Liset Sulay Rodriguez Baca

Work place: Autonomous University of Peru, Peru, South America



Research Interests: Information Security, Marketing and Business Strategy


Liset Sulay Rodriguez Baca, Autonomous University of Peru, Peru, South America, PhD in Educational Sciences, Master in Systems Engineering, Master in Strategic Business Management, Systems Engineer and Bachelor of Education Postgraduate Research, Diploma in Cybersecurity, ISO 27001 Internal Auditor from the University of Catalonia in Colombia RENACYT Research Teacher recognized by CONCYTEC (National Council of Science and Technology and Technological Innovation). University professor with more than 15 years of experience in undergraduate and postgraduate. It has academic publications related to technology and education, as well as participation in competitive fund projects Experience in different areas of Information Technology in public and private organizations, international speaker on information and communication technologies, information security, business architecture, technology and educational innovation, higher education. Member of the International Scientific Committee of Refereed Journals.

Author Articles
Chilli Dryness and Ripening Stages Assessment Using Machine Vision

By Mahantesh Sajjan Lingangouda Kulkarni Basavaraj S. Anami Nijagunadev B. Gaddagimath Liset Sulay Rodriguez Baca

DOI:, Pub. Date: 8 Dec. 2023

The quality of chilli is prime concern for farmers, traders and chilli processing industries. The effective determination of chilli dryness and ripening stages are important factors in determining its quality and chilli shelf life with respect to manual estimation of ripening/dryness that are complex and time consuming. Chilli dryness and ripeness prediction at post-harvest stage by non-destructive machine vision technologies have potential of fair valuation for chilli produce for the chilli stalk holders. Chilli pericarp color values calculated from RGB, HSV and CIE-L*a*b* color space, texture properties using edge-wrinkles parameters are described by histogram of oriented gradients (HOG). LDA(linear discriminant analysis), RF(random-forest) and SVM(support vector machine) classifiers are analysed for performance accuracy for chilli dryness identification and chilli ripening stages using the machine vision. The chilli dryness identification accuracies of 83%, 85.4% and 83.5% are achieved using chilli color and HOG features with LDA, Random Forest and SVM classifiers respectively. Chilli ripening stage identification with combined chilli feature set of {color, HOG, SURF and LBP} using Support Vector Machine (SVM) average classifier accuracy is 90.56% across four chilli ripening stages. This work is simple with rapid, intelligent and high accuracy of chilli dryness and ripening identification by using machine vision approach has prospect in real-time chilli quality monitoring and grading. The results yielded were promising quality measurements compared previous studies.

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