Estimating Relative Chlorophyll Content in Rice Leaves Using Unmanned Aerial Vehicle Multi-Spectral Images and Spectral–Textural Analysis

Leaf chlorophyll content is crucial for monitoring plant growth and photosynthetic capacity. The Soil and Plant Analysis Development (SPAD) values are widely utilized as a relative chlorophyll content index in ecological agricultural surveys and vegetation remote sensing applications. Multi-spectral cameras are a cost-effective alternative to hyperspectral cameras for agricultural monitoring. However, the limited spectral bands of multi-spectral cameras restrict the number of vegetation indices (VIs) that can be synthesized, necessitating the exploration of other options for SPAD estimation. This study evaluated the impact of using texture indices (TIs) and VIs, alone or in combination, for estimating rice SPAD values during different growth stages. A multi-spectral camera was attached to an unmanned aerial vehicle (UAV) to collect remote sensing images of the rice canopy, with manual SPAD measurements taken immediately after each flight. Random forest (RF) was employed as the regression method, and evaluation metrics included coefficient of determination (R2) and root mean squared error (RMSE). The study found that textural information extracted from multi-spectral images could effectively assess the SPAD values of rice. Constructing TIs by combining two textural feature values (TFVs) further improved the correlation of textural information with SPAD. Utilizing both VIs and TIs demonstrated superior performance throughout all growth stages. The model works well in estimating the rice SPAD in an independent experiment in 2022, proving that the model has good generalization ability. The results suggest that incorporating both spectral and textural data can enhance the precision of rice SPAD estimation throughout all growth stages, compared to using spectral data alone. These findings are of significant importance in the fields of precision agriculture and environmental protection.

[1]  Qiang Wu,et al.  Estimation of Relative Chlorophyll Content in Spring Wheat Based on Multi-Temporal UAV Remote Sensing , 2023, Agronomy.

[2]  Q. Chang,et al.  Rice Leaf Chlorophyll Content Estimation Using UAV-Based Spectral Images in Different Regions , 2022, Agronomy.

[3]  Yaohua Hu,et al.  Estimation of Potato Chlorophyll Content from UAV Multispectral Images with Stacking Ensemble Algorithm , 2022, Agronomy.

[4]  P. Zarco-Tejada,et al.  Evaluating the role of solar-induced fluorescence (SIF) and plant physiological traits for leaf nitrogen assessment in almond using airborne hyperspectral imagery , 2022, Remote Sensing of Environment.

[5]  S. Siebert,et al.  UAV-based indicators of crop growth are robust for distinct water and nutrient management but vary between crop development phases , 2022, Field Crops Research.

[6]  Y. Cohen,et al.  How many gigabytes per hectare are available in the digital agriculture era? A digitization footprint estimation , 2022, Comput. Electron. Agric..

[7]  Guofeng Wu,et al.  Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease , 2022, Ecological Indicators.

[8]  Minzan Li,et al.  UAV-based chlorophyll content estimation by evaluating vegetation index responses under different crop coverages , 2022, Comput. Electron. Agric..

[9]  Liangsheng Shi,et al.  A VI-based phenology adaptation approach for rice crop monitoring using UAV multispectral images , 2022, Field Crops Research.

[10]  Masakazu Komatsuzaki,et al.  Rice Height Monitoring between Different Estimation Models Using UAV Photogrammetry and Multispectral Technology , 2021, Remote. Sens..

[11]  Yi Peng,et al.  Identification of High Nitrogen Use Efficiency Phenotype in Rice (Oryza sativa L.) Through Entire Growth Duration by Unmanned Aerial Vehicle Multispectral Imagery , 2021, Frontiers in Plant Science.

[12]  J. Eitel,et al.  Improving Unmanned Aerial Vehicle (UAV) remote sensing of rice plant potassium accumulation by fusing spectral and textural information , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[13]  Minzan Li,et al.  Transfer-learning-based approach for leaf chlorophyll content estimation of winter wheat from hyperspectral data , 2021, Remote Sensing of Environment.

