Igor Kuivjogi Fernandes Source Confirmed

OverviewAI-generated summary

Igor Kuivjogi Fernandes' research focuses on the application of machine learning techniques to agricultural science, particularly in crop yield prediction and the characterization of breeding environments. His work integrates genetic and environmental data to model complex interactions, aiming to improve crop adaptation and breeding strategies. Fernandes has investigated these methods using case studies from Brazil, focusing on crops such as common bean and maize, as well as tropical irrigated rice.

His publications explore enviromic prediction as a tool to define climate adaptation limits and the use of machine learning for pattern recognition in environmental quality prediction. Fernandes also utilizes crop models in conjunction with machine learning to understand spatial-temporal characteristics of agricultural environments. His scholarship metrics include an h-index of 4, with 8 total publications and 83 citations. Key collaborators include Samuel B. Fernandes and Caio Canella Vieira, both from the University of Arkansas at Fayetteville, with whom he shares multiple publications.

Metrics

• h-index: 10
• Publications: 18
• Citations: 350