CROP YIELD PREDICTION IN PRECISION AGRICULTURE USING MACHINE LEARNING TECHNIQUES: A STUDY - Rakesh Kumar Ray , Sujata Chakravarty , SMIEEE (International Journal of Modern Agriculture, 2020)
In the field of agriculture, the accurate estimation of yield production is crucial for farmers and Govt. Remote sensing plays an important role which collects data from the field level, that is been deployed in the contemporary farming system for building decision-making tool, which can predict accurate yield production and other field level parameters by minimizing operation cost. The remote sensing approach generates a lot of data gathered from different platforms, so it becomes essential to introduce the Machine Learning technique to manage the huge data. Machine Learning has the capability to analyze those huge numbers of inputs and handle the non-linear task to produce knowledge, which can use in decision making. This paper discussed several research works for irrigation control using machine learning techniques, plant disease monitoring, accurate yield production. This paper concludes that the hybrid machine learning technique will give an effective and accurate model for predicting yield production by using remote sensing for decision making and environment state estimation in agriculture.