CROP YIELD PREDICTION IN PRECISION AGRICULTURE USING MACHINE LEARNING TECHNIQUES: A STUDY

In the field of agriculture, the accurate estimation of yield production is crucial for farmers and Govt. Remotesensing plays an important role which collects data from the field level, that is been deployed in thecontemporary farming system for building decision-making tool, which can predict accurate yield productionand other field level parameters by minimizing operation cost. The remote sensing approach generates a lot ofdata gathered from different platforms, so it becomes essential to introduce the Machine Learning technique tomanage the huge data. Machine Learning has the capability to analyze those huge numbers of inputs and handlethe non-linear task to produce knowledge, which can use in decision making. This paper discussed severalresearch works for irrigation control using machine learning techniques, plant disease monitoring, accurate yieldproduction. This paper concludes that the hybrid machine learning technique will give an effective and accuratemodel for predicting yield production by using remote sensing for decision making and environment stateestimation in agriculture.