Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble.

Jessica Fernandes Lopes,Sylvio Barbon,Douglas Fernandes Barbin,Leniza Ludwig,Maria Victória Eiras Grossmann

doi:10.3390/s19132953

Abstract

Imaging sensors are largely employed in the food processing industry for quality control. Flour from malting barley varieties is a valuable ingredient in the food industry, but its use is restricted due to quality aspects such as color variations and the presence of husk fragments. On the other hand, naked varieties present superior quality with better visual appearance and nutritional composition for human consumption. Computer Vision Systems (CVS) can provide an automatic and precise classification of samples, but identification of grain and flour characteristics require more specialized methods. In this paper, we propose CVS combined with the Spatial Pyramid Partition ensemble (SPPe) technique to distinguish between naked and malting types of twenty-two flour varieties using image features and machine learning. SPPe leverages the analysis of patterns from different spatial regions, providing more reliable classification. Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), J48 decision tree, and Random Forest (RF) were compared for samples’ classification. Machine learning algorithms embedded in the CVS were induced based on 55 image features. The results ranged from 75.00% (k-NN) to 100.00% (J48) accuracy, showing that sample assessment by CVS with SPPe was highly accurate, representing a potential technique for automatic barley flour classification.

Highlights

Barley is one of the most ancient cereal crops grown by humanity [1]
The results of algorithm performance for the classification of naked and malting barley flour revealed the advantages of the proposed Spatial Pyramid Partition ensemble (SPPe) method, in comparison to Spatial Pyramid Partition (SPP) and traditional approaches
The experiments showed distinct performance values achieved with the techniques applied to this approach using machine learning algorithms

Summary

Introduction

Some barley cultivars (e.g. malting or hulled barley) were selected for the malt and brewery industry, while other cultivars were selected to be used as food ingredients. These last cultivars are known as naked, or even hull-less or uncovered barley, generally containing higher amounts of soluble fiber [2,3]. Requirements concerning barley characteristics are quite different for malting and food industries. Grains with a low β-glucan concentration and barley kernels with a tough inedible outer hull still attached are required. High β-glucan levels interfere negatively in the malting filtration process. The loss of husks during malting processes leads to a reduction in malt quality

Methods

Results

Conclusion