Non-destructive low-cost approach for fuzzy classification of tomato images based on firmness prediction using regression

Abstract

Non-destructive techniques such as hyperspectral imaging, backscattering imaging are the advanced techniques used for predicting mechanical properties of horticulture products. They show relatively good performance but at the expense of costly measuring setups. This application-oriented paper investigates the feasibility of employing simple digital color camera imaging for prediction and fuzzy classification of firmness of tomatoes. Images acquired using digital color camera are preprocessed and subject to texture analysis in order to extract the number of features. The proposed approach exploits four texture feature extraction algorithms: three are based on statistical techniques viz. first order statistics (FOS), gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), and one is based on transform-based technique viz. wavelet-transform. Out of all extracted features, redundant features are eliminated using various attribute selection methods. Subsequently, prediction models are built and analyzed using regression analysis. Sample space has been split into two sets; 80% training and 20% testing data having tomatoes with almost identical formation. Experimental results illustrates that RBF regression gave the lowest RMSE of 0.174 and highest prediction correlation coefficient of 0.929 for wavelet feature set. Grounded on the prediction model, fuzzy rule based classification (FRBC) is proposed to classify tomatoes into three firmness categories soft, medium, and hard. Accuracy statistics of the proposed FRBCS are compared with the state-of-the-art result and highest classification accuracy of 92.68% is achieved by proposed FRBCS. The results exhibit the possibility of using a digital color imaging system for firmness estimation and further for classification.