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Dr. Alireza Pourreza
University of California at Davis

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Research Keywords & Expertise

0 Agricultural Engineering
0 sensing technology
0 Digital agriculture
0 Precision and Digital Agriculture
0 Remote sensing & GIS applications in Agriculture and Forestry

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Journal article
Published: 26 October 2020 in Remote Sensing
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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.

ACS Style

Ali Moghimi; Alireza Pourreza; German Zuniga-Ramirez; Larry Williams; Matthew Fidelibus. A Novel Machine Learning Approach to Estimate Grapevine Leaf Nitrogen Concentration Using Aerial Multispectral Imagery. Remote Sensing 2020, 12, 3515 .

AMA Style

Ali Moghimi, Alireza Pourreza, German Zuniga-Ramirez, Larry Williams, Matthew Fidelibus. A Novel Machine Learning Approach to Estimate Grapevine Leaf Nitrogen Concentration Using Aerial Multispectral Imagery. Remote Sensing. 2020; 12 (21):3515.

Chicago/Turabian Style

Ali Moghimi; Alireza Pourreza; German Zuniga-Ramirez; Larry Williams; Matthew Fidelibus. 2020. "A Novel Machine Learning Approach to Estimate Grapevine Leaf Nitrogen Concentration Using Aerial Multispectral Imagery." Remote Sensing 12, no. 21: 3515.

Journal article
Published: 26 October 2020 in Sustainability
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Unmanaged spray drift from orchard pesticide application contributes to environmental contamination and causes significant danger to farmworkers, nearby residential areas, and neighbors’ crops. Most drift control approaches do not guarantee adequate and uniform canopy spray coverage. Our goal was to develop a spray backstop system that could block drifting from the top without any negative impact on spray coverage and on-target deposition. The design included a foldable mast and a shade structure that covered the trees from the top. We used a continuous loop sampling to assess and quantify the effectiveness of spray backstop on drift potential reduction. We also collected leaf samples from different sections of trees to compare on-target deposition and coverage. The results showed that the spray backstop system could significantly (p-Value < 0.01) reduce drift potential from the top (78% on average). While we did not find any statistical difference in overall canopy deposition with and without the backstop system, we observed some improvement in treetops deposition. This experiment’s output suggests that growers may be able to adjust their air-assist sprayers for a more uniform spray coverage without concern about the off-target movement of spray droplets when they employ the spray backstop system.

ACS Style

Alireza Pourreza; Ali Moghimi; Franz Niederholzer; Peter Larbi; German Zuniga-Ramirez; Kyle Cheung; Farzaneh Khorsandi. Spray Backstop: A Method to Reduce Orchard Spray Drift Potential without Limiting the Spray and Air Delivery. Sustainability 2020, 12, 8862 .

AMA Style

Alireza Pourreza, Ali Moghimi, Franz Niederholzer, Peter Larbi, German Zuniga-Ramirez, Kyle Cheung, Farzaneh Khorsandi. Spray Backstop: A Method to Reduce Orchard Spray Drift Potential without Limiting the Spray and Air Delivery. Sustainability. 2020; 12 (21):8862.

Chicago/Turabian Style

Alireza Pourreza; Ali Moghimi; Franz Niederholzer; Peter Larbi; German Zuniga-Ramirez; Kyle Cheung; Farzaneh Khorsandi. 2020. "Spray Backstop: A Method to Reduce Orchard Spray Drift Potential without Limiting the Spray and Air Delivery." Sustainability 12, no. 21: 8862.

Journal article
Published: 11 August 2020 in Water
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As water scarcity becomes of greater concern in arid and semi-arid regions due to altered weather patterns, greater and more accurate knowledge regarding evapotranspiration of crops produced in these areas is of increased significance to better manage limited water resources. This study aimed at determining the actual evapotranspiration (ETa) and crop coefficients (Ka) in California date palms. The residual of energy balance method using a combination of surface renewal and eddy covariance techniques was applied to measure ETa in six commercial mature date palm orchards (8–22 years old) over one year. The experimental orchards represent various soil types and conditions, irrigation management practices, canopy characteristics, and the most common date cultivars in the region. The results demonstrated considerable variability in date palm consumptive water use, both spatially and temporally. The cumulative ETa (CETa) across the six sites ranged from 1299 to 1501 mm with a mean daily ETa of 7.2 mm day−1 in June–July and 1.0 mm day−1 in December at the site with the highest crop water consumption. The mean monthly Ka values varied between 0.63 (December) and 0.90 (June) in the non-salt-affected, sandy loam soil date palms with an average density of 120 plants ha−1 and an average canopy cover and tree height of more than 80% and 11.0 m, respectively. However, the values ranged from 0.62 to 0.75 in a silty clay loam saline-sodic date palm orchard with 55% canopy cover, density of 148 plants ha−1, and 7.3 m tree height. Inverse relationships were derived between the CETa and soil salinity (ECe) in the crop root zone; and between the mean annual Ka and ECe. This information addresses the immediate needs of date growers for irrigation management in the region and enables them to more efficiently utilize water and to achieve full economic gains in a sustainable manner, especially as water resources become less available or more expensive.

ACS Style

Aliasghar Montazar; Robert Krueger; Dennis Corwin; Alireza Pourreza; Cayle Little; Sonia Rios; Richard L. Snyder. Determination of Actual Evapotranspiration and Crop Coefficients of California Date Palms Using the Residual of Energy Balance Approach. Water 2020, 12, 2253 .

