Nondestructive Determination Of Beans Water Absorption Capacity Using CFA Images Analysis For Hard-To-Cook Evaluation

Hard to cook (HTC) phenomenon is developed by storing bean grains under the adverse conditions of high temperature (≥ 25 °C ) and high humidity (≥ 65 %).  Bean grains that have undergone this HTC phenomenon are characterized by loss of color lightness, development of  browning and darkening,  and decrease of Water Absorption Capacity (WAC). The objective of this study was to develop a CFA (Color Filter Array) image processing system to measure Water Absorption Capacity (WAC) of bean grains with high precision in short time intervals (10 min). The relationships between the CFA image features, extracted from raw images captured by CCD (charge coupled device) camera, and the measured WAC were established. The calibration models using multiple linear regression (MLR) were developed to predict WAC. The MLR models for prediction samples resulted in correlation coefficient (R2) in the range of 0.811 to 0.947, standard error of prediction (SEP) in the range of 7.587 to 11.669, and Fisher variable value (F)  in the range of 52.300 to 221.690. Results indicate that computer vision system (CVS) based on CFA image analysis technique can provide an accurate, reliable and nondestructive measurement method of WAC to evaluate the hard to cook defect in bean grains.DOI:http://dx.doi.org/10.11591/ijece.v3i2.214

Classification of Pepper Seeds by Machine Learning Using Color Filter Array Images

The purpose of this work is to classify pepper seeds using color filter array (CFA) images. This study focused specifically on Penja pepper, which is found in the Litoral region of Cameroon and is a type of Piper nigrum. India and Brazil are the largest producers of this variety of pepper, although the production of Penja pepper is not as significant in terms of quantity compared to other major producers. However, it is still highly sought after and one of the most expensive types of pepper on the market. It can be difficult for humans to distinguish between different types of peppers based solely on the appearance of their seeds. To address this challenge, we collected 5618 samples of white and black Penja pepper and other varieties for classification using image processing and a supervised machine learning method. We extracted 18 attributes from the images and trained them in four different models. The most successful model was the support vector machine (SVM), which achieved an accuracy of 0.87, a precision of 0.874, a recall of 0.873, and an F1-score of 0.874