Incorporating Deep Learning into the Diagnosis of Banana Leaf Spot Diseases for the Protection of Banana Crops

Abstract

Banana crops play a pivotal role in securing global food supplies and supporting economic stability. However, they are confronted with significant challenges stemming from a variety of diseases that not only diminish yields but also compromise the quality of the fruit. Artificial intelligence, especially deep learning, assumes a pivotal role in tackling this challenge by leveraging advanced algorithms and data analysis techniques to enhance disease detection and diagnosis in banana crops, thus contributing significantly to their protection and preservation. To address this challenge, we present the "Banana Leaf Spot Diseases (BananaLSD) Dataset" comprising images of major banana leaf spot diseases and healthy leaves, meticulously labelled by plant pathologists. Using deep learning models, including DenseNet-201, EfficientNet-b0, and VGG16, we achieved remarkable disease classification accuracy rates. DenseNet-201 achieved an impressive 98.12% accuracy. The study analyses performance metrics and visualization by grad-cam technique. These results underscore the potential of deep learning for precise banana leaf disease diagnosis, offering significant implications for crop preservation, economic stability, and global food security.

Keywords

• Banana leaves
• Classification
• Deep learning
• DenseNet-201
• EfficientNet-b0
• VGG16

References

1. Akila M & Deepan P (2018). "Detection and classification of plant leaf diseases by using deep learning algorithm," International Journal of Engineering Research & Technology (IJERT), vol. 6, no. 7, pp. 1-5
2. Albattah W, Nawaz M, Javed A, Masood M & Albahli S (2022). "A novel deep learning method for detection and classification of plant diseases," Complex & Intelligent Systems, pp. 1-18,doi: https://doi.org/10.1007/s40747-021-00536-1.
3. Amara J, Bouaziz B & Algergawy A (2017). "A deep learning-based approach for banana leaf diseases classification," Datenbanksysteme für Business, Technologie und Web (BTW 2017)-Workshopband.
4. Aruraj A, Alex A, Subathra M, Sairamya N, George S T & Ewards S V (2019). "Detection and classification of diseases of banana plant using local binary pattern and support vector machine," in 2019 2nd International Conference on Signal Processing and Communication (ICSPC): IEEE, pp. 231-235, doi: 10.1109/ICSPC46172.2019.8976582.
5. Arman S E, Bhuiyan M A B , Abdullah H M , Islam S, Chowdhury T T & Hossain M A (2023). Bananalsd: A Banana Leaf Images Dataset for Classification of Banana Leaf Diseases Using Machine Learning, doi: 10.17632/9tb7k297ff.1.
6. Arun Y & Viknesh G (2022). "Leaf Classification for Plant Recognition Using EfficientNet Architecture," in 2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC): IEEE, pp. 1-5, doi: 10.1109/ICAECC54045.2022.9716637.
7. Atila U, Ucar M, Akyol K & Ucar E (2021). "Plant leaf disease classification using EfficientNet deep learning model," Ecological Informatics, vol. 61, p. 101182,doi: https://doi.org/10.1016/j.ecoinf.2020.101182.
8. Banan A,Nasiri A & Taheri-Garavand A (2020). "Deep learning-based appearance features extraction for automated carp species identification," Aquacultural Engineering, vol. 89, 102053 pp, doi: https://doi.org/10.1016/j.aquaeng.2020.102053

