Dr. Vasugi Chinnaiyan: Principal Scientist at ICAR-IIHR, Bengaluru, India
Article Details
The article titled “A novel combined approach for peel and pulp color classification in guava (Psidium guajava L.)” was published in Scientific Reports on 19 July 2026 as an open-access research article. The study was conducted by M. Megha, C. Vasugi, D. V. Sudhakar Rao, K. V. Ravishankar, K. S. Shivashankara, K. HimaBindu, C. Kanupriya, Yazgan Tunç, and Ali Khadivi. The research focused on developing a standardized method for classifying guava fruit color by integrating CIELAB color measurements and the Munsell color system. A total of 167 intervarietal guava progenies derived from the cross Purple local × Arka Poorna were evaluated. The researchers analyzed peel and pulp color characteristics along with pigment compounds such as anthocyanins, carotenoids, and lycopene. The study investigated relationships between measurable color parameters (L*, a*, b*, C*, and h°) and pigment levels to establish a rapid method for fruit quality evaluation.
Novelty
The major novelty of this study is the development of a combined CIELAB–Munsell color classification approach for guava that integrates visual color grouping with precise numerical color measurements. Unlike traditional methods that depend mainly on subjective color observation or separate pigment analysis, this approach provides a standardized and quantitative system for classifying guava peel and pulp colors. The study also introduced the use of the CIELAB a* value as an indirect indicator of pigment concentration, showing that fruit color measurements can predict levels of anthocyanins, carotenoids, and lycopene. This reduces the need for expensive and time-consuming biochemical analyses. The application of this method to a large guava breeding population represents an innovative approach for identifying superior fruit color traits and nutritional characteristics.
Impact
The research has significant implications for guava improvement, fruit quality assessment, and the food industry. In breeding programs, the developed color classification method can help researchers rapidly screen large numbers of guava progenies and select varieties with desirable peel and pulp colors. For the food industry, the approach can support efficient grading, sorting, and quality evaluation based on objective color standards. Since fruit color is closely associated with nutritional pigments and consumer preference, this method can help identify guava varieties with higher functional value. The research may also contribute to reducing production losses by improving quality-based classification systems and enhancing the commercial value of guava fruits.
Experimental Rigor
The experimental rigor of this study is demonstrated through the systematic evaluation of a large and genetically diverse guava population consisting of 167 intervarietal progenies developed from the cross Purple local × Arka Poorna. The researchers used standardized and internationally recognized color measurement methods, including the CIELAB color system and Munsell color classification, to obtain objective and reproducible color data. The study combined physical color measurements with biochemical analysis of important pigments such as anthocyanins, carotenoids, and lycopene, allowing validation of the relationship between visible fruit color and internal pigment composition. Statistical correlation analysis was performed to determine the strength of associations between color parameters and pigment levels. The development of predictive relationships between a* values and pigment concentration further strengthened the reliability and practical value of the proposed method.
Sustainability Impact
The study contributes to sustainable agricultural practices by introducing a rapid and less resource-intensive approach for fruit quality evaluation. Traditional pigment estimation methods often require chemical extraction, specialized equipment, and considerable laboratory resources. The color-based approach developed in this research can reduce chemical consumption, analysis time, and operational costs. In breeding programs, faster identification of desirable guava genotypes can improve resource efficiency by reducing the number of plants requiring detailed laboratory evaluation. The method also supports sustainable food production by enabling better fruit grading, reducing postharvest losses, and promoting the development of high-quality, nutrient-rich guava varieties.
Applicability
The developed color classification system has wide applications in horticulture, agriculture, and food industries. In guava breeding programs, it can be used for rapid screening and selection of superior progenies based on fruit appearance and pigment-related quality traits. Commercial growers and food industries can apply this approach for standardized grading, sorting, and quality control of guava fruits. The method can support digital imaging and automated fruit classification technologies by providing measurable color indicators. It can also assist researchers in characterizing germplasm collections and documenting fruit diversity. Beyond guava, the approach may be adapted for other fruits where color is associated with nutritional compounds and market quality.
Future Scope and Research Potential
The study provides a foundation for future development of advanced fruit quality assessment technologies. Further research can integrate artificial intelligence, machine learning, and computer vision systems with CIELAB color measurements to enable automated and real-time fruit grading. Large-scale validation across different guava varieties, environmental conditions, and cultivation regions would improve the reliability of the prediction models. Future studies may also explore relationships between color parameters and other nutritional compounds, antioxidant activity, sensory properties, and consumer preferences. The approach could contribute to precision horticulture by enabling rapid, non-destructive assessment of fruit quality and supporting the development of improved guava cultivars with enhanced nutritional and commercial value.
Research Portfolio
Dr. C. Vasugi is a Principal Scientist in the Division of Fruit Crops at the ICAR-Indian Institute of Horticultural Research (ICAR-IIHR), Bengaluru. She has made significant contributions to fruit crop improvement, particularly in guava, mango, and papaya through genetic resource utilization, varietal development, and stress resistance breeding. With more than two decades of research experience, she has played a key role in developing improved fruit varieties, conserving genetic resources, mentoring young researchers, and strengthening horticultural research in India.
