Classification of Citrus Plant Diseases Using Deep Transfer Learning
Source abstract
Abstract
In recent years, the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits. This in turn has helped in improving the quality and production of vegetables and fruits. Citrus fruits are well known for their taste and nutritional values. They are one of the natural and well known sources of vitamin C and planted worldwide. There are several diseases which severely affect the quality and yield of citrus fruits. In this paper, a new deep learning based technique is proposed for citrus disease classification. Two different pre-trained deep learning models have been used in this work. To increase the size of the citrus dataset used in this paper, image augmentation techniques are used. Moreover, to improve the visual quality of images, hybrid contrast stretching has been adopted. In addition, transfer learning is used to retrain the pre-trained models and the feature set is enriched by using feature fusion. The fused feature set is optimized using a meta-heuristic algorithm, the Whale Optimization Algorithm (WOA). The selected features are used for the classification of six different diseases of citrus plants. The proposed technique attains a classification accuracy of 95.7% with superior results when compared with recent techniques.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Spectroscopy and Chemometric Analyses · Remote Sensing in Agriculture
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
Show source-provided references
1. Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection10.1016/j.compag.2018.04.023 ↗
2. A survey on recognition of plant disease with help of algorithm
3. Potential nutritional benefits of current citrus consumption10.3390/agriculture3010170 ↗
4. An automated detection and classification of citrus plant diseases using image processing techniques: A review10.1016/j.compag.2018.07.032 ↗
5. CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features10.1016/j.compag.2018.10.013 ↗
6. An optimized method for segmentation and classification of apple diseases based on strong correlation and genetic algorithm based feature selection10.1109/ACCESS.2019.2908040 ↗
7. A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning10.1016/j.dib.2019.104340 ↗
8. Diagnosis and recognition of grape leaf diseases: An automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion
9. Intelligent microscopic approach for identification and recognition of citrus deformities10.1002/jemt.23320 ↗
10. Pedestrian identification using motion-controlled deep neural network in real-time visual surveillance
11. Prediction of COVID-19-pneumonia based on selected deep features and one class kernel extreme learning machine10.1016/j.compeleceng.2020.106960 ↗
12. An automated system for cucumber leaf diseased spot detection and classification using improved saliency method and deep features selection
13. Entropy-controlled deep features selection framework for grape leaf diseases recognition
14. Computer-aided gastrointestinal diseases analysis from wireless capsule endoscopy: A framework of best features selection10.1109/ACCESS.2020.3010448 ↗
15. StomachNet: Optimal deep learning features fusion for stomach abnormalities classification10.1109/ACCESS.2020.3034217 ↗
16. Fruits diseases classification: Exploiting a hierarchical framework for deep features fusion and selection10.1007/s11042-020-09244-3 ↗
17. Recognition of apple leaf diseases using deep learning and variances-controlled features reduction10.1016/j.micpro.2021.104027 ↗
18. Severity recognition of aloe vera diseases using AI in tensor flow domain10.32604/cmc.2020.012257 ↗
19. Recognizing apple leaf diseases using a novel parallel real-time processing framework based on MASK RCNN and transfer learning: an application for smart agriculture
20. A joint framework of feature reduction and robust feature selection for cucumber leaf diseases recognition10.1016/j.ijleo.2021.166566 ↗
21. Deep neural network features fusion and selection based on PLS regression with an application for crops diseases classification10.1016/j.asoc.2021.107164 ↗
22. Unhealthy region of citrus leaf detection using image processing techniques
23. Image recognition of citrus diseases based on deep learning10.32604/cmc.2020.012165 ↗
24. Citrus greening detection using visible spectrum imaging and C-SVC10.1016/j.compag.2016.09.005 ↗
25. Deep metric learning based citrus disease classification with sparse data10.1109/ACCESS.2020.3021487 ↗
26. A smart mobile diagnosis system for citrus diseases based on densely connected convolutional networks10.1109/ACCESS.2019.2924973 ↗
27. Classification of canker on small datasets using improved deep convolutional generative adversarial networks10.1109/ACCESS.2019.2900327 ↗
28. Symptom based automated detection of citrus diseases using color histogram and textural descriptors10.1016/j.compag.2017.04.008 ↗
29. Citrus greening infection detection by computer vision and deep learning
30. Mobilenetv2: Inverted residuals and linear bottlenecks
31. Densely connected convolutional networks
32. A survey on image data augmentation for deep learning10.1186/s40537-019-0197-0 ↗
33. Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray10.3390/app10093233 ↗
34. Feature selection approach based on whale optimization algorithm
35. Automatic plant image identification of vietnamese species using deep learning models