Soft computing-based method for estimation of almond kernel mass from its shell features

Abstract

Fast and, at the same time, accurate methods for measuring physical properties of agricultural products are constantly needed to meet customer demand. Achieving evenly graded products based on their masses, as an important physical parameter, not only adds to customer satisfaction but also effects prices. The study analyzed the performance of the multi-linear regression (MLR) model and four different machine learning models—i.e. multilayer perceptron neural network (MLP-NN), radial basis function neural network (RBF-NN), adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM)—in order to compute the kernel mass of three almond varieties without breaking the shell. The best predicting model was used as the almond classifier into different mass classes (very small to large). The three main dimensions (length, maximum width, and maximum thickness) of nuts were manually measured and were used as the parameters for developing the models. Results showed that the intelligent system could successfully predict mass; however, RBF-NN (RMSE: 0.05-0.07 g; MAPE: 3.87–7.20 % for the training phase and RMSE: 0.05-0.06 g; MAPE: 3.84–6.77 % for the testing phase) had better results than other methods in prediction of the mass. The most overall accuracy of the RBF-NN classifier was 96.22 %. Considering the prediction accuracy and computational efficiency, RBF-NN is strongly recommended for non-destructive estimation of almond kernel mass.