AI SearchAssessment of leaf chlorophyll content in pangola grass (Digitaria eriantha) using UAV-derived vegetation indices and chlorophyll meter
Call Number: AIT Thesis no.AE-24-02 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. AE-24-02Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2024Description: 112 leaves : ill. + 1online resourceSubject(s): Online resources: Dissertation note: Thesis (M. Eng.) - Asian Institute of Technology, 2024 Summary: Optimizing nitrogen (N) application in agricultural systems is crucial for enhancing crop productivity and sustainability. This study delves into the impact of varying nitrogen levels on plant health, specifically focusing on chlorophyll content and vegetation higher nitrogen levels (60 kg/ha, 120 kg/ha, and 180 kg/ha) are associated with increased plant height, larger leaf area, and higher yields in terms of fresh and dry weight, indicating the positive effects of nitrogen on plant development indices as indicators of plant vigor and nitrogen status. Through a comprehensive analysis integrating field experiments and remote sensing techniques, we explore the intricate relationships between nitrogen application, SPAD values, vegetation indices (NDVI, NDRE, GNDVI) from UAVs and Plant-O-Meter, and total chlorophyll content. Higher nitrogen levels consistently correlate with increased plant height, leaf area, and yields, reflecting improved photosynthetic activity and overall plant health. Notably, SPAD values and total chlorophyll content exhibit a strong positive correlation, with higher SPAD values indicating greater chlorophyll concentration and enhanced photosynthetic efficiency. Regression analyses reveal a precise relationship high coefficient of determination (R² = 0.94), with every unit increase in SPAD values associated with a significant rise in total chlorophyll content. Furthermore, the analysis of vegetation indices (NDVI, NDRE, GNDVI) from UAVs and Plant-O-Meter shows consistent positive correlations with total chlorophyll content, indicating the utility of remote sensing data in assessing plant health and vigor. NDVI and GNDVI exhibit relatively stronger associations compared to NDRE, explaining significant portions (51% to 74%) of chlorophyll content variability, suggesting NDVI's and GNDVI's potential as a powerful remote sensing tool for monitoring nitrogen-induced changes and evaluating vegetation vigor remotely.
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A Thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Agricultural Systems and Engineering
Thesis (M. Eng.) - Asian Institute of Technology, 2024
Optimizing nitrogen (N) application in agricultural systems is crucial for enhancing crop productivity and sustainability. This study delves into the impact of varying nitrogen levels on plant health, specifically focusing on chlorophyll content and vegetation higher nitrogen levels (60 kg/ha, 120 kg/ha, and 180 kg/ha) are associated with increased plant height, larger leaf area, and higher yields in terms of fresh and dry weight, indicating the positive effects of nitrogen on plant development indices as indicators of plant vigor and nitrogen status. Through a comprehensive analysis integrating field experiments and remote sensing techniques, we explore the intricate relationships between nitrogen application, SPAD values, vegetation indices (NDVI, NDRE, GNDVI) from UAVs and Plant-O-Meter, and total chlorophyll content. Higher nitrogen levels consistently correlate with increased plant height, leaf area, and yields, reflecting improved photosynthetic activity and overall plant health. Notably, SPAD values and total chlorophyll content exhibit a strong positive correlation, with higher SPAD values indicating greater chlorophyll concentration and enhanced photosynthetic efficiency. Regression analyses reveal a precise relationship high coefficient of determination (R² = 0.94), with every unit increase in SPAD values associated with a significant rise in total chlorophyll content. Furthermore, the analysis of vegetation indices (NDVI, NDRE, GNDVI) from UAVs and Plant-O-Meter shows consistent positive correlations with total chlorophyll content, indicating the utility of remote sensing data in assessing plant health and vigor. NDVI and GNDVI exhibit relatively stronger associations compared to NDRE, explaining significant portions (51% to 74%) of chlorophyll content variability, suggesting NDVI's and GNDVI's potential as a powerful remote sensing tool for monitoring nitrogen-induced changes and evaluating vegetation vigor remotely.
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