Pixel Count Based Yield Estimation Model, to Reduce Input feature required in Machine Learning System for Major Agricultural Crop

Abstract

Traditionally, the crop analysis and agricultural production predictions were done based on statistical models. However, with the climate of the world changing to drastic degrees, these statistical models have become very ambiguous. Hence, it becomes prudent that we resort to other less vague methods. Through a traditional model, user interacts primarily with a mathematical computations and its results and helps to solve well-defined and structured problems. Whereas, in a data driven model, user interacts primarily with the data and helps to solve mainly unstructured problems. At this point, enters the concept of Machine Learning. In this work we tried to find a new approach to reduce the input feature to reduce the processing power needed. We have attempted at predicting the agricultural outputs of rice production in an area by implementing a pixel count based classification machine learning model. Through this model, we tried to predict the approximate crop yield based on NDVI values analyzed for a particular season and area.