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I am passionate about conducting interdisciplinary research centered on the food-water-energy nexus. My current research focuses on applying innovative technologies (LiDAR and multispectral/hyperspectral imaging), automation (UAVs), and artificial intelligence (machine learning and deep learning algorithms) in agriculture to facilitate the digital revolution in agriculture.
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.
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 StyleAli 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 StyleAli 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.
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.
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 StyleAlireza 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 StyleAlireza 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.
Crop production needs to increase in a sustainable manner to meet the growing global demand for food. To identify crop varieties with high yield potential, plant scientists and breeders evaluate the performance of hundreds of lines in multiple locations over several years. To facilitate the process of selecting advanced varieties, an automated framework was developed in this study. A hyperspectral camera was mounted on an unmanned aerial vehicle to collect aerial imagery with high spatial and spectral resolution in a fast, cost-effective manner. Aerial images were captured in two consecutive growing seasons from three experimental yield fields composed of hundreds experimental wheat lines. The grain of more than thousand wheat plots was harvested by a combine, weighed, and recorded as the ground truth data. To investigate the yield variation at sub-plot scale and leverage the high spatial resolution, plots were divided into sub-plots using image processing techniques integrated by domain knowledge. Subsequent to extracting features from each sub-plot, deep neural networks were trained for yield estimation. The coefficient of determination for predicting the yield was 0.79 and 0.41 with normalized root mean square error of 0.24 and 0.14 g at sub-plot and plot scale, respectively. The results revealed that the proposed framework, as a valuable decision support tool, can facilitate the process of high-throughput yield phenotyping by offering the possibility of remote visual inspection of the plots as well as optimizing plot size to investigate more lines in a dedicated field each year.
Ali Moghimi; Ce Yang; James A. Anderson. Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat. Computers and Electronics in Agriculture 2020, 172, 105299 .
AMA StyleAli Moghimi, Ce Yang, James A. Anderson. Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat. Computers and Electronics in Agriculture. 2020; 172 ():105299.
Chicago/Turabian StyleAli Moghimi; Ce Yang; James A. Anderson. 2020. "Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat." Computers and Electronics in Agriculture 172, no. : 105299.
Fusarium head blight (FHB) is a devastating disease of wheat worldwide. In addition to reducing the yield of the crop, the causal pathogens also produce mycotoxins that can contaminate the grain. The development of resistant wheat varieties is one of the best ways to reduce the impact of FHB. To develop such varieties, breeders must expose germplasm lines to the pathogen in the field and assess the disease reaction. Phenotyping breeding materials for resistance to FHB is time-consuming, labor-intensive, and expensive when using conventional protocols. To develop a reliable and cost-effective high throughput phenotyping system for assessing FHB in the field, we focused on developing a method for processing color images of wheat spikes to accurately detect diseased areas using deep learning and image processing techniques. Color images of wheat spikes at the milk stage were collected in a shadow condition and processed to construct datasets, which were used to retrain a deep convolutional neural network model using transfer learning. Testing results showed that the model detected spikes very accurately in the images since the coefficient of determination for the number of spikes tallied by manual count and the model was 0.80. The model was assessed, and the mean average precision for the testing dataset was 0.9201. On the basis of the results for spike detection, a new color feature was applied to obtain the gray image of each spike and a modified region-growing algorithm was implemented to segment and detect the diseased areas of each spike. Results showed that the region growing algorithm performed better than the K-means and Otsu’s method in segmenting diseased areas. We demonstrated that deep learning techniques enable accurate detection of FHB in wheat based on color image analysis, and the proposed method can effectively detect spikes and diseased areas, which improves the efficiency of the FHB assessment in the field.
Ruicheng Qiu; Ce Yang; Ali Moghimi; Man Zhang; Brian J. Steffenson; Cory D. Hirsch. Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging. Remote Sensing 2019, 11, 2658 .
AMA StyleRuicheng Qiu, Ce Yang, Ali Moghimi, Man Zhang, Brian J. Steffenson, Cory D. Hirsch. Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging. Remote Sensing. 2019; 11 (22):2658.
