Prediction of Sulfur Content in Copra Using Machine Learning Algorithm

Abstract

Coconut copra is the white stout inside a coconut. Besides coconut oil, coconut copra has become a trendy snack and ingredient in cooking, owing to its numerous health merits. A good quality coconut without any infections is maintained by the farmers by employing the procedure of sulfur fumigation over the coconuts. The usage of sulfur is poisonous, and the pollution caused by burning of sulfur is toxic. This sulfur addition creates breathing, skin problems for the consumers. The proposed method is intended to make sure the availability of good quality coconut in the market by assessing the quality of each individual sample going into the production line. The sulfur content in the copra is predicted by the feed-forward machine learning technique. The features of dissimilar kinds of copra are examined and are used to train the machine model. Simulation of the proposed work is carried with MATLAB. From the validation and testing, it is found that 70% of the samples are trained; among them; 15% are validated and 15% are tested. Results indicate that 96.5% accuracy is obtained from the validation.