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8Machine Learning in Maximizing Cotton Yield with Special Reference to Fertilizer Selection

G. Hannah Grace¹ and Nivetha Martin^2*

¹Division of Mathematics, School of Advanced Sciences, VIT University, Chennai, India

²Department of Mathematics, Arul Anandar College (Autonomous), Karumathur, India

Abstract

The mismanagement of land resources greatly affects the quality of the soil. Land degradation disintegrates the nature of the cultivable land, and in consequence, the yield rate of crops is adversely affected. As a means of supplementation, fertilizers are used to enrich the quality of the soil by preventing the potential loss. This has led to the decision-making circumstances of making suitable selection of fertilizers to rectify the deficiency of the soil and to prevent the potential loss. As a step towards it, this chapter intends to apply Fuzzy c-means clustering (FCM) and random forest algorithm (RFA) to the soil samples collected from the cotton agricultural lands of rural areas. The main objective of this research work is to cluster the fertilizers into compatible and non-compatible based on the attributes considered. The random forest algorithm is applied to rank the compatible fertilizers. The results obtained using the combination of FCM and RFA is validated using a multi-criteria decision-making method. The optimal results of this selection-based decision-making problem will certainly facilitate the farmers in making right decisions on the choice of the fertilizers ...