[14]  Shenghui Fang,et al.  Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season , 2021, Remote. Sens..

[15]  F. Shao,et al.  The Theoretical and Experimental Analysis of the Maximal Information Coefficient Approximate Algorithm , 2021 .

[16]  Yidan Bao,et al.  Grain yield prediction of rice using multi-temporal UAV-based RGB and multispectral images and model transfer – a case study of small farmlands in the South of China , 2020 .

[17]  M. G. Acorsi,et al.  Estimating Biomass of Black Oat Using UAV-Based RGB Imaging , 2019, Agronomy.

[18]  Xiaoyu Chen,et al.  Unmanned Aerial Vehicle for Remote Sensing Applications - A Review , 2019, Remote. Sens..

[19]  Wenjiang Huang,et al.  A New Integrated Vegetation Index for the Estimation of Winter Wheat Leaf Chlorophyll Content , 2019, Remote. Sens..

[20]  Guijun Yang,et al.  Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[21]  Suming Zhang,et al.  Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage , 2019, Sensors.

[22]  Hengbiao Zheng,et al.  Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery , 2018, Precision Agriculture.

[23]  C. Giardino,et al.  Spatial and temporal dynamics of primary producers in shallow lakes as seen from space: Intra-annual observations from Sentinel-2A , 2018, Limnologica.

[24]  Eberhard Hartung,et al.  Early optical detection of infection with brown rust in winter wheat by chlorophyll fluorescence excitation spectra , 2018, Comput. Electron. Agric..

[25]  Weixing Cao,et al.  Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery , 2017 .

[26]  Hao Yang,et al.  Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives , 2017, Front. Plant Sci..

[27]  Mryka Hall-Beyer,et al.  Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales , 2017 .

[28]  Mryka Hall-Beyer,et al.  GLCM Texture: A Tutorial v. 3.0 March 2017 , 2017 .

[29]  Bin Chen,et al.  Leaf chlorophyll content as a proxy for leaf photosynthetic capacity , 2017, Global change biology.

[30]  O. Mutanga,et al.  Investigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas , 2015 .

[31]  Jong-Min Yeom,et al.  Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data , 2014 .

[32]  F. Baret,et al.  Green area index from an unmanned aerial system over wheat and rapeseed crops , 2014 .

[33]  Huili Gong,et al.  Sensitivity Analysis of Vegetation Reflectance to Biochemical and Biophysical Variables at Leaf, Canopy, and Regional Scales , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[34]  G. Robertson,et al.  Global metaanalysis of the nonlinear response of soil nitrous oxide (N2O) emissions to fertilizer nitrogen , 2014, Proceedings of the National Academy of Sciences.

[35]  Sandra Eckert,et al.  Improved Forest Biomass and Carbon Estimations Using Texture Measures from WorldView-2 Satellite Data , 2012, Remote. Sens..

[36]  J. Nichol,et al.  Improved forest biomass estimates using ALOS AVNIR-2 texture indices , 2011 .

[37]  A. Viña,et al.  Remote estimation of leaf area index and green leaf biomass in maize canopies , 2003 .

[38]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[39]  R. Hall,et al.  Incorporating texture into classification of forest species composition from airborne multispectral images , 2000 .

[40]  John A. Gamon,et al.  Assessing leaf pigment content and activity with a reflectometer , 1999 .

[41]  G. A. Blackburn,et al.  Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches , 1998 .

[42]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[43]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[44]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

[45]  Didier Tanré,et al.  Atmospherically resistant vegetation index (ARVI) for EOS-MODIS , 1992, IEEE Trans. Geosci. Remote. Sens..

[46]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[47]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[48]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[49]  C. Jordan.  Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[50]  Guijun Yang,et al.  Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements , 2014 .

[51]  Cha Zhang,et al.  Ensemble Machine Learning , 2012 .