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Article

A Novel Machine Learning Approach to Estimate Grapevine Leaf Nitrogen Concentration Using Aerial Multispectral Imagery

1
Department of Biological and Agricultural Engineering, University of California, Davis, One Shields Ave, Davis, CA 95616, USA
2
Kearney Agricultural Research and Extension Center, 9240 S. Riverbend Avenue, Parlier, CA 93648, USA
3
Department of Viticulture and Enology, University of California, Davis, 595 Hilgard Ln, Davis, CA 95616, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(21), 3515; https://doi.org/10.3390/rs12213515
Received: 8 September 2020 / Revised: 13 October 2020 / Accepted: 21 October 2020 / Published: 26 October 2020
(This article belongs to the Special Issue Remote Sensing for Precision Nitrogen Management)
Assessment of the nitrogen status of grapevines with high spatial, temporal resolution offers benefits in fertilizer use efficiency, crop yield and quality, and vineyard uniformity. The primary objective of this study was to develop a robust predictive model for grapevine nitrogen estimation at bloom stage using high-resolution multispectral images captured by an unmanned aerial vehicle (UAV). Aerial imagery and leaf tissue sampling were conducted from 150 grapevines subjected to five rates of nitrogen applications. Subsequent to appropriate pre-processing steps, pixels representing the canopy were segmented from the background per each vine. First, we defined a binary classification problem using pixels of three vines with the minimum (low-N class) and two vines with the maximum (high-N class) nitrogen concentration. Following optimized hyperparameters configuration, we trained five machine learning classifiers, including support vector machine (SVM), random forest, XGBoost, quadratic discriminant analysis (QDA), and deep neural network (DNN) with fully-connected layers. Among the classifiers, SVM offered the highest F1-score (82.24%) on the test dataset at the cost of a very long training time compared to the other classifiers. Alternatively, QDA and XGBoost required the minimum training time with promising F1-score of 80.85% and 80.27%, respectively. Second, we transformed the classification into a regression problem by averaging the posterior probability of high-N class for all pixels within each of 150 vines. XGBoost exhibited a slightly larger coefficient of determination (R2 = 0.56) and lower root mean square error (RMSE) (0.23%) compared to other learning methods in the prediction of nitrogen concentration of all vines. The proposed approach provides values in (i) leveraging high-resolution imagery, (ii) investigating spatial distribution of nitrogen across a vine’s canopy, and (iii) defining spatial zones for nitrogen application and smart sampling. View Full-Text
Keywords: grapevine; hyperparameter optimization; machine learning; multispectral imaging; nitrogen; precision viticulture; UAV grapevine; hyperparameter optimization; machine learning; multispectral imaging; nitrogen; precision viticulture; UAV
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MDPI and ACS Style

Moghimi, A.; Pourreza, A.; Zuniga-Ramirez, G.; Williams, L.E.; Fidelibus, M.W. A Novel Machine Learning Approach to Estimate Grapevine Leaf Nitrogen Concentration Using Aerial Multispectral Imagery. Remote Sens. 2020, 12, 3515. https://doi.org/10.3390/rs12213515

AMA Style

Moghimi A, Pourreza A, Zuniga-Ramirez G, Williams LE, Fidelibus MW. A Novel Machine Learning Approach to Estimate Grapevine Leaf Nitrogen Concentration Using Aerial Multispectral Imagery. Remote Sensing. 2020; 12(21):3515. https://doi.org/10.3390/rs12213515

Chicago/Turabian Style

Moghimi, Ali, Alireza Pourreza, German Zuniga-Ramirez, Larry E. Williams, and Matthew W. Fidelibus. 2020. "A Novel Machine Learning Approach to Estimate Grapevine Leaf Nitrogen Concentration Using Aerial Multispectral Imagery" Remote Sensing 12, no. 21: 3515. https://doi.org/10.3390/rs12213515

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