A layer-sensitivity based artificial neural network for characterization of oil palm fruitlets

ABSTRACT This paper presents an intelligent means of addressing characterization and grading problems in the oil palm industry for the purpose of quality control. A Layer-Sensitivity Based Artificial Neural Network (LSB_ANN) which updates its layer weights based on sensitivity analysis was designed to predict the oil content and dielectric constant of mature oil palm fruitlets. The LSB_ANN was designed, optimized and trained with 604 data points obtained from laboratory microwave coaxial sensor measurements within 2-4 GHz. The performance evaluation of the model when tested with a separate set of data showed that the properties of the fruitlets were accurately modeled. To further investigate the generalization ability of the trained neural network, three other neural network training algorithms were deployed for the same dataset. A multi-criteria evaluation of the performances of the networks showed that the proposed LSB_ANN outperformed the other three in generalization accuracy, time and computing resources. The LSB_ANN therefore represents a handy tool for rapid and intelligent characterization of oil palm fruitlets for quality control and research purposes.