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An Embedded Computer Vision System for Beans Quality Inspection

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Robson A.G. Macedo, Peterson A. Belan, Sidnei A. Araújo

Robson A G Macedo, Peterson A Belan and Sidnei A Araújo. An Embedded Computer Vision System for Beans Quality Inspection. International Journal of Computer Applications 175(24):44-53, October 2020. BibTeX

	author = {Robson A.G. Macedo and Peterson A. Belan and Sidnei A. Araújo},
	title = {An Embedded Computer Vision System for Beans Quality Inspection},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2020},
	volume = {175},
	number = {24},
	month = {Oct},
	year = {2020},
	issn = {0975-8887},
	pages = {44-53},
	numpages = {10},
	url = {},
	doi = {10.5120/ijca2020920779},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Despite the importance of agricultural grains appearance for their choice by the consumers as well as for determining their selling price, the visual inspection of the quality of these products is usually conducted in a manual way and, therefore, susceptible to high operational costs, human errors and inaccurate results. Recently, a computer vision system for quality inspection of beans composed by a set of hardware and software, named the SIVQUAF, was proposed in the literature. However, the software of the SIVQUAF was designed for a personal computer, which makes its operation more complex, decreases its performance and raises the cost of the equipment. Thus, in this work we explored the customization and optimization of SIVQUAF aiming its running on a Raspberry Pi 3, keeping similar performance, generating the SIVQUAF(Compact. Besides redesigning and parallelizing algorithms, we proposed improvements in the classification and defect detection steps, and a new touch-sensitive interface. The experiments conducted with SIVQUAF(Compact embedded in a Raspberry Pi 3 demonstrated that in addition to reproducing high hit rates in the tasks of segmentation (97.50%), classification (97.06%) and detection of defects (74.78% ), there was a significant gain in terms of cost, operation and compaction of the equipment, increasing its operational, technical and economic viability.


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Embedded System; Raspberry; Computer Vision; Visual Inspection; Bean