momentum worldwide. The main goal is to achieve better production by predicting right crop considering present conditions of weather and soil. The climatic changes that are being uncertain results in reduced yield when the farmers follow traditional way of growing crops. The features of soil and conditions of the weather change time to time and this criteria when concentrated leads to precision farming. The study implements machine learning techniques to predict the right crop for cultivation, expecting better yield, taking into account the changes in the weather and soil every time, as and when the farmer aims for growing a fresh crop.

The paper introduces Monte Carlo Area Algorithm using Mat lab simulation to generate uniform random distribution points and evaluate the area accordingly and analyze the error. The method to calculate the error had done by comparing the real area value and estimated area values the repetition of the algorithm introduce superior efficiency and reduce the difference between real area value and estimated which considered as error. The relation between Error and number of random points is obviously an exponential relation which affected by number of repetition and random points. The true challenge in such studies is to limit the deviation in the exponential relation between error and number of random points.