Chicago/Turabian StyleRuicheng Qiu; Ce Yang; Ali Moghimi; Man Zhang; Brian J. Steffenson; Cory D. Hirsch. 2019. "Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging." Remote Sensing 11, no. 22: 2658.
Fusarium head blight (FHB) disease is extensively distributed worldwide. This disease damages grain quality and reduces yield. The detection of this disease in a high throughput way is crucial to planters and breeders. Our study focused on developing a method for processing wheat color images and accurately detecting disease areas using deep learning and image processing techniques. The color images of wheat at the milky stage were collected and processed to construct datasets, which were used to retrain a deep convolutional neural network model using transfer learning. Testing results showed that the model can detect spikes, and the coefficient of determination of the number of spikes between the manual count and the detection was 0.80. The model was assessed, and the mean average precision for the testing dataset was 0.9201. On the basis of the results of spike detection, a new color feature was applied to obtain the gray image of each spike. Then, a modified region growing algorithm was implemented to segment and detect the diseased areas of each spike. Results show that the region growing algorithm performs better than K-means and Otsu’s method in segmenting the FHB disease. Overall, this study demonstrates that deep learning techniques enable the accurate detection of FHB in wheat using color images, and the proposed method can effectively detect spikes and diseased areas, thereby improving the efficiency of FHB detection.
Ruicheng Qiu; Ce Yang; Ali Moghimi; Man Zhang; Brian Steffenson. Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging. 2019, 1 .
AMA StyleRuicheng Qiu, Ce Yang, Ali Moghimi, Man Zhang, Brian Steffenson. Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging. . 2019; ():1.
Chicago/Turabian StyleRuicheng Qiu; Ce Yang; Ali Moghimi; Man Zhang; Brian Steffenson. 2019. "Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging." , no. : 1.
Hyperspectral imaging is becoming an increasingly popular tool for high-throughput plant phenotyping, because it provides remarkable insights about the health status of plants. Feature selection is a key component in a hyperspectral image analysis, largely because a significant portion of spectral features are redundant and/or irrelevant, depending on the desired application. This paper presents an ensemble feature selection method to identify the most informative spectral features for practical applications in plant phenotyping. The hyperspectral data set contained the images of four wheat lines, each with a control and a salt (NaCl) treatment. To rank spectral features, six feature selection methods were used as the base for the ensemble: correlation-based feature selection, ReliefF, sequential feature selection, support vector machine-recursive feature elimination (SVM-RFE), LASSO logistic regression, and random forest. The best results were achieved by the ensemble of ReliefF, SVM-RFE, and random forest, which drastically reduced the dimension of the hyperspectral data set from 215 to 15 features, while improving the accuracy in classifying the salt-treated vegetation pixels from the control pixels by 8.5%. To transform the hyperspectral data set into a multispectral data set, six wavelengths as the center of broad multispectral bands around the most prominent features were determined by a clustering algorithm. The result of salt tolerance assessment of the four wheat lines using the derived multispectral data set was similar to that of the hyperspectral data set. This demonstrates that the proposed feature selection pipeline can be utilized for determining the most informative features and can be a valuable tool in the development of tailored multispectral cameras.
Ali Moghimi; Ce Yang; Peter Marchetto. Ensemble Feature Selection for Plant Phenotyping: A Journey From Hyperspectral to Multispectral Imaging. IEEE Access 2018, 6, 56870 -56884.
AMA StyleAli Moghimi, Ce Yang, Peter Marchetto. Ensemble Feature Selection for Plant Phenotyping: A Journey From Hyperspectral to Multispectral Imaging. IEEE Access. 2018; 6 ():56870-56884.
Chicago/Turabian StyleAli Moghimi; Ce Yang; Peter Marchetto. 2018. "Ensemble Feature Selection for Plant Phenotyping: A Journey From Hyperspectral to Multispectral Imaging." IEEE Access 6, no. : 56870-56884.
