Estimation of tobacco leaf chlorophyll content under different nitrogen levels using UAV-based multispectral camera

Ruonan Zhang; Jiuquan Zhang; Yan Kuai; Tiancai Chen; Huifeng Yan

doi:10.1117/12.2658242

18 October 2022 Estimation of tobacco leaf chlorophyll content under different nitrogen levels using UAV-based multispectral camera

Ruonan Zhang, Jiuquan Zhang, Yan Kuai, Tiancai Chen, Huifeng Yan

Author Affiliations +

Proceedings Volume 12349, International Conference on Agri-Photonics and Smart Agricultural Sensing Technologies (ICASAST 2022); 123491E (2022) https://doi.org/10.1117/12.2658242
Event: International Conference on Agri-Photonics and Smart Agricultural Sensing Technologies (ICASAST 2022), 2022, Zhengzhou, China

Abstract

Leaf chlorophyll content is one of the key indices for plant growth state and nutrient management. This study was designed to develop a new prediction model for tobacco leaf chlorophyll content using Unmanned aerial vehicles (UAV)-based Multispectral Camera. An experimental field with four levels of N application rate (67.5, 142.5, 217.5, 292.5 kg N/ha) was conducted. Five bands (450, 560, 650, 730, 840nm) spectral information and destructive measurements of leaf area, leaf dry weight, leaf chlorophyll content were determined from representative upper, middle and lower leaves at leaf maturity stage. 58 vegetation indexes were calculated and regressed against the measured leaf chlorophyll content using stepwise regression analysis (SR), partial least squares regression (PLSR), random forest (RF), and Artificial neural network (ANN). The chlorophyll content per unit area ranged from 0.73 to 5.15 mg/cm², and the chlorophyll content per unit weight ranged from 0.14 to 0.91 mg/g. The relationship between chlorophyll content and N rate was directly proportionally. The leaf reflectance at different N levels was basically the same, leaf showed lower reflectance at 450 nm, 560 nm, 650 nm and high reflectance at 730 nm and 840 nm. Signal band reflectance has a lower correlation (|r|<0.75) with the chlorophyll content. The models’ R2 for predicting chlorophyll content per unit area ranged from 0.60 to 0.88, which has more accurate than the model predicting chlorophyll content per unit weight. The ANN model exhibited better performance for predicting chlorophyll content, with R²=0.875 and 0.743. These findings have important implications for improving tobacco growth-related traits in precision agriculture.

Citation Download Citation

Ruonan Zhang, Jiuquan Zhang, Yan Kuai, Tiancai Chen, and Huifeng Yan "Estimation of tobacco leaf chlorophyll content under different nitrogen levels using UAV-based multispectral camera", Proc. SPIE 12349, International Conference on Agri-Photonics and Smart Agricultural Sensing Technologies (ICASAST 2022), 123491E (18 October 2022); https://doi.org/10.1117/12.2658242

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KEYWORDS

Reflectivity

Vegetation

Data modeling

Performance modeling

Nitrogen

Cameras

Unmanned aerial vehicles

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