AMA Style

Aliasghar Montazar, Robert Krueger, Dennis Corwin, Alireza Pourreza, Cayle Little, Sonia Rios, Richard L. Snyder. Determination of Actual Evapotranspiration and Crop Coefficients of California Date Palms Using the Residual of Energy Balance Approach. Water. 2020; 12 (8):2253.

Chicago/Turabian Style

Aliasghar Montazar; Robert Krueger; Dennis Corwin; Alireza Pourreza; Cayle Little; Sonia Rios; Richard L. Snyder. 2020. "Determination of Actual Evapotranspiration and Crop Coefficients of California Date Palms Using the Residual of Energy Balance Approach." Water 12, no. 8: 2253.

Journal article
Published: 23 April 2017 in Robotics
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A vision sensor was introduced and tested for early detection of citrus Huanglongbing (HLB). This disease is caused by the bacterium Candidatus Liberibacter asiaticus (CLas) and is transmitted by the Asian citrus psyllid. HLB is a devastating disease that has exerted a significant impact on citrus yield and quality in Florida. Unfortunately, no cure has been reported for HLB. Starch accumulates in HLB infected leaf chloroplasts, which causes the mottled blotchy green pattern. Starch rotates the polarization plane of light. A polarized imaging technique was used to detect the polarization-rotation caused by the hyper-accumulation of starch as a pre-symptomatic indication of HLB in young seedlings. Citrus seedlings were grown in a room with controlled conditions and exposed to intensive feeding by CLas-positive psyllids for eight weeks. A quantitative polymerase chain reaction was employed to confirm the HLB status of samples. Two datasets were acquired; the first created one month after the exposer to psyllids and the second two months later. The results showed that, with relatively unsophisticated imaging equipment, four levels of HLB infections could be detected with accuracies of 72%–81%. As expected, increasing the time interval between psyllid exposure and imaging increased the development of symptoms and, accordingly, improved the detection accuracy.

ACS Style

Alireza Pourreza; Won Suk Lee; Eva Czarnecka; Lance Verner; William Gurley. Feasibility of Using the Optical Sensing Techniques for Early Detection of Huanglongbing in Citrus Seedlings. Robotics 2017, 6, 11 .

AMA Style

Alireza Pourreza, Won Suk Lee, Eva Czarnecka, Lance Verner, William Gurley. Feasibility of Using the Optical Sensing Techniques for Early Detection of Huanglongbing in Citrus Seedlings. Robotics. 2017; 6 (2):11.

Chicago/Turabian Style

Alireza Pourreza; Won Suk Lee; Eva Czarnecka; Lance Verner; William Gurley. 2017. "Feasibility of Using the Optical Sensing Techniques for Early Detection of Huanglongbing in Citrus Seedlings." Robotics 6, no. 2: 11.

Journal article
Published: 01 January 2016 in IFAC-PapersOnLine
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ACS Style

Alireza Pourreza; Won Suk Lee; Ed Etxeberria; Yao Zhang. Identification of Citrus Huanglongbing Disease at the Pre-Symptomatic Stage Using Polarized Imaging Technique. IFAC-PapersOnLine 2016, 49, 110 -115.

AMA Style

Alireza Pourreza, Won Suk Lee, Ed Etxeberria, Yao Zhang. Identification of Citrus Huanglongbing Disease at the Pre-Symptomatic Stage Using Polarized Imaging Technique. IFAC-PapersOnLine. 2016; 49 (16):110-115.

Chicago/Turabian Style

Alireza Pourreza; Won Suk Lee; Ed Etxeberria; Yao Zhang. 2016. "Identification of Citrus Huanglongbing Disease at the Pre-Symptomatic Stage Using Polarized Imaging Technique." IFAC-PapersOnLine 49, no. 16: 110-115.

Journal article
Published: 01 February 2015 in Biosystems Engineering
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ACS Style

Alireza Pourreza; Won Suk Lee; Ed Etxeberria; Arunava Banerjee. An evaluation of a vision-based sensor performance in Huanglongbing disease identification. Biosystems Engineering 2015, 130, 13 -22.

AMA Style

Alireza Pourreza, Won Suk Lee, Ed Etxeberria, Arunava Banerjee. An evaluation of a vision-based sensor performance in Huanglongbing disease identification. Biosystems Engineering. 2015; 130 ():13-22.

Chicago/Turabian Style

Alireza Pourreza; Won Suk Lee; Ed Etxeberria; Arunava Banerjee. 2015. "An evaluation of a vision-based sensor performance in Huanglongbing disease identification." Biosystems Engineering 130, no. : 13-22.

Journal article
Published: 01 January 2015 in Computers and Electronics in Agriculture
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ACS Style

Alireza Pourreza; Won Suk Lee; Reza Ehsani; John K. Schueller; Eran Raveh. An optimum method for real-time in-field detection of Huanglongbing disease using a vision sensor. Computers and Electronics in Agriculture 2015, 110, 221 -232.

AMA Style

Alireza Pourreza, Won Suk Lee, Reza Ehsani, John K. Schueller, Eran Raveh. An optimum method for real-time in-field detection of Huanglongbing disease using a vision sensor. Computers and Electronics in Agriculture. 2015; 110 ():221-232.

Chicago/Turabian Style

Alireza Pourreza; Won Suk Lee; Reza Ehsani; John K. Schueller; Eran Raveh. 2015. "An optimum method for real-time in-field detection of Huanglongbing disease using a vision sensor." Computers and Electronics in Agriculture 110, no. : 221-232.