RICE YIELD CLASSIFICATION USING BACKPROPAGATION NETWORK

Abstract

Among factors that affect rice yield are diseases, pests and weeds. It is intractable to model the correlation between plant diseases, pests and weeds on the amount of rice yield statistically and mathematically. In this study, a backpropagation network (BPN) is developed to classify rice yield based on the aforementioned factors in MUDA irrigation area Malaysia. The result of this study shows that BPN is able to classify the rice yield to a deviation of 0.03.