Salinity stress has significant adverse effects on crop productivity and yield. The primary goal of this study was to quantitatively rank salt tolerance in wheat using hyperspectral imaging. Four wheat lines were assayed in a hydroponic system with control and salt treatments (0 and 200 mM NaCl). Hyperspectral images were captured one day after salt application when there were no visual symptoms. Subsequent to necessary preprocessing tasks, two endmembers, each representing one of the treatment, were identified in each image using successive volume maximization. To simplify image analysis and interpretation, similarity of all pixels to the salt endmember was calculated by a technique proposed in this study, referred to as vector-wise similarity measurement. Using this approach allowed high-dimensional hyperspectral images to be reduced to one-dimensional gray-scale images while retaining all relevant information. Two methods were then utilized to analyze the gray-scale images: minimum difference of pair assignments and Bayesian method. The rankings of both methods were similar and consistent with the expected ranking obtained by conventional phenotyping experiments and historical evidence of salt tolerance. This research highlights the application of machine learning in hyperspectral image analysis for phenotyping of plants in a quantitative, interpretable, and non-invasive manner.
Ali Moghimi; Ce Yang; Marisa E. Miller; Shahryar F. Kianian; Peter Marchetto. A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging. Frontiers in Plant Science 2018, 9, 1182 .
AMA StyleAli Moghimi, Ce Yang, Marisa E. Miller, Shahryar F. Kianian, Peter Marchetto. A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging. Frontiers in Plant Science. 2018; 9 ():1182.
Chicago/Turabian StyleAli Moghimi; Ce Yang; Marisa E. Miller; Shahryar F. Kianian; Peter Marchetto. 2018. "A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging." Frontiers in Plant Science 9, no. : 1182.
In order to address the worldwide growing demand for food, agriculture is facing certain challenges and limitations. One of the important threats limiting crop productivity is salinity. Identifying salt tolerate varieties is crucial to mitigate the negative effects of this abiotic stress in agricultural production systems. Traditional measurement methods of this stress, such as biomass retention, are labor intensive, environmentally influenced, and often poorly correlated to salinity stress alone. In this study, hyperspectral imaging, as a non-destructive and rapid method, was utilized to expedite the process of identifying relatively the most salt tolerant line among four wheat lines including Triticum aestivum var. Kharchia, T. aestivum var. Chinese Spring, (Ae. columnaris) T. aestivum var. Chinese Spring, and (Ae. speltoides) T. aestivum var. Chinese Spring. To examine the possibility of early detection of a salt tolerant line, image acquisition was started one day after stress induction and continued on three, seven, and 12 days after adding salt. Simplex volume maximization (SiVM) method was deployed to detect superior wheat lines in response to salt stress. The results of analyzing images taken as soon as one day after salt induction revealed that Kharchia and (columnaris)Chinese Spring are the most tolerant wheat lines, while (speltoides) Chinese Spring was a moderately susceptible, and Chinese Spring was a relatively susceptible line to salt stress. These results were confirmed with the measuring biomass performed several weeks later. © (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ali Moghimi; Ce Yang; Marisa E. Miller; Shahryar Kianian; Peter Marchetto. Hyperspectral imaging to identify salt-tolerant wheat lines. Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II 2017, 10218, 1021805 .
AMA StyleAli Moghimi, Ce Yang, Marisa E. Miller, Shahryar Kianian, Peter Marchetto. Hyperspectral imaging to identify salt-tolerant wheat lines. Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II. 2017; 10218 ():1021805.
Chicago/Turabian StyleAli Moghimi; Ce Yang; Marisa E. Miller; Shahryar Kianian; Peter Marchetto. 2017. "Hyperspectral imaging to identify salt-tolerant wheat lines." Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II 10218, no. : 1021805.
