Machine learning and deep learning models integration: Detection of apple leaves diseases
Abstract
Differentiating between sick states is made possible by variations in the visual properties of leaf diameters, which makes leaves useful markers for disease diagnosis. Recognizing the unique patterns that infections leave on foliage is crucial to correctly diagnosing diseases. Plant inspection has often been carried out by specialists or cultivators, which may be expensive and time-consuming. Thus, illness detection automation is essential, particularly in places where access to experts is restricted. In order to create a model for identifying illnesses on apple leaves, this study uses five classification algorithms: Inception V3, Support Vector Machine (SVM), Random Forest, and Decision Tree. Apple Scab, Apple Spot, and Apple Rust are the main subjects of the study. To detect these illnesses, a comparative examination of machine learning and deep learning models is carried out using the "Apple Leaves Disease Dataset." Among all the models tested, VGG19 achieved the highest test accuracy, reaching an impressive 95 percent.
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1. Samajpati, B.J.; Degadwala, S.D. A Survey on Apple Fruit Diseases Detection and Classification. Int. J. Comput. Appl. 2015, 130, 25–32.
2. Khirade, S.D.; Patil, A. Plant disease detection using image processing. In Proceedings of the 2015 International Conference on Computing Communication Control and Automation, Pune, India, 26–27 February 2015; pp. 768–771.
3. Rao, A.; Kulkarni, S. A Hybrid Approach for Plant Leaf Disease Detection and Classification Using Digital Image Processing Methods. Int. J. Electr. Eng. Educ. 2020, 0020720920953126.]
4. Phadikar, S.; Sil, J.; Das, A.K. Rice diseases classification using feature selection and rule generation techniques. Comput. Electron. Agric. 2013, 90, 76–85.
5. Rastogi, A.; Arora, R.; Sharma, S. Leaf disease detection and grading using computer vision technology & fuzzy logic. In Proceedings of the 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 19–20 February 2015; pp. 500–505.
6. Singh, R.; Gehlot, A.; Prajapat, M.K.; Singh, B. Artificial Intelligence in Agriculture; CRC Press: London, UK, 2021.
7. Gupta, J., Pathak, S., & Kumar, G. (2022). Deep learning (CNN) and transfer learning: A review. Journal of Physics: Conference Series, 2273(1), 012029. doi:https://doi.org/10.1088/1742-6596/2273/1/012029
8. Li, L.; Zhang, S.; Wang, B. Apple Leaf Disease Identification with a Small and Imbalanced Dataset Based on Lightweight Convolutional Networks. Sensors 2021, 22, 173.
9. Di Franco, G.; Santurro, M. Machine learning, artificial neural networks and social research. Qual. Quant. 2020, 55, 1007–1025.
10. Abdullah, D.M.; Abdulazeez, A.M. Machine Learning Applications based on SVM Classification A Review. Qubahan Acad. J. 2021, 1, 81–90.
11. Sood, S., Singh, H. A comparative study of grape crop disease classification using various transfer learning techniques. Multimed Tools Appl 83, 4359–4382 (2024). https://doi.org/10.1007/s11042-023-14808-0
12. Kaur, P., Harnal, S., Gautam, V. et al. Performance analysis of segmentation models to detect leaf diseases in tomato plant. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-16238-4
13. Mitra, M. K-Means Clustering in Machine Learning—A Review. Peer Nest 2019, 1, 4.
14. Kaur, P., Harnal, S., Gautam, V. et al. A novel transfer deep learning method for detection and classification of plant leaf disease. J Ambient Intell Human Comput 14, 12407–12424 (2023). https://doi.org/10.1007/s12652-022-04331-9
15. Barhate D, Pathak S, Dubey AK. Hyperparameter-tuned batch-updated stochastic gradient descent: Plant species identification by using hybrid deep learning. Ecological Informatics. 2023 Jul 1;75:102094.
16. Bonkra, A., Bhatt, P. K., Rosak-Szyrocka, J., Muduli, K., Pilař, L., Kaur, A., ... & Rana, A. K. (2023). Apple leave disease detection using collaborative ml/dl and artificial intelligence methods: Scientometric analysis. International journal of environmental research and public health, 20(4), 3222.
17. Bednarz, C. W., Shurley, W. D., & Anthony, W. S. (2002).Losses in yield, quality, and profitability of cotton from improper harvest timing. Agronomy Journal, 94(5), 1004-1011.
18. Linker, R., Cohen, O., &Naor, A. (2012).Determination of the number of green apples in RGB images recorded in orchards. Computers and Electronics in Agriculture, 81, 45-57.
19. A. Bonkra, P. Dhiman, S. Goyal and N. Gupta, "A Systematic Study: Implication of Deep Learning in Plant Disease Detection," 2022 IEEE International Conference on Current Development in Engineering and Technology (CCET), Bhopal, India, 2022, pp. 1-6, doi: 10.1109/CCET56606.2022.10080181.
