Automatic visual inspection of beans quality

• Grant number: 17/05188-9
• Support Opportunities: Regular Research Grants
• Start date: July 01, 2017
• End date: August 31, 2019
• Field of knowledge: Engineering - Electrical Engineering
• Principal Investigator: Sidnei Alves de Araújo
• Grantee: Sidnei Alves de Araújo
• Host Institution: Universidade Nove de Julho (UNINOVE). Campus Memorial. São Paulo , SP, Brazil
• Associated researchers: Cleber Gustavo Dias ; José Carlos Curvelo Santana ; Peterson Adriano Belan

Abstract

For many industrial and agricultural products, their visual properties are important factors for determining the market price and assist the choice of consumers. Basically, the quality inspection of Brazilian beans is done manually following the operating procedures established by the Ministry of Agriculture, Livestock and Supply. However, in manual processes of quality inspection usually occur some problems such as high cost and lack of standardization of results. In this context, it is important the use of computational systems for supporting such processes in order to reduce operational costs and standardize the results, generating competitive advantage to the companies. In this project, we propose the development of a computer vision system (CVS) applicable to the process of visual inspection of beans quality. It needs to able to classify the most consumed beans in Brazil, based on the color and size of the grains and recognizing the main defects. Thus, the proposed CVS can be used for automatically determine the class and the type of product which consequently impact on its market price. In addition to the implementation of the CVS, we intend to further develop a low-cost equipment applicable to the solution of the investigated problem. (AU)

Scientific publications

(5)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

GOMES DE MACEDO, ROBSON APARECIDO; MARQUES, WILSON DAVID; BELAN, PETERSON ADRIANO; DE ARAUJO, SIDNEI ALVES. AUTOMATIC VISUAL INSPECTION OF GRAIN QUALITY IN AGROINDUSTRY 4.0. INTERNATIONAL JOURNAL OF INNOVATION, v. 6, n. 3, p. 207-216, SEP-DEC 2018. (17/05188-9)

BELAN, PETERSON ADRIANO; DE MACEDO, ROBSON APARECIDO GOMES; ALVES, WONDER ALEXANDRE LUZ; SANTANA, JOSE CARLOS CURVELO; ARAUJO, SIDNEI ALVES. Machine vision system for quality inspection of beans. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, v. 111, n. 11-12, NOV 2020. (17/05188-9)

LIMA, STANLEY JEFFERSON DE A.; DE ARAUJO, SIDNEI ALVES; KOROSEC, P; MELAB, N; TALBI, EG. A New Binary Encoding Scheme in Genetic Algorithm for Solving the Capacitated Vehicle Routing Problem. BIOINSPIRED OPTIMIZATION METHODS AND THEIR APPLICATIONS, BIOMA 2018, v. 10835, p. 11-pg., 2018-01-01. (17/05188-9)

BELAN, PETERSON A.; DE MACEDO, ROBSON A. G.; DE ARAUJO, SIDNEI A.; NYSTROM, I; HEREDIA, YH; NUNEZ, VM. Computer Vision Approaches to Detect Bean Defects. PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS (CIARP 2019), v. 11896, p. 10-pg., 2019-01-01. (17/05188-9)

BELAN, PETERSON A.; DE MACEDO, ROBSON A. G.; PEREIRA, MARIHA M. A.; ALVES, WONDER A. L.; DE ARAUJO, SIDNEI A.; CAMPILHO, A; KARRAY, F; ROMENY, BT. A Fast and Robust Approach for Touching Grains Segmentation. IMAGE ANALYSIS AND RECOGNITION (ICIAR 2018), v. 10882, p. 8-pg., 2018-01-01. (17/05188-9)