Texture and Color-Based Analysis to Determine the Quality of the Manila Mango Using Digital Image Processing Techniques

Abstract

This work shows the development of an algorithm based on image processing techniques and aims to determine the quality of Manila mango for export purposes. Currently in Mexico, in most of the mango producing states, the analysis of this fruit is done manually and the quality of the mango is determined by considering the state of maturity, cleanliness of the mango´s skin and size. During this process, the fruit is granding and then classified; in these tasks, human errors can occur due to fatigue. The consequences of these errors translate into losses for farmers, since for one fruit detected to be of poor quality, the entire lot is rejected. Therefore, any attempt made to support the determination of the quality of this fruit in an automated way will be of great support, especially for small and medium-sized agricultural enterprises (SMEs). The methodology employed uses digital image processing techniques by analyzing color and texture, calculating the mean of the components and using statistical methods (histograms and co-occurrence matrices) in the regions of interest, in addition to applying the support vector machine (SVM) algorithm to classify Manila mango based on maturity and peel damage. The algorithm presented here details a process of obtaining the image to filter it and identify the edges of the mango, the representation and manipulation through the histogram is used to improve the image without affecting aspects that may be relevant in the image such as contours, textures and intensity. These characteristics will be used later to determine the quality of the fruit. The results shown so far are satisfactory, reaching 86% Accuracy.