20. Bargoti, S., & Underwood, J. P. (2017).Image segmentation for fruit detection and yield estimation in apple orchards. Journal of Field Robotics, 34(6), 1039-1060.
21. Bonkra A, Noonia A, Kaur A. Apple leaf diseases detection system: A review of the different segmentation and deep learning methods. InInternational Conference on Artificial Intelligence and Data Science 2021 Dec 17 (pp. 263-278). Cham: Springer Nature Switzerland.
22. Gené-Mola, J., Vilaplana, V., Rosell-Polo, J. R., Morros, J. R., Ruiz-Hidalgo, J., & Gregorio, E. (2019).Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities. Computers and Electronics in Agriculture, 162, 689-698.
23. O. Sharma, Rajgaurang, S. Mohapatra, J. Mohanty, P. Dhiman and A. Bonkra, "Predicting Agriculture Leaf Diseases (Potato): An Automated Approach using Hyper-parameter Tuning and Deep Learning," 2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC), Jalandhar, India, 2023, pp. 490-493, doi: 10.1109/ICSCCC58608.2023.10176819.
24. Sabzi, S., Abbaspour-Gilandeh, Y., García-Mateos, G., Ruiz-Canales, A., & Molina-Martínez, J. M. (2018).Segmentation of apples in aerial images under sixteen different lighting conditions using color and texture for optimal irrigation. Water, 10(11), 1634.
25. Perveen, K., Kumar, S., Kansal, S., Soni, M., Alshaikh, N. A., Batool, S., ... & Osei, B. (2023). Multidimensional Attention-Based CNN Model for Identifying Apple Leaf Disease. Journal of Food Quality, 2023.
26. Li, L., Wang, B., Li, Y., & Yang, H. (2023). Diagnosis and mobile application of apple leaf disease degree based on a small-sample dataset. Plants, 12(4), 786.
27. Li, H., Shi, L., Fang, S., & Yin, F. (2023). Real-Time Detection of Apple Leaf Diseases in Natural Scenes Based on YOLOv5. Agriculture, 13(4), 878.
28. Rohini, V., & Jyothsna, R. (2023). Disease Detection in Apple Tree Leaves Using CNN Algorithms. Journal of Survey in Fisheries Sciences, 10(4S), 1097-1101.
29. Zhu, R., Zou, H., Li, Z., & Ni, R. (2022). Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases. Plants, 12(1), 169.
30. Vishnoi, V. K., Kumar, K., Kumar, B., Mohan, S., & Khan, A. A. (2022). Detection of Apple Plant Diseases Using Leaf Images Through Convolutional Neural Network. IEEE Access, 11, 6594-6609.
31. Liu, S., Qiao, Y., Li, J., Zhang, H., Zhang, M., & Wang, M. (2022). An Improved Lightweight Network for Real-Time Detection of Apple Leaf Diseases in Natural Scenes. Agronomy, 12(10), 2363.
32. Sangeetha, K., Rima, P., Kumar, P., & Preethees, S. (2022, March). Apple leaf disease detection using deep learning. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1063-1067). IEEE.
33. Tian, L., Zhang, H., Liu, B., Zhang, J., Duan, N., Yuan, A., & Huo, Y. (2022). VMF-SSD: a Novel v-space based multi-scale feature fusion SSD for apple leaf disease detection. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
34. Sai, A. M., & Patil, N. (2022, October). Comparative Analysis of Machine Learning Algorithms for Disease Detection in Apple Leaves. In 2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) (pp. 239-244). IEEE.
35. Yu, H., & Kim, S. (2012). SVM Tutorial-Classification, Regression and Ranking. Handbook of Natural computing, 1, 479-506.
36. Finley, T., & Joachims, T. (2005, August). Supervised clustering with support vector machines. In Proceedings of the 22nd international conference on Machine learning (pp. 217-224).
37. Fratello, M., & Tagliaferri, R. (2018). Decision trees and random forests. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 374.
38. Hegelich, S. (2016). Decision trees and random forests: Machine learning techniques to classify rare events. European Policy Analysis, 2(1), 98-120.
39. Ikechukwu, A. V., Murali, S., Deepu, R., & Shivamurthy, R. C. (2021). ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images. Global Transitions Proceedings, 2(2), 375-381.
40. Bansal, M., Kumar, M., Sachdeva, M., & Mittal, A. (2021). Transfer learning for image classification using VGG19: Caltech-101 image data set. Journal of ambient intelligence and humanized computing, 1-12.
41. Devi, D., Prasanna, N., Ram, R. A. S., & Mohamed, N. N. (2023, December). Tomato leaves disease identification by deep convolution neural network using inception V3. In AIP Conference Proceedings (Vol. 2914, No. 1). AIP Publishing.
DOI: https://doi.org/10.32629/jai.v7i5.1756
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