9. Bhuiyan M A B, Abdullah H M, Arman S E, Rahman S S & Al Mahmud Kv (2023). "BananaSqueezeNet: A very fast, lightweight convolutional neural network for the diagnosis of three prominent banana leaf diseases," Smart Agricultural Technology, vol. 4, p. 100214, , doi: https://doi.org/10.1016/j.atech.2023.100214.
10. Bishop C M & Nasrabadi N M (2006). Pattern recognition and machine learning (no. 4). Springer. Caruana R (1997). "Multitask learning," Machine learning, vol. 28, pp. 41-75, doi: https://doi.org/10.1023/A:1007379606734.
11. Cinar I, Taspinar Y S & Koklu M (2023). "Development of Early Stage Diabetes Prediction Model Based on Stacking Approach," Tehnički glasnik, vol. 17, no. 2, pp. 153-159
12. Chen L, Chen J,Hajimirsadeghi H & Mori G (2020). "Adapting grad-cam for embedding networks," in proceedings of the IEEE/CVF winter conference on applications of computer vision, 2020, pp. 2794-2803
13. Davis J & Goadrich M (2006). "The relationship between Precision-Recall and ROC curves," in Proceedings of the 23rd international conference on Machine learning, pp. 233-240, doi: https://doi.org/10.1145/1143844.1143874.
14. Evuri S R R (2022). "Banana Leaf Disease Detection With Multi Feature Extraction Techniques Using SVM," Dublin, National College of Ireland.
15. Feyzioglu A & Taspinar Y S (2023). "Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models," International Journal of Applied Mathematics Electronics and Computers, vol. 11, no. 1, pp. 37-43,doi: https://doi.org/10.18100/ijamec.1253191.
16. Hart P E, Stork D G & Duda R O (2000). Pattern classification. Wiley Hoboken. Koklu M, Unlersen M F, Ozkan I A, Aslan M F &Sabanci K (2022). "A CNN-SVM study based on selected deep features for grapevine leaves classification," Measurement, vol. 188, 110425 pp
17. Kursun R, Cinar I, Taspinar Y S & Koklu M (2022)."Flower recognition system with optimized features for deep features," in 2022 11th Mediterranean Conference on Embedded Computing (MECO): IEEE, pp. 1-4
18. Kursun R, Bastas K K & Koklu M (2023). "Segmentation of dry bean (Phaseolus vulgaris L.) leaf disease images with U-Net and classification using deep learning algorithms," European Food Research and Technology, pp. 1-16,doi: https://doi.org/10.1007/s00217-023-04319-5.
19. Kursun R & Koklu M (2023). "Enhancing Explainability in Plant Disease Classification using Score-CAM: Improving Early Diagnosis for Agricultural Productivity," in 2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS),vol. 1: IEEE, pp. 759-764, doi: 10.1109/IDAACS58523.2023.10348713.
20. detection," Journal Krishnan V G , Deepa J, Rao P V, Divya V & Kaviarasan S (2022). "An automated segmentation and classification model for banana leaf disease of Applied Biology and Biotechnology, vol. 10, no. 1, pp. 213-220, doi: http://dx.doi.org/10.7324/JABB.2021.100126.
21. Kumar R, Chug A, Singh A P & Singh D (2022). "A Systematic analysis of machine learning and deep learning based approaches for plant leaf disease classification: a review," Journal of Sensors, vol. 2022, doi: https://doi.org/10.1155/2022/3287561.
22. Koklu M, Kursun R, Yasin E T & Taspinar Y S (2023). "Detection of Defects in Soybean Seeds by Extracting Deep Features with SqueezeNet," in 2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS),vol. 1: IEEE, pp. 713-717, doi: 10.1109/IDAACS58523.2023.10348939.
23. Li L, Zhang S & Wang B (2021). "Plant disease detection and classification by deep learning—a review," IEEE Access, vol. 9, pp. 56683 56698,doi: 10.1109/ACCESS.2021.3069646.
24. Lv Q, Zhang S & Wang Y (2022). "Deep learning model of image classification using machine learning," Advances in Multimedia, vol. 2022, doi: https://doi.org/10.1155/2022/3351256.
25. Lamba M, Gigras Y & Dhull A (2021). "Classification of plant diseases using machine and deep learning," Open Computer Science, vol. 11, no. 1, pp. 491-508, 2021, doi: https://doi.org/10.1515/comp-2020-0122.
26. Lu T, Han B, Chen L, Yu F & Xue C (2021). "A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning," Scientific Reports, vol. 11, no. 1, 15824 pp
