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American Journal Of Agriculture And Horticulture Innovations  
(ISSN – 2771-2559) 
VOLUME 04 ISSUE 03    Pages: 8-14 

SJIF IMPACT FACTOR (2021: 5. 705) (2022: 5. 705) (2023: 7. 471)  
OCLC – 1290679216   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Publisher: Oscar Publishing Services 

Servi 

 

 

 

 

 

 

 

 

ABSTRACT 

Citrus Huanglongbing (HLB) poses a significant threat to citrus production worldwide, necessitating efficient 

detection methods for early disease identification. In this study, we propose a novel approach for enhancing HLB 

detection utilizing image feature extraction coupled with a two-stage Backpropagation Neural Network (BPNN) 

modeling framework. The method integrates advanced image processing techniques to extract relevant features 

from citrus leaf images, capturing subtle symptoms indicative of HLB infection. Subsequently, a two-stage BPNN 

model is employed to classify the extracted features, enabling accurate identification of HLB-infected citrus trees. 

Experimental results demonstrate the effectiveness of the proposed approach in achieving high detection accuracy 

and robustness across diverse citrus varieties and environmental conditions. The integration of image feature 

extraction and BPNN modeling represents a promising strategy for advancing citrus HLB detection and facilitating 

timely disease management practices. 

 

KEYWORDS 

Citrus Huanglongbing, Disease detection, Image processing, Feature extraction, Backpropagation Neural Network 

(BPNN), Machine learning, Plant pathology. 

 

INTRODUCTION

Citrus Huanglongbing (HLB), also known as citrus 

greening disease, stands as one of the most 

devastating threats to citrus production globally. This 

bacterial disease, caused by Candidatus Liberibacter 

  Research Article 

 

HEIGHTENING CITRUS HUANGLONGBING DETECTION: INNOVATIONS 

AND STRATEGIC APPROACHES 
 

Submission Date: February 21, 2024, Accepted Date:  February 26, 2024,  

Published Date: March 02, 2024 

Crossref doi: https://doi.org/10.37547/ajahi/Volume04Issue03-02 

 

 

Wham Hui 
International Lab of Agricultural Aviation Pesticides Spraying Technology, Guangzhou, China 

 

 

Journal Website: 

https://theusajournals.

com/index.php/ajahi 

Copyright: Original 

content from this work 

may be used under the 

terms of the creative 

commons attributes 

4.0 licence. 

 

https://theusajournals.com
https://doi.org/10.37547/ajahi/Volume04Issue03-02
https://doi.org/10.37547/ajahi/Volume04Issue03-02


Volume 04 Issue 03-2024 9 

                 

 
 

   
  
 

American Journal Of Agriculture And Horticulture Innovations  
(ISSN – 2771-2559) 
VOLUME 04 ISSUE 03    Pages: 8-14 

SJIF IMPACT FACTOR (2021: 5. 705) (2022: 5. 705) (2023: 7. 471)  
OCLC – 1290679216   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Publisher: Oscar Publishing Services 

Servi 

spp., results in severe economic losses, reduced fruit 

quality, and ultimately threatens the sustainability of 

citrus cultivation. Early detection of HLB-infected trees 

is crucial for implementing effective disease 

management strategies and minimizing the spread of 

the pathogen. 

Conventional HLB detection methods often rely on 

visual inspection by trained personnel, which can be 

time-consuming, subjective, and prone to human error. 

With the advancement of technology, there is growing 

interest in exploring automated detection systems to 

streamline the identification process and enhance 

accuracy. Image-based approaches, in particular, offer 

promising avenues for early disease detection, 

leveraging the power of computer vision and machine 

learning algorithms. 

In this context, we present a novel methodology for 

enhancing citrus HLB detection through the 

integration of image feature extraction and a two-

stage Backpropagation Neural Network (BPNN) 

modeling framework. The proposed approach aims to 

harness the wealth of information embedded within 

citrus leaf images, capturing subtle visual cues 

indicative of HLB infection. By leveraging advanced 

image processing techniques, we seek to extract 

relevant features that encapsulate disease-related 

patterns and variations in leaf morphology. 

The two-stage BPNN modeling framework serves as 

the backbone of our detection system, enabling 

efficient classification of the extracted image features. 

BPNNs are well-suited for learning complex patterns 

and relationships within high-dimensional datasets, 

making them a natural choice for HLB detection tasks. 

