AAVI: A Novel Approach to Estimating Leaf Nitrogen Concentration in Rice from Unmanned Aerial Vehicle Multispectral Imagery at Early and Middle Growth Stages

This paper evaluates the potential of integrating textural and spectral information from unmanned aerial vehicle (UAV)-based multispectral imagery for improving the quantification of nitrogen (N) status in rice crops. Vegetation indices (VIs), normalized difference texture indices (NDTIs), and their combination were used to estimate four N nutrition parameters leaf nitrogen concentration (LNC), leaf nitrogen accumulation (LNA), plant nitrogen concentration (PNC), and plant nitrogen accumulation (PNA). Results demonstrated that the normalized difference red-edge index (NDRE) performed best in estimating the N nutrition parameters among all the VI candidates. The optimal texture indices had comparable performance in N nutrition parameters estimation as compared to NDRE. Significant improvement for all N nutrition parameters could be obtained by integrating VIs with NDTIs using multiple linear regression. While tested across years and growth stages, the multivariate models also exhibited satisfactory estimation accuracy. For texture analysis, texture metrics calculated in the direction D3 (perpendicular to the row orientation) are recommended for monitoring row-planted crops. These findings indicate that the addition of textural information derived from UAV multispectral imagery could reduce the effects of background materials and saturation and enhance the N signals of rice canopies for the entire season.

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Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice

Frontiers in Plant Science ◽

10.3389/fpls.2018.00936 ◽

2018 ◽

Vol 9 ◽

Cited By ~ 18

Author(s):

Hengbiao Zheng ◽

Tao Cheng ◽

Dong Li ◽

Xia Yao ◽

Yongchao Tian ◽

...

Keyword(s):

Unmanned Aerial Vehicle ◽

Nitrogen Concentration ◽

Hyperspectral Data ◽

Multispectral Imagery ◽

Plant Nitrogen ◽

Concentration Estimation ◽

Aerial Vehicle

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Estimating leaf nitrogen concentration considering unsynchronized maize growth stages with canopy hyperspectral technique

Ecological Indicators ◽

10.1016/j.ecolind.2019.105590 ◽

2019 ◽

Vol 107 ◽

pp. 105590 ◽

Cited By ~ 5

Author(s):

Peng-Fei Wen ◽

Jia He ◽

Fang Ning ◽

Rui Wang ◽

Yuan-Hong Zhang ◽

...

Keyword(s):

Nitrogen Concentration ◽

Leaf Nitrogen ◽

Growth Stages ◽

Leaf Nitrogen Concentration ◽

Maize Growth

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Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques

Remote Sensing ◽

10.3390/rs12193237 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3237

Author(s):

Lucas Prado Osco ◽

José Marcato Junior ◽

Ana Paula Marques Ramos ◽

Danielle Elis Garcia Furuya ◽

Dthenifer Cordeiro Santana ◽

...

Keyword(s):

Machine Learning ◽

Plant Height ◽

Vegetation Index ◽

Nitrogen Concentration ◽

Leaf Nitrogen ◽

Machine Learning Techniques ◽

Multispectral Imagery ◽

Leaf Nitrogen Concentration ◽

Spectral Bands ◽

Learning Techniques

Under ideal conditions of nitrogen (N), maize (Zea mays L.) can grow to its full potential, reaching maximum plant height (PH). As a rapid and nondestructive approach, the analysis of unmanned aerial vehicles (UAV)-based imagery may be of assistance to estimate N and height. The main objective of this study is to present an approach to predict leaf nitrogen concentration (LNC, g kg−1) and PH (m) with machine learning techniques and UAV-based multispectral imagery in maize plants. An experiment with 11 maize cultivars under two rates of N fertilization was carried during the 2017/2018 and 2018/2019 crop seasons. The spectral vegetation indices (VI) normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), green normalized difference vegetation (GNDVI), and the soil adjusted vegetation index (SAVI) were extracted from the images and, in a computational system, used alongside the spectral bands as input parameters for different machine learning models. A randomized 10-fold cross-validation strategy, with a total of 100 replicates, was used to evaluate the performance of 9 supervised machine learning (ML) models using the Pearson’s correlation coefficient (r), mean absolute error (MAE), coefficient of regression (R²), and root mean square error (RMSE) metrics. The results indicated that the random forest (RF) algorithm performed better, with r and RMSE, respectively, of 0.91 and 1.9 g.kg−¹ for LNC, and 0.86 and 0.17 m for PH. It was also demonstrated that VIs contributed more to the algorithm’s performances than individual spectral bands. This study concludes that the RF model is appropriate to predict both agronomic variables in maize and may help farmers to monitor their plants based upon their LNC and PH diagnosis and use this knowledge to improve their production rates in the subsequent seasons.

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