Migrating from Invasive to Noninvasive Techniques for Enhanced Leaf Chlorophyll Content Estimations Efficiency

Area of Science:

• Plant Physiology
• Remote Sensing
• Spectroscopy

Background:

• Leaf chlorophyll is essential for photosynthesis and plant energy production.
• Accurate chlorophyll estimation is vital for agricultural and ecological monitoring.
• Various laboratory and field-based methods exist for chlorophyll quantification.

Purpose of the Study:

• To review and compare destructive and nondestructive methods for estimating leaf chlorophyll content.
• To identify the most effective techniques for both laboratory and outdoor field conditions.
• To evaluate the suitability of different indices and algorithms for chlorophyll estimation.

Main Methods:

• Literature review of destructive and nondestructive chlorophyll estimation techniques.
• Analysis of spectrophotometry, portable devices, and hyperspectral remote sensing.
• Evaluation of various vegetation indices and Artificial Intelligence (AI)/Machine Learning (ML) algorithms.

Main Results:

• Arnon's spectrophotometry is a popular laboratory method.
• Android applications and portable devices offer onsite utility but lack generalization.
• Red-edge-based hyperspectral indices and AI/ML algorithms (Random Forest, SVM, ANN) show high suitability for chlorophyll estimation.

Conclusions:

• Generic hyperspectral indices (e.g., Three-band Hyperspectral Vegetation Index, Chlgreen) are recommended for diverse plant species.
• AI/ML algorithms are well-suited for chlorophyll estimation using hyperspectral data.
• Further comparative studies are needed to assess reflectance-based indices versus chlorophyll fluorescence imaging.