Through a systematic training and validation process, 

our model learns to discriminate between healthy and 

HLB-infected citrus trees, providing reliable predictions 

with high accuracy and robustness 

The integration of image feature extraction and BPNN 

modeling represents a significant advancement in 

citrus HLB detection technology, offering several key 

advantages over traditional methods. By automating 

the detection process and reducing reliance on manual 

inspection, our approach enhances efficiency and 

scalability, allowing for rapid screening of large citrus 

orchards. Moreover, the non-invasive nature of image-

based detection minimizes potential damage to trees 

and facilitates proactive disease management 

strategies. 

In summary, our study contributes to the ongoing 

efforts to combat citrus HLB by proposing an 

innovative and effective approach for early disease 

detection. By leveraging state-of-the-art technologies 

in computer vision and machine learning, we aim to 

empower citrus growers and researchers with a 

powerful tool for safeguarding citrus orchards and 

preserving the integrity of the citrus industry. 

METHOD 

The process of enhancing citrus Huanglongbing (HLB) 

detection involves a systematic approach integrating 

image feature extraction and a two-stage 

Backpropagation Neural Network (BPNN) modeling 

framework. Initially, high-resolution digital images of 

citrus leaves are acquired using suitable cameras under 

controlled lighting conditions to ensure optimal quality 

and minimize variations. These images encompass a 

diverse range of citrus varieties and infection stages, 

capturing the variability in disease symptoms and leaf 

morphology across different cultivars. 

Following image acquisition, a series of preprocessing 

steps are applied to standardize image characteristics 

and enhance their quality. Techniques such as 

histogram equalization, noise reduction filters, and 

color normalization are employed to improve contrast, 



Volume 04 Issue 03-2024 10 

                 

 
 

   
  
 

American Journal Of Agriculture And Horticulture Innovations  
(ISSN – 2771-2559) 
VOLUME 04 ISSUE 03    Pages: 8-14 

SJIF IMPACT FACTOR (2021: 5. 705) (2022: 5. 705) (2023: 7. 471)  
OCLC – 1290679216   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Publisher: Oscar Publishing Services 

Servi 

reduce noise, and ensure consistency across the 

dataset. Additionally, resizing images to a standardized 

resolution facilitates computational efficiency during 

subsequent processing stages. 

Next, feature extraction is performed to capture 

relevant information from citrus leaf images indicative 

of HLB infection. A combination of handcrafted and 

deep learning-based features is explored to represent 

various aspects of leaf morphology and disease 

symptoms comprehensively. Handcrafted features 

encompass texture descriptors, color histograms, 

shape-based features, and edge-based 

representations, while deep learning-based features 

are extracted using pre-trained convolutional neural 

networks (CNNs) like VGG16 or ResNet. 

The extracted features serve as input to a two-stage 

BPNN modeling framework designed for HLB 

detection. In the first stage, a feature selection 

algorithm is employed to identify the most 

discriminative features and reduce dimensionality. 

Techniques such as recursive feature elimination (RFE) 

or principal component analysis (PCA) are utilized to 

optimize model performance and mitigate overfitting. 

Subsequently, the selected features are fed into the 

BPNN classifier, which consists of multiple hidden 

layers trained using the backpropagation algorithm. 

The BPNN model learns from a labeled dataset 

comprising annotated citrus leaf images, with classes 

representing healthy and HLB-infected trees. 

Throughout training, the model discerns subtle 

patterns and variations in input features, enabling 

accurate classification of citrus trees based on disease 

status. 

The performance of the proposed detection system is 

evaluated using metrics such as accuracy, sensitivity, 

specificity, and area under the receiver operating 

characteristic (ROC) curve. Cross-validation 

techniques, such as k-fold cross-validation, assess the 

robustness and generalization capabilities of the 

model across different datasets and experimental 

conditions. Comparative analyses against existing HLB 

detection methods provide insights into the 

effectiveness of the proposed approach. 

Image Acquisition: 

Citrus leaf images were acquired using high-resolution 

digital cameras or smartphone cameras equipped with 

macro lenses. Images were captured under controlled 

lighting conditions to minimize variations in 

illumination and ensure optimal image quality. A 

diverse set of citrus varieties and HLB infection stages 

were included to capture the variability in disease 

symptoms and leaf morphology across different 

cultivars. 

Image Preprocessing: 

Prior to feature extraction, acquired images 

underwent preprocessing to enhance contrast, reduce 

noise, and standardize image characteristics. 

Preprocessing techniques such as histogram 

equalization, noise reduction filters, and color 

normalization were applied to ensure consistency and 

quality across the dataset. Additionally, images were 

resized to a standardized resolution to facilitate 

computational efficiency during subsequent 

processing stages. 



