Estimating Some Physical Properties of Sour and Sweet Cherries Based on Combined Image Processing and AI Techniques

Abstract

Abstract Physical properties of agricultural products are considered as important factors in optimization of storage conditions, packaging, transportation, water adsorption/desorption, heat, pesticides, and foodstuff moving out and also their breathing. This paper presents a time and cost economizing method to determine these important attributes of sour and sweet cherries by combining image processing and two common artificial intelligence techniques, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS). The measuring technique consisted of a charge-coupled device camera for image acquisition, fluorescent illuminants, capture card, and MATLAB for image analysis. Several networks were designed, trained, and generalized with a back-propagation algorithm using “trainlm” as training function. Several ANFIS models were designed with different number and type of membership functions (MFs) for each input. Generally, “gaussian” and “pi-shaped” MFs showed better results for estimating output variables among others. Considering statistical analysis, ANFIS showed better results than ANN.