LAUSR

Leaf chlorophyll is vital for plants because it helps them get energy through the process of photosynthesis. The present review thus examines various leaf chlorophyll content estimation techniques in laboratories and outdoor field conditions. The review consists of two sections: (1) destructive and (2) nondestructive methods for chlorophyll estimation. Through this review, we could find that Arnon's spectrophotometry method is the most popular and simplest method for the estimation of leaf chlorophyll under laboratory conditions. While android-based applications and portable equipment for the quantification of chlorophyll content are useful for onsite utilities. The algorithm used in these applications and equipment is trained for specific plants rather than being generalized across all plants. In the case of hyperspectral remote sensing, more than 42 hyperspectral indices were observed for chlorophyll estimations, and among these red-edge-based indices were found to be more appropriate. This review recommends that hyperspectral indices such as the three-band hyperspectral vegetation index, Chlgreen, Triangular Greenness Index, Wavelength Difference Index, and Normalized Difference Chlorophyll are generic and can be used for chlorophyll estimations of various plants. It was also observed that Artificial Intelligence (AI) and Machine Learning (ML)-based algorithms such as Random Forest, Support Vector Machine, and Artificial Neural Network regressions are the most suited and widely applied algorithms for chlorophyll estimation using the above hyperspectral data. It was also concluded that comparative studies are required in order to understand the advantages and disadvantages of reflectance-based vegetation indices and chlorophyll fluorescence imaging methods for chlorophyll estimations to comprehend their efficiency.