Recent trends and advances in hyperspectral imaging techniques to estimate solar induced fluorescence for plant phenotyping

Inevitable environmental changes empower the researchers to understand and analyze the plant traits for improving the ecosystem. Solar-induced fluorescence (SIF) is one of the functional traits to analyze the vege-tation and assess plant phenotyping. Estimation of SIF through hyperspectral imaging technique gaining its popularity in the recent days than any other estimating techniques due to its contiguous spectrum property, which allows us to obtain more information. Another merit of hyperspectral images is that they can be used to acquire data on different scales. In our review, we have focused on three major areas as follows, a.) Hyperspectral imaging techniques in estimating SIF in different scales varying from Ground Scale to Orbital Scales. b.) Cor -relation of other functional traits and factors influences the SIF estimation c.) Machine learning techniques used to interpret the SIF traits for Agricultural Monitoring. Moreover, the aforementioned areas are becoming crucial in the recent trend, and we confine our review with the state-of-the-art techniques exclusively from 2010 to 2021. We comprehend the details in the review to provide insights on the breakthrough made in hyperspectral imaging for SIF estimation, allowing the reader to deepen their understanding in the areas of plant phenotyping, which would enable them to explore the field for future research.

Automated summary

This article reviews the application of hyperspectral imaging technique in plant phenotyping, focusing on the estimation of Solar-induced fluorescence (SIF), correlation with other functional traits, and the use of machine learning techniques for interpreting SIF traits in agricultural monitoring. These areas have become crucial in recent trends, and the article provides insights into the breakthroughs in hyperspectral imaging for SIF estimation, enabling readers to deepen their understanding in the field of plant phenotyping and explore future research directions.