Journal of Researches in Mechanics of Agricultural Machinery

Journal of Researches in Mechanics of Agricultural Machinery

A new approach to diagnose three grape plant diseases (Black Rot, Black Measles, and Leaf Bligh) based on color image processing and machine learning

Document Type : Research Paper

Authors
Mohsen Najafabadiha 1
Davood Mohammadzamani 2
Mohammad Gholamiparashkoohi 3
1 Ph.D. student, Biosystems engineering, Islamic Azad University, Takestan branch, Takestan, Iran.
2 Associate Professor, Biosystems Engineering, Islamic Azad University, Takestan branch, Takestan, Iran.
3 Department of Mechanical Engineering, Shahr-e-Qods Barench,Islamic Azad University, Tehran, Iran.
10.22034/jrmam.2024.14711.698
Abstract
Disease management in grapevine has become one of the basic issues that farmers have to face. Diseases can greatly affect the performance and life of this plant. With increasing environmental pressures and climate changes, the need for new and smart methods in managing diseases and pests has become inevitable. In this research, in order to diagnose and classify grape plant leaf diseases with the names Black Rot, Black Measles and Leaf Blight, after removing the background from the images of the leaves and extracting the features of texture, color and shape from the images, A combination of support vector machine classifier and butterfly optimization algorithm was used to select the most important features in the diagnosis of grape plant leaf disease. The results of the precision for black rot, Black Measles, and Leaf Blight diseases and healthy leaves were 100, 100, 100 and 95% respectively, and the classification accuracy for the diagnosis of the diseases and healthy group was 98.75%. Also, 15 characteristics of texture, color and shape were introduced to the researchers of plant pathology and data science with the help of butterfly optimization feature selection algorithm. The classification results showed that the use of image processing and machine learning has a high ability to diagnose and classify plant diseases.
Keywords
Feature Selection
Feature Extraction
Grape Diseases
Diagnosis and Classification
Artificial Intelligence
Subjects
Abdulridha, J., Ampatzidis, Y., Kakarla, S. C., & Roberts, P. (2019). Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques. In Precision Agriculture. Springer Science and Business Media LLC., 21(5): 955-978.
 
Arora, S., & Singh, S. (2018). Butterfly optimization algorithm: a novel approach for global optimization. In Soft Computing. Springer Science and Business Media LLC., 23(3): 715-734.
 
Asefpour Vakilian, K., & Massah, J. (2017). A farmer-assistant robot for nitrogen fertilizing management of greenhouse crops. Computers and Electronics in Agriculture, 139(9): 153-163.
 
Barbedo, J. G. A. (2018). Factors influencing the use of deep learning for plant disease recognition. Biosystems Engineering, 172(6): 84-91.
 
Chen, J., Zheng, H., Lin, X., Wu, Y., & Su, M. (2018). A novel image segmentation method based on fast density clustering algorithm. In Engineering Applications of Artificial Intelligence, 73(10): 92–110.
 
Cristin, R., Kumar, B. S., Priya, C., & Karthick, K. (2020). Deep neural network-based Rider-Cuckoo Search Algorithm for plant disease detection. Artificial intelligence review, 53(8): 4993-5018.
 
Jaisakthi S. M., Mirunalini P., Thenmozhi D. (2019). Grape leaf disease identification using machine learning techniques, in Proceedings of the 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), 60-68. Chennai.
 
Javidan, S. M., Ampatzidis, Y., Vakilian, K. A., & Mohammadzamani, D. (2024b). A Novel Approach for Automated Strawberry Fruit Varieties Classification Using Image Processing and Machine Learning. In 2024 10th International Conference on Artificial Intelligence and Robotics (QICAR). 2024 10th International Conference on Artificial Intelligence and Robotics (QICAR). IEEE.
 
Javidan, S. M., Banakar, A., Vakilian, K. A., & Ampatzidis, Y. (2022, December). A feature selection method using slime mould optimization algorithm in order to diagnose plant leaf diseases. In 2022 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS). Sep 10-12. IEEE.
 
Javidan, S. M., Banakar, A., Vakilian, K. A., & Ampatzidis, Y. (2023). Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning. In Smart Agricultural Technology, 3(3): 100081-100110. Elsevier BV.
 
Javidan, S. M., Banakar, A., Vakilian, K. A., Ampatzidis, Y., & Rahnama, K. (2024a). Diagnosing the spores of tomato fungal diseases using microscopic image processing and machine learning. In Multimedia Tools and Applications. Springer Science and Business Media LLC., 83(5): 20-43.
 
Kumar, S., Sharma, B., Sharma, V. K., Sharma, H., & Bansal, J. C. (2020). Plant leaf disease identification using exponential spider monkey optimization. Sustainable computing: Informatics and systems, 28(3): 100283-100294.
 
Liu B., Tan C., Li S., He J., Wang H. (2020). A data augmentation method based on generative adversarial networks for grape leaf disease identification. April 17-20. IEEE.
 
Mirjalili, S., & Lewis, A. (2013). S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization. In Swarm and Evolutionary Computation, 9(3): 1–14.
 
Mohamadzamani, D., Sajadian, S., & Javidan, S. M. (2020). DDetection of Callosobruchus maculatus F. with image processing and artificial neural network. Applied Entomology and Phytopathology, 88(1): 103-112. (in Persian).
 
Mohammadzamani, D., Javidan, S. M., Zand, M., & Rasouli, M. (2023). Detection of Cucumber Fruit on Plant Image Using Artificial Neural Network. Journal of Agricultural Machinery, 13(1): 18-29.
 
Padol P. B., & Yadav A. A. (2016). SVM classifier based grape leaf disease detection, in Proceedings of the 2016 Conference on Advances in Signal Processing (CASP), April 12-15. Lisbon,
 
Roostaei, P., Rasouli, M., & Babaei, A. (2015). Study of Compatibility and the Effect of Pollen of Some Grape Cultivars on Fruit set and Quantitative and Qualitative Characters of Fruit, cv. Rish Baba Sefid. Plant Production Technology, 7(1): 193-210. (in Persian).
 
Sadeghian, Z., Akbari, E., & Nematzadeh, H. (2021). A hybrid feature selection method based on information theory and binary butterfly optimization algorithm. In Engineering Applications of Artificial Intelligence, 97(5): 104079-104084.
 
Thaiyalnayaki, K., & Joseph, C. (2021). Classification of plant disease using SVM and deep learning. In Materials Today: Proceedings, 47(6): 468–470.
 
Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018). Relief-based feature selection: Introduction and review. In Journal of Biomedical Informatics, 85(3): 189–203.
 
Xie X., Ma Y., Liu B., He J., Li S., & Wang H. (2020). A deep-learning-based real-time detector for grape leaf diseases using improved convolutional neural networks. Front. Plant Sci., 11(2): 751-766