Application of Machine Learning Techniques for Yield Prediction on Delineated Zones in Precision Agriculture

Abstract

Precision agriculture is the implementation of the recent technology in agriculture. Huge amount of data is collected in agriculture and various techniques of data mining are used to make efficient use of it. In this paper, we have discussed how with the help of both, clustering and classification algorithms, the crop suitable for a particular piece of land can be determined. Management zone delineation is a key task in this. From a data-mining point of view this comes down to variant of spatial clustering which has a constraint of keeping the resulting clusters spatially mostly contiguous. We analyze the need to discretize and normalize the data set and the various techniques that are used for the same. Further, a comparative analysis of the algorithm is shown where it can be seen which algorithm is best suited. We also talk about the future scope of the same and how these could actually be implemented in the real life scenarios.