Diagnosis of Citrus Greening Using Artificial Intelligence: A Faster Region-Based Convolutional Neural Network Approach with Convolution Block Attention Module-Integrated VGGNet and ResNet Models

• Published in: Plants (Basel) 2024-06, Vol.13 (12), p.1631
• Main Authors: Dong, Ruihao, Shiraiwa, Aya, Pawasut, Achara, Sreechun, Kesaraporn, Hayashi, Takefumi
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Applications programs; Artificial intelligence; Artificial neural networks; attention mechanism; Bacterial diseases; Citrus fruits; Citrus greening; CNN models; Deep learning; Diagnosis; Digital imaging; Disease control; Disease detection; Disease management; Disease transmission; Experiments; Leaves; Machine learning; Medical diagnosis; Medical imaging; Modules; Neural networks; object detection; Pathogens; Plant bacterial diseases; plant disease detection; Plant diseases; Real time; Transfer learning
• ISSN: 2223-7747; 2223-7747
• DOI: 10.3390/plants13121631

The vector-transmitted Citrus Greening (CG) disease, also called Huanglongbing, is one of the most destructive diseases of citrus. Since no measures for directly controlling this disease are available at present, current disease management integrates several measures, such as vector control, the use of disease-free trees, the removal of diseased trees, etc. The most essential issue in integrated management is how CG-infected trees can be detected efficiently. For CG detection, digital image analyses using deep learning algorithms have attracted much interest from both researchers and growers. Models using transfer learning with the Faster R-CNN architecture were constructed and compared with two pre-trained Convolutional Neural Network (CNN) models, VGGNet and ResNet. Their efficiency was examined by integrating their feature extraction capabilities into the Convolution Block Attention Module (CBAM) to create VGGNet+CBAM and ResNet+CBAM variants. ResNet models performed best. Moreover, the integration of CBAM notably improved CG disease detection precision and the overall performance of the models. Efficient models with transfer learning using Faster R-CNN were loaded on web applications to facilitate access for real-time diagnosis by farmers via the deployment of in-field images. The practical ability of the applications to detect CG disease is discussed.