Cocoa Beans Classification Using Enhanced Image Feature Extraction Techniques and A Regularized Artificial Neural Network Model.

The Cut-Test technique employs visual inspection of interior colouration, compartmentalization, and defects of beans for effective classification of cocoa beans. However, due to its subjective nature and natural variances in visual perception, it is intrinsically limited, resulting in disparity in the verdict, imprecision, discordance, and time-consuming and labor-intensive classification procedure. Although machine learning (ML) techniques have been proposed to fix these challenges with significant results, there is a need for improvement. In this paper, we propose a color and texture extraction technique for image representation as well as a generalized, less complex, and robust Neural Network model to help improve the performance of machine classification of Cut-Test cocoa beans. A total of 1400 beans were classified into 14 grades. Experimental results on the equal cocoa cut-test dataset, which is the standard publicly available cut-test dataset, show that the novel extraction method combined with the developed artificial neural network provides a more homogeneous classification rate for all the cocoa grades. The proposed model outperformed the Support Vector Machine, Decision Tree, Random Forest, and Nave Bayes on the same dataset. The proposed techniques in this work are robust on the cut-test dataset and can serve as an accurate computer-aided diagnostic tool for cocoa bean classification.