Intelligent classification of cocoa bean using E-nose

Cocoa bean (Theobrama cacao) is an essential raw material in the manufacture of chocolate, and their classification is crucial for the synthesis of good chocolate flavour. Cocoa beans appear to be very similar to one another when visualised. Hence, an electronic device named the electronic nose (E-Nose) is used to classify the odor of cocoa beans to give the best cocoa bean quality. E-nose is a set of an array of chemical sensors used to sense the gas vapours produced by the cocoa bean and the raw data collected was kept in Microsoft Excel, and the classification took place in Octave. They then underwent normalisation technique to increase classification accuracy, and their features were extracted using mean calculation. The features were classified using CBR, and the similarity value is obtained. The results show that CBR's classification accuracy, specificity and sensitivity are all 100%

A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System

Case-based reasoning has been a widely-used approach to assist humans in making decisions through four steps: retrieve, reuse, revise, and retain. Among these steps, case retrieval plays a significant role because the rest of processes cannot proceed without successfully identifying the most similar past case beforehand. Some popular methods such as angle-based and distance-based similarity measures have been well explored for case retrieval. However, these methods may match inaccurate cases under certain extreme circumstances. Thus, a triangular similarity measure is proposed to identify commonalities between cases, overcoming the drawbacks of angle-based and distance-based measures. For verifying the effectiveness and performance of the proposed measure, case-based reasoning was applied to an agricultural decision support system for pest management and 300 new cases were used for testing purposes. Once a new pest problem is reported, its attributes are compared with historical data by the proposed triangular similarity measure. Farmers can obtain quick decision support on managing pest problems by learning from the retrieved solution of the most similar past case. The experimental result shows that the proposed measure can retrieve the most similar case with an average accuracy of 91.99% and it outperforms the other measures in the aspects of accuracy and robustness