Hyperspectral imaging can provide hundreds of images at different wave bands covering the visible and near infrared regions, which is superior to traditional spectral and RGB techniques. Minnesota produced a lot of maize every year, while the temperature in Minnesota can change abruptly during spring. This study was carried out to use hyperspectral imaging technique to identify maize seedlings with cold stress prior to having visible phenotypes. A total of 60 samples were scanned by the hyperspectral camera at the wave range of 395-885 nm. The spectral reflectance information was extracted from the corrected hyperspectral images. By spectral reflectance information, support vector machine (SVM) classification models were established to identify the cold stressed samples. Then, the wavelengths which could play significant roles for the detection were selected using two-wavelength combination method. The classifiers were built again using the selected wavelengths. From the results, it can be found the selected wavelengths can even perform better than full wave range. The overall results indicated that hyperspectral imaging has the potential to classify cold stress symptoms in maize seedlings and thus help in selecting the corn genome lines with cold stress resistance. © (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chuanqi Xie; Ce Yang; Ali Moghimi. Detection of cold stressed maize seedlings for high throughput phenotyping using hyperspectral imagery. Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017 2017, 10213, 1021305 .
AMA StyleChuanqi Xie, Ce Yang, Ali Moghimi. Detection of cold stressed maize seedlings for high throughput phenotyping using hyperspectral imagery. Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017. 2017; 10213 ():1021305.
Chicago/Turabian StyleChuanqi Xie; Ce Yang; Ali Moghimi. 2017. "Detection of cold stressed maize seedlings for high throughput phenotyping using hyperspectral imagery." Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017 10213, no. : 1021305.
Ali Moghimi; Mohammad Hosaien Saiedirad; Ebrahim Ganji Moghadam. Interpretation of viscoelastic behaviour of sweet cherries using rheological models. International Journal of Food Science & Technology 2011, 46, 855 -861.
AMA StyleAli Moghimi, Mohammad Hosaien Saiedirad, Ebrahim Ganji Moghadam. Interpretation of viscoelastic behaviour of sweet cherries using rheological models. International Journal of Food Science & Technology. 2011; 46 (4):855-861.
Chicago/Turabian StyleAli Moghimi; Mohammad Hosaien Saiedirad; Ebrahim Ganji Moghadam. 2011. "Interpretation of viscoelastic behaviour of sweet cherries using rheological models." International Journal of Food Science & Technology 46, no. 4: 855-861.
Ali Moghimi; Mohammad H. Aghkhani; Ameneh Sazgarnia; Majid Sarmad. Vis/NIR spectroscopy and chemometrics for the prediction of soluble solids content and acidity (pH) of kiwifruit. Biosystems Engineering 2010, 106, 295 -302.
AMA StyleAli Moghimi, Mohammad H. Aghkhani, Ameneh Sazgarnia, Majid Sarmad. Vis/NIR spectroscopy and chemometrics for the prediction of soluble solids content and acidity (pH) of kiwifruit. Biosystems Engineering. 2010; 106 (3):295-302.
Chicago/Turabian StyleAli Moghimi; Mohammad H. Aghkhani; Ameneh Sazgarnia; Majid Sarmad. 2010. "Vis/NIR spectroscopy and chemometrics for the prediction of soluble solids content and acidity (pH) of kiwifruit." Biosystems Engineering 106, no. 3: 295-302.
Ali Moghimi; Mohammad H. Aghkhani; Ameneh Sazgarnia; Mohammad H. Abbaspour-Fard. IMPROVEMENT OF NIR TRANSMISSION MODE FOR INTERNAL QUALITY ASSESSMENT OF FRUIT USING DIFFERENT ORIENTATIONS. Journal of Food Process Engineering 2010, 34, 1759 -1774.
AMA StyleAli Moghimi, Mohammad H. Aghkhani, Ameneh Sazgarnia, Mohammad H. Abbaspour-Fard. IMPROVEMENT OF NIR TRANSMISSION MODE FOR INTERNAL QUALITY ASSESSMENT OF FRUIT USING DIFFERENT ORIENTATIONS. Journal of Food Process Engineering. 2010; 34 (5):1759-1774.
Chicago/Turabian StyleAli Moghimi; Mohammad H. Aghkhani; Ameneh Sazgarnia; Mohammad H. Abbaspour-Fard. 2010. "IMPROVEMENT OF NIR TRANSMISSION MODE FOR INTERNAL QUALITY ASSESSMENT OF FRUIT USING DIFFERENT ORIENTATIONS." Journal of Food Process Engineering 34, no. 5: 1759-1774.