Oil Palm Maturity Classification System
In this chapter, the methodology of a non-destructive oil palm fresh fruit maturity classification system will be discussed in detail.
Figure 3. 1: System block diagram This system consists of sensing device that has connection with the Arduino and its peripherals. The acquired data will be transferred to MATLAB and Simulink with Arduino Hardware in Simulink. An algorithm is designed and modelling for data by Artificial Neural Network (ANN) model.
3. 2 Oil Palm Maturity Classification System – The V Diagram
The V diagram or known as V- model is a term applied to a range of models with start form a conceptual model designed to produce a simplified understanding the complexity of development lifecycle models and project management models.
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The acquired spectrum will be used to train the supervised learning Neural Network Toolbox in Simulink and construct an experiment to verify the ANN trained model can classify the oil palm maturity.
3.4. 3 Data processing
Artificial Neural Network (ANN) model will be used to manage acquired data. It will process the selection of the input and the output. Besides, the system will trained with this model. The model will be tested. In the end, compare the both actual output and predicted output for accuracy.
3.4. 4 Performance
The accuracy of the oil palm fresh fruit maturity classification system will be evaluated by using Mean Square Error (MSE) method. The MSE assesses the quality of a predictor (a function mapping arbitrary inputs to a sample of value of some random variable) MSE= 1/N Σ_(i=1)^N 〖(Y_t-Y_N)〗^2
where Y_t=target or actual output , Y_N=predicted output, N = number of point.
3.4. 5 Result validation
The oil palm fresh fruit will be provided by MPOB or Sime Darby with 3 major classes: unripe, ripe and under ripe. The data will be acquired for these 3 maturities and trained to
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