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Diagnosis of leaf chlorophyll content based on close-range multispectral fluorescence image correction

 

1. Introduction

Multispectral fluorescence imaging has emerged as a valuable tool for studying plant stress responses and diagnosing nutrient deficiencies, particularly in agricultural research. Chlorophyll content is a key indicator of plant health, and its accurate assessment can enhance precision farming and crop management strategies. However, fluorescence shadow errors caused by leaf structure and excitation light variation pose challenges to accurate diagnosis. This study explores the BLF-CLAHE (Butterworth Low Filter Contrast Limited Adaptive Histogram Equalization) method to correct fluorescence shadow effects and optimize chlorophyll content diagnostics in maize leaves. By integrating deep learning models for enhanced feature extraction, this research provides an advanced approach for improving plant fluorescence analysis and chlorophyll content assessment.

2. Impact of Maize Leaf Structure on Fluorescence Imaging

Leaf structure plays a critical role in influencing fluorescence imaging accuracy. The natural curvature and venation of maize leaves create variations in fluorescence intensity, leading to uneven light distribution and shadowing effects. These structural differences contribute to directional variability, with fluorescence intensity coefficients of variation (CV) being higher in the horizontal direction than in the vertical direction. Understanding the interplay between leaf morphology and fluorescence signal distribution is crucial for developing correction techniques that enhance imaging precision and minimize diagnostic errors in chlorophyll content analysis.

3. Frequency Domain Analysis of Fluorescence Information

Fluorescence information is distributed across different frequency domains, with the low-frequency domain containing a significantly higher proportion (over 65%) of relevant fluorescence data. This study's frequency domain analysis reveals that fluorescence yield varies with excitation light sources, with blue light inducing stronger signals compared to UV and red light. By leveraging this spectral insight, researchers can design more effective imaging corrections and diagnostic models. Filtering techniques such as Butterworth Low Filter (BLF) enhance critical fluorescence information, reducing noise interference and improving the reliability of plant health assessments.

4. BLF-CLAHE: A Novel Image Enhancement Method for Fluorescence Imaging

The BLF-CLAHE technique combines the advantages of Butterworth Low Filtering and Contrast Limited Adaptive Histogram Equalization to improve fluorescence image quality. Traditional CLAHE methods, while effective in some applications, struggle with uneven fluorescence intensity distribution caused by plant structures. BLF-CLAHE enhances fluorescence signals by selectively amplifying relevant image features while reducing distortions. Comparative analysis before and after correction demonstrates that BLF-CLAHE significantly improves fluorescence intensity distribution, leading to more accurate chlorophyll content assessments in maize leaves.

5. Deep Learning-Based Optimization of Chlorophyll Content Diagnosis

Deep learning techniques provide a powerful approach for extracting fluorescence information and refining chlorophyll content diagnostic models. By integrating deep learning algorithms, the study demonstrates an increase in diagnostic accuracy from 0.665 using conventional methods to 0.874, highlighting the potential of AI-driven analysis in agricultural imaging. Deep learning models enable automated feature extraction, reducing the need for manual corrections and improving the robustness of plant health assessments. This research underscores the growing role of artificial intelligence in precision agriculture and plant physiology studies.

6. Future Perspectives and Applications in Precision Agriculture

The findings of this study have significant implications for the future of precision agriculture and plant monitoring. The integration of advanced imaging techniques with deep learning models can revolutionize real-time crop health monitoring, enabling early detection of nutrient deficiencies and stress conditions. Future research should focus on expanding the application of BLF-CLAHE and deep learning methods to other crops, optimizing imaging parameters, and integrating field-based remote sensing technologies. Further development of AI-driven chlorophyll assessment models could lead to fully automated diagnostic systems, improving agricultural productivity and sustainability.

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