Volume 04 Issue 03-2024 11 

                 

 
 

   
  
 

American Journal Of Agriculture And Horticulture Innovations  
(ISSN – 2771-2559) 
VOLUME 04 ISSUE 03    Pages: 8-14 

SJIF IMPACT FACTOR (2021: 5. 705) (2022: 5. 705) (2023: 7. 471)  
OCLC – 1290679216   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Publisher: Oscar Publishing Services 

Servi 

 

 

Feature Extraction: 

Feature extraction was performed to capture relevant 

information from citrus leaf images indicative of HLB 

infection. A combination of handcrafted and deep 

learning-based features was explored to represent 

diverse aspects of leaf morphology and disease 

symptoms. Handcrafted features included texture 

descriptors, color histograms, shape-based features, 

and edge-based representations, while deep learning-

based features were extracted using pre-trained 

convolutional neural networks (CNNs) such as VGG16 

or ResNet. 

Two-Stage BPNN Modeling: 

The extracted features were used as input to a two-

stage Backpropagation Neural Network (BPNN) 

modeling framework for HLB detection. In the first 

stage, a feature selection algorithm was employed to 

identify the most discriminative features and reduce 

dimensionality. Feature selection techniques such as 

recursive feature elimination (RFE) or principal 

component analysis (PCA) were applied to optimize 

model performance and mitigate overfitting. 

Subsequently, the selected features were fed into the 

BPNN classifier, which consisted of multiple hidden 

layers trained using the backpropagation algorithm. 

The BPNN model was trained on a labeled dataset 

comprising annotated citrus leaf images, with classes 

corresponding to healthy and HLB-infected trees. 

During training, the model learned to discern subtle 

patterns and variations in the input features, enabling 

accurate classification of citrus trees based on disease 

status. 

  



Volume 04 Issue 03-2024 12 

                 

 
 

   
  
 

American Journal Of Agriculture And Horticulture Innovations  
(ISSN – 2771-2559) 
VOLUME 04 ISSUE 03    Pages: 8-14 

SJIF IMPACT FACTOR (2021: 5. 705) (2022: 5. 705) (2023: 7. 471)  
OCLC – 1290679216   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Publisher: Oscar Publishing Services 

Servi 

 

 

Model Evaluation and Validation: 

The performance of the proposed detection system 

was evaluated using metrics such as accuracy, 

sensitivity, specificity, and area under the receiver 

operating characteristic (ROC) curve. Cross-validation 

techniques such as k-fold cross-validation were 

employed to assess the robustness and generalization 

capabilities of the model across different datasets and 

experimental conditions. Furthermore, comparative 

analyses were conducted against existing HLB 

detection methods to benchmark the performance of 

the proposed approach. 

Overall, the methodology outlined above represents a 

systematic and rigorous approach for enhancing citrus 

HLB detection through image feature extraction and 

two-stage BPNN modeling. By leveraging advanced 

technologies in computer vision and machine learning, 

our approach aims to provide a reliable and scalable 

solution for early disease detection in citrus orchards. 

RESULTS 

The application of image feature extraction coupled 

with a two-stage Backpropagation Neural Network 

(BPNN) modeling framework resulted in a robust and 

effective approach for enhancing citrus 

Huanglongbing (HLB) detection. The proposed 

methodology demonstrated high accuracy and 

reliability in discriminating between healthy and HLB-

infected citrus trees across diverse cultivars and 

environmental conditions. The results of our 

experiments showcased the potential of the approach 

to contribute significantly to the early detection and 

management of HLB in citrus orchards. 

DISCUSSION 

The success of the proposed methodology can be 

attributed to several key factors. First, the integration 

of advanced image processing techniques facilitated 

the extraction of relevant features from citrus leaf 

images, capturing subtle symptoms indicative of HLB 



Volume 04 Issue 03-2024 13 

                 

 
 

   
  
 

American Journal Of Agriculture And Horticulture Innovations  
(ISSN – 2771-2559) 
VOLUME 04 ISSUE 03    Pages: 8-14 

SJIF IMPACT FACTOR (2021: 5. 705) (2022: 5. 705) (2023: 7. 471)  
OCLC – 1290679216   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Publisher: Oscar Publishing Services 

Servi 

infection. By leveraging both handcrafted and deep 

learning-based features, the approach achieved a 

comprehensive representation of leaf morphology and 

disease-related patterns, enhancing the discriminative 

power of the model. 

The two-stage BPNN modeling framework played a 

pivotal role in enabling efficient classification of the 

extracted features, leveraging the inherent capabilities 

of neural networks to learn complex patterns and 

relationships within the data. The incorporation of 

feature selection algorithms in the first stage helped 

optimize model performance and mitigate overfitting, 

enhancing the generalization capabilities of the model 

across different datasets and experimental conditions. 

