A plum selection system that uses a multi-class Convolutional Neural Network (CNN)

Abstract

Cultivation of the Horvin plum is one of the main economic activities in the region of Márquez, in the department of Boyacá, in Colombia. However, its selection process, which needs to be optimized and improved, is still done manually and leads to delays, high labor costs and errors in its classification due to long working days. In this paper, an electromechanical system for plum classification into three classes –morphologically defined by the experience of farmers in the region— is proposed, in nutshell: The first category is the largest and most desirable, the second is smaller in size compared to the first, and the third exhibits visual impairments, rendering it undesirable. For this, 1928 samples, distributed in three categories, were entered into a convolutional neural network using the low-cost architecture available for the research. Initially, a classification accuracy of 80% was obtained for the validation data. Ultimately, following a regularization process that included DropOut and data augmentation, the algorithm achieved a 90% accuracy rate on samples not encountered during the training phase. In addition, through the Grad-CAM algorithm, the operation of the CNN was ratified, evidencing the patterns learned in each of the convolutional layers. Considering the above, it was possible to implement a machine vision system capable of classifying plums into three categories using low-cost devices.

Keywords

• Deep learning
• Fruit selection
• Grad-CAM
• Machine vision
• Plum classification