Online Profile
- Citations: 829 total citations, with 520 citations accumulated since 2021. This shows that a large portion of the impact has come from more recent work.
- h-index: An overall h-index of 13 means that 13 publications have received at least 13 citations each. The h-index of 11 since 2021 suggests that recent publications are contributing significantly to research visibility.
- i10-index: An overall i10-index of 28 means that 28 publications have received at least 10 citations each. The since-2021 i10-index of 14 indicates continued publication influence in recent years.
Dr. C. Vasugi is a recognized horticultural scientist associated with ICAR-IIHR, specializing in Fruit Science and crop improvement. She holds a Google Scholar profile (https://scholar.google.com/citations?user=xfi9dkcAAAAJ&hl=en) and an ORCID profile (0000-0001-6336-9946). Her research contributions include more than 60 publications in national and international journals, development of protected fruit varieties, DUS guideline preparation, and leadership in externally funded research projects supported by ICAR, DBT, RKVY, PPV&FRA, NICRA, UNEP-GEF, ADB, and other organizations.
Education & Research Focus
Dr. C. Vasugi holds a Ph.D. in Horticulture with specialization in Fruit Science. Her research focuses on genetic improvement, breeding, characterization, conservation, and utilization of fruit crop genetic resources, especially guava, mango, and papaya. Her major research interests include developing high-yielding and stress-tolerant varieties, identifying gene donors for important traits, developing interspecific and intergeneric hybrids, improving fruit quality attributes, and enhancing resistance against biotic and abiotic stresses.
Experience, Research Timeline & Activities
Dr. Vasugi joined the ICAR service in 1997 and has been actively engaged in fruit crop improvement research at ICAR-IIHR since 1999. She has contributed to the development of guava varieties including Arka Rashmi, Arka Poorna, Arka Kiran, and Arka Mridula, and mango varieties Arka Udaya and Arka Suprabhath, many of which received legal protection through PPV&FRA registration. Her research activities include fruit germplasm collection, conservation, evaluation, DUS testing, pre-breeding line development, hybrid evaluation, seed storage technology, and management of 13 externally funded projects as Principal Investigator/Co-Principal Investigator. She has also guided and mentored 17 postgraduate and Ph.D. students and handled several higher education courses.
Awards & Honors
Dr. C. Vasugi has received ten awards and recognitions for her outstanding contributions as Principal Investigator and Co-Principal Investigator in fruit crop research. Her achievements include recognition for varietal development, conservation of fruit genetic resources, innovative breeding approaches, and successful execution of nationally and internationally supported research projects.
Strengths for Best Innovator Award
1. Innovative Development of a Standardized Fruit Color Classification Technology
- Developed a novel CIELAB–Munsell integrated color classification system for guava (Psidium guajava L.), combining objective color measurement with visual color categorization.
- The study was published as an open-access research article in Scientific Reports (19 July 2026), demonstrating international recognition and visibility.
- Evaluated 167 intervarietal guava progenies derived from Purple Local × Arka Poorna, providing a robust scientific foundation for fruit quality classification.
2. High-Impact Research Linking Color Phenotyping with Nutritional Traits
- Established relationships between measurable color parameters (L, a, b*, C*, and h°**) and important bioactive pigments including anthocyanins, carotenoids, and lycopene.
- Demonstrated the potential of CIELAB a value as a rapid indirect marker for pigment content*, reducing dependence on complex biochemical analysis.
- Created a practical innovation for rapid screening of fruit quality and nutritional value in breeding populations.
3. Strong Research Productivity and Scientific Recognition
- Dr. C. Vasugi has contributed more than 60 research publications in national and international journals.
- Google Scholar metrics indicate:
- Total citations: 829+
- Citations since 2021: 520+
- h-index: 13 (overall), 11 (since 2021)
- i10-index: 28 (overall), 14 (since 2021)
- These metrics demonstrate sustained research influence and increasing global visibility in horticultural science.
4. Translational Innovation Impact in Fruit Crop Improvement
- The innovation supports rapid selection of superior guava genotypes for:
- Improved fruit appearance
- Enhanced nutritional quality
- Consumer-preferred traits
- Commercial value enhancement
- Research outcomes have direct applications in breeding programs, germplasm characterization, fruit grading, and quality control systems.
- Contributions to fruit crop improvement include development and characterization of improved varieties such as Arka Rashmi, Arka Poorna, Arka Kiran, Arka Mridula, Arka Udaya, and Arka Suprabhath.
5. Leadership, Research Management, and Future Technology Potential
- More than 25 years of research experience in fruit crop improvement and genetic resource utilization.
- Managed and contributed to 13 externally funded research projects supported by organizations including ICAR, DBT, RKVY, PPV&FRA, NICRA, UNEP-GEF, and ADB.
- Guided and mentored 17 postgraduate and Ph.D. scholars, contributing to scientific capacity building.
- The innovation provides a pathway for integration with AI-based fruit grading, computer vision, and precision horticulture technologies.