An improved multilayer perceptron approach for detecting sugarcane yield production in IoT based smart agriculture

摘要

Internet of Things (IoT) as one of most powerful technologies can provides precision management and intelligent navigation for managers and manufacturing plants? Smart agriculture to deal a good strategy for improving agricultural productions and maximizing farm efficiency. Sugar production is subsidiary to many diverse and various parameters. Due to a diverse variety of parameters and the lengthy process in precision agriculture, the analytical prediction is difficult and impossible. In such situations, using intelligent systems such as machine learning may be proposed as an alternative solution. This paper proposed an improved Multilayer Perceptron (MLP) approach to predict the amount of sugar yield production in IoT agriculture. Experimental results show that the proposed MLP algorithm has maximum accuracy of 99%, precision of 95%, recall of 96% and Minimum Mean Absolute Error (MAE) of 0.04% and Root mean square error (RMSE) of 0.006% for detecting sugarcane yield production in IoT Agriculture.