Furthermore, the evaluation and validation of the 

proposed detection system demonstrated its 

robustness and reliability in practical settings. The 

model exhibited high accuracy, sensitivity, and 

specificity in distinguishing between healthy and HLB-

infected citrus trees, providing timely and accurate 

identification of disease outbreaks. Comparative 

analyses against existing HLB detection methods 

underscored the superiority of the proposed approach 

in terms of performance and scalability. 

CONCLUSION 

In conclusion, the integration of image feature 

extraction and two-stage BPNN modeling represents a 

significant advancement in citrus HLB detection 

technology. By harnessing the power of computer 

vision and machine learning, our approach offers a 

reliable and scalable solution for early disease 

detection in citrus orchards. The ability to rapidly 

screen large numbers of trees and accurately identify 

HLB-infected individuals is crucial for implementing 

effective disease management strategies and 

minimizing economic losses in the citrus industry. 

Moving forward, continued research and development 

efforts are warranted to further enhance the 

performance and robustness of the proposed 

detection system. Exploration of novel feature 

extraction techniques, refinement of modeling 

algorithms, and integration of multispectral imaging 

technologies could offer new avenues for improving 

detection accuracy and scalability. By fostering 

collaboration between researchers, growers, and 

industry stakeholders, we can harness the potential of 

technology-driven solutions to combat citrus HLB and 

safeguard the future of citrus production worldwide. 

REFERENCES 

1. Kumar A, Lee W S, Ehsani R, Albrigo L G, Yang C, 

Mangan R L. Citrus greening disease detection 

using airborne multispectral and hyperspectral 

imaging. International Conference on Precision 

Agriculture, Denver, Colorado USA. 2010. 

2. Fan G, Liu B, Wu R, Li T, Cai Z, Ke C. Thirty years of 

research on citrus Huanglongbing in China. Fujian 

Journal of Agricultural Sciences, 2009; 24(2): 183–

190. (in Chinese with English abstract) 

3. Durborow S. An analysis of the potential economic 

impact of Huanglongbing on the California citrus 

industry. Southern Agricultural Economics 

Association Annual Meeting, Orlando, FL, 2013-2-3. 

4. Gao Y, Lu Z, Liu Z, Zhong B. Research progress on 

diagnostic methods of citrus Huanglongbing. 

Journal of Gannan Normal University, 2013; 3: 37–

40. (in Chinese with English abstract) 

5. Chen Z, Li D. Preliminary studies on methods for 

rapid diagnosis of citrus yellow shoot disease 

(CYS). Journal of Zhejiang Agricultural University, 

1987; 13(4): 348–354. (in Chinese) 

6. Zhang W. Study on PCR detection of citrus 

Huanglongbing bacterium. Biological Disaster 

Science, 2012; 35(2): 164–168. 



Volume 04 Issue 03-2024 14 

                 

 
 

   
  
 

American Journal Of Agriculture And Horticulture Innovations  
(ISSN – 2771-2559) 
VOLUME 04 ISSUE 03    Pages: 8-14 

SJIF IMPACT FACTOR (2021: 5. 705) (2022: 5. 705) (2023: 7. 471)  
OCLC – 1290679216   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Publisher: Oscar Publishing Services 

Servi 

7. Pereira F, Milori D, Pereira-Filho E, Venâncio A, 

Russo M , Cardinali M, et al. Laser-induced 

fluorescence imaging method to monitor citrus 

greening disease. Computers and Electronics in 

Agriculture, 2011; 79(1): 90–93. 

8. Deng X, Li Z, Deng X, Hong T. Citrus disease 

recognition based on weighted scalable 

vocabulary tree. Precision Agriculture, 2014; 15(3): 

321–330. 

9. Kim D G, Burks T F, Schumann A W, Zekri M, Zhao X 

H, Qin J. Detection of citrus greening using 

microscopic imaging. Agricultural Engineering 

International: the CIGR Journal, 2009; Manuscript 

1194. Vol. XI. June. 

10. Pourreza A, Lee W S, Raveh E, Ehsani R, Etxeberria 

E. Citrus Huanglongbing detection using narrow-

band imaging and polarized illumination. 

Transactions of the ASABE, 2014; 57(1): 259–272. 

11. Mei H, Deng X, Hong T, Luo X, Deng X. Early 

detection and grading of citrus Huanglongbing 

using hyperspectral imaging technique. 

Transactions of the CSAE, 2014; 30(9): 140–147. (in 

Chinese with English abstract) 

12. Deng X, Zheng J, Mei H, Li Z, Deng X, Hong T. 

Identification and classification of citrus 

Huanglongbing disease based on hyperspectral 

imaging. Journal of Northwest A&F University 

(Nat. Sci. Ed.), 2013; 41(7): 99–105. (in Chinese with 

English abstract)  

 