27. Mohanty S P,Hughes D P & Salathé M (2016). "Using deep learning for image-based plant disease detection," Frontiers in plant science, vol. 7, p. 1419, doi: https://doi.org/10.3389/fpls.2016.01419.
28. Platform," in Mirandilla J P C,Bating C B,Cabatuan M K & Jose J A C (2022). "Classification of Philippine Herbal Medicine Plant Using EfficientNet on Mobile TENCON 2022-2022 IEEE Region 10 Conference (TENCON): IEEE, pp. 1-6, doi: 10.1109/TENCON55691.2022.9977715.
29. Narayanan K L, Krishnan R S, Robinson Y H, Julie E G, Vimal S, Saravanan V & Kaliappan M (2022). ."Banana plant disease classification using hybrid convolutional neural network," Computational Intelligence and Neuroscience, vol. 2022.
30. Singh R & Athisayamani S (2020). "Banana leaf diseased image classification using novel HEAP auto encoder (HAE) deep learning," Multimedia Tools and Applications, vol. 79, no. 41-42, pp. 30601-30613, doi: https://doi.org/10.1007/s11042-020-09521-1.
31. Saleem M H,Potgieter J & Arif K M (2019). "Plant disease detection and classification by deep learning," Plants, vol. 8, no. 11, 468 pp,doi: https://doi.org/10.3390/plants8110468.
32. Simonyan K & Zisserman A (2014). "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556,doi: https://doi.org/10.48550/arXiv.1409.1556.
33. Saritas M M, Taspinar Y S, Cinar I & Koklu M (2023). "Railway Track Fault Detection with ResNet Deep Learning Models."
34. Selvaraju R R, Das A, Vedantam R, Cogswell M, Parikh D & Batra D (2016). "Grad-CAM: Why did you say that?," arXiv preprint arXiv:1611.07450,doi: 10.48550/arXiv.1611.07450
35. Selvaraju R R, Cogswell M, Das A, Vedantam R, Parikh D & Batra D (2017). "Grad-cam: Visual explanations from deep networks via gradient based localization," in Proceedings of the IEEE international conference on computer vision, pp. 618-626, doi: 10.1109/ICCV.2017.74.
36. Selvaraju R, Cogswell M, Das A, Vedantam R, Parikh D & Batra D (2016). "Grad-CAM: visual explanations from deep networks via gradient based localization. arXiv [cs. CV]," ed.
37. Tan M & Le Q (2019). "Efficientnet: Rethinking model scaling for convolutional neural networks," in International conference on machine learning: PMLR, pp. 6105-611
38. Theckedath & Sedamkar R (2020). "Detecting affect states using VGG16, ResNet50 and SE-ResNet50 networks," SN Computer Science, vol. 1, pp. 1-7,doi: https://doi.org/10.1007/s42979-020-0114-9.
39. Taspinar Y S, Dogan M, Cinar I, Kursun R, Ozkan I A & Koklu M (2022). "Computer vision classification of dry beans (Phaseolus vulgaris L.) based on deep transfer learning techniques," European Food Research and Technology, vol. 248, no. 11, pp. 2707-2725
40. Yasin E T,Kursun R & Koklu M (2024). "Machine Learning-Based Classification of Mulberry Leaf Diseases," in Proceedings of International Conference on Intelligent Systems and New Applications,vol. 2, pp. 58-63, doi: 10.58190/icisna.2024.91.
41. Yildiz M B, Yasin E T & Koklu M (2024). "Fisheye freshness detection using common deep learning algorithms and machine learning methods with a developed mobile application," European Food Research and Technology, pp. 1-14,doi: 10.1007/s00217-024-04493-0.
42. Yasin E & Koklu M (2023). "Utilizing Random Forests for the Classification of Pudina Leaves through Feature Extraction with InceptionV3 and VGG19," in Proceedings of the International Conference on New Trends in Applied Sciences,vol. 1, pp. 1-8, doi: 10.58190/icontas.2023.48.
43. Yasar A, Kaya E & Saritas I (2016). "Classification of wheat types by artificial neural network," International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. 1, pp. 12-15,doi: https://doi.org/10.18201/ijisae.64198.
44. Yasin E T & Koklu M (2023). "Classification of Organic and Recyclable Waste based on Feature Extraction and Machine Learning Algorithms."
45. Taspinar Y S (2022). "Classification and Analysis of Tomato Species with Convolutional Neural Networks," Selcuk Journal of Agriculture and Food Sciences, vol. 36, no. 3, pp. 515-520
46. Vipinadas M & Thamizharasi A (2016). "Detection and Grading of diseases in Banana leaves using Machine Learning," International Journal of Scientific & Engineering Research, vol. 7, no. 7, pp. 916-924