A Robust Image Enhancement System Designed to Improve Plant Leaf Images for Machine Learning based Disease Detection

Plant disease detection is critical for ensuring agricultural productivity and food security, yet the performance of machine learning models is often limited by the quality of input images. This study presents a robust image enhancement system designed to improve plant leaf images for machine learning-based disease detection. The system integrates three complementary techniques such as Non-Local Means (NLM) filtering which was used for noise reduction, then Wiener filtering used for image deblurring and Contrast Limited Adaptive Histogram Equalization (CLAHE) which was finally used for contrast enhancement and haze removal. Plant leaf images were collected from three farms in Uzu-Uwani, Enugu State, Nigeria and they underwent preprocessing steps including resizing, normalization and class balancing using SMOTE. Then the enhanced images were evaluated using a YOLOv5-based plant disease detection model for cassava and maize leaves. The results from the system implementation demonstrate that images processed with the proposed enhancement techniques significantly improved disease detection accuracy, thereby enabling the identification of multiple disease types that were otherwise missed in raw images. The findings highlight the importance of image enhancement in agricultural machine learning pipelines, providing a practical tool for researchers, agronomists, and farmers to improve disease monitoring and crop management.

Plant Disease Detection; Image Enhancement; Non-Local Means (NLM)

1. Baheti, H., Thakare, A., Bhople, Y., Darekar, S., &Dodmani, O. (2023). Tomato plant leaf disease detection using Inception V3. In Intelligent Systems and Applications: Select Proceedings of ICISA 2022 (pp. 49–60). Springer. ISBN 978-981-19658-0-7. [Google Scholar] [Crossref]

2. Bashish, D. A., Braik, M., & Bani-Ahmad, S. (2011). Detection and classification of leaf diseases using k-means-based segmentation and neural-networks-based classification. Information Technology Journal, 10(2), 267–275. https://doi.org/10.3923/itj.2011.267.275 [Google Scholar] [Crossref]

3. Budach, L., Feuerpfeil, M., Ihde, N., Nathansen, A., Noack, N., Patzlaff, H., Harmouch, H., & Naumann, F. (2022). The effects of data quality on ML-model performance (arXiv:2207.14529v1 [cs.DB]). arXiv. [Google Scholar] [Crossref]

4. CHIDI, E. U., UDANOR, C. N., &ANOLIEFO, E. (2024). Exploring the Depths of Visual Understanding: A Comprehensive Review on Real-Time Object of Interest Detection Techniques. Preprints. https://doi.org/10.20944/preprints202402.0583.v1 [Google Scholar] [Crossref]

5. Dwivedi, R., Dey, S., Chckraborty, C., & Tiwari, S. (2021). Grape disease detection network based on multi-task learning and attention features. IEEE Sensors Journal, 21, 17573–17580. [Google Scholar] [Crossref]

6. Ezeani, N. I., Usoro, S. S., Okoye, N. B., Oyeka, D. O., &Iloanusi, O. N. (2024). Smart multi-purpose farm disease monitoring and notification model. Nigerian Journal of Technology, 43(4), 807–817. https://doi.org/10.4314/njt.v43i4.21 [Google Scholar] [Crossref]

7. Gadekallu, T. R., Rajput, D. S., & Reddy, M. P. (2021). A novel PCA–whale optimization-based deep neural network model for classification of tomato plant diseases using GPU. Journal of Real-Time Image Processing, 18, 1383–1396. [Google Scholar] [Crossref]

8. Goel, L., & Nagpal, J. (2022). A systematic review of recent machine learning techniques for plant disease identification and classification. IETE Technical Review, 40(3), 423–439. https://doi.org/10.1080/02564602.2022.2121772 [Google Scholar] [Crossref]

9. Islam, M., Shuvo, M., Shamsojjaman, M., Hasan, S., Hossain, M., & Khatun, T. (2021). An automated convolutional neural network-based approach for paddy leaf disease detection. International Journal of [Google Scholar] [Crossref]

10. Advanced Computer Science and Applications, 12(12), 280–288. https://doi.org/10.14569/IJACSA.2021.0121236 [Google Scholar] [Crossref]

11. Islam, T., Sah, M., Baral, S., & Choudhury, R. R. (2018). A faster technique on rice disease detection using image processing of affected area in agro-field. In Proceedings of the Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 1–6). IEEE. [Google Scholar] [Crossref]

12. Kapoor, A., Bhat, S. I., Shidnal, S., & Mehra, A. (2016). Implementation of IoT (internet of things) and image processing in smart agriculture. In 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) (pp. 21–26). IEEE. [Google Scholar] [Crossref]

13. Kaur, P., & Gautam, V. (2021). Plant biotic disease identification and classification based on leaf image: A review. In Proceedings of the 3rd International Conference on Computing Informatics and Networks: ICCIN 2020 (pp. 597–610). Springer. ISBN 978-981-15-9711-4. [Google Scholar] [Crossref]

14. Kaur, R., Karmakar, G., Xia, F., & Imran, M. (2023). Deep learning: Survey of environmental and camera impacts on internet of things images. Artificial Intelligence Review, 56, 9605–9638. https://doi.org/10.1007/s10462-023-10405-7 [Google Scholar] [Crossref]

15. Kekong P.E, Ajah I.A., Ebere U.C. (2019). Real-time drowsy driver monitoring and detection system using deep learning based behavioural approach. International Journal of Computer Sciences and Engineering 9 (1), 11-21; http://www.ijcseonline.isroset.org/pub_paper/2-IJCSE-08441-18.pdf [Google Scholar] [Crossref]

16. Khirade, S. D., & Patil, A. B. (2015). Plant disease detection using image processing. In Proceedings of the 2015 International Conference on Computing Communication Control and Automation (pp. 768– 771). IEEE. [Google Scholar] [Crossref]

17. Kumar, V. V., Raghunath, K., Natarajan, R., Muthukumaran, V., Joseph, P. R., & Nadesan, T. (2021). Paddy plant disease recognition, risk analysis, and classification using deep convolution neuro-fuzzy network. Journal of Mobile Multimedia, 18(2), 325–348. [Google Scholar] [Crossref]

18. Liu, X., Wu, Z., & Wang, X. (2023). Validity of non-local mean filter and novel denoising method. Virtual Reality & Intelligent Hardware, 5(4), 338–350. https://doi.org/10.1016/j.vrih.2022.08.017 [Google Scholar] [Crossref]

19. Lu, S., Lu, Z., & Zhang, Y.-D. (2019). Pathological brain detection based on AlexNet and transfer learning. Journal of Computer Science, 30, 41–47. [Google Scholar] [Crossref]

20. Monowar, M. M., Hamid, M. A., Kateb, F. A., Ohi, A. Q., &Mridha, M. F. (2022). Self-supervised clustering for leaf disease identification. Agriculture, 12(6), 814. https://doi.org/10.3390/agriculture12060814 [Google Scholar] [Crossref]

21. Mukti, I. Z., & Biswas, D. (2019). Transfer learning-based plant diseases detection using ResNet50. In Proceedings of the 4th International Conference on Electrical Information and Communication Technology (EICT) (pp. 1–6). IEEE. [Google Scholar] [Crossref]

22. Pooja, V., Das, R., & Kanchana, V. (2017). Identification of plant leaf diseases using image processing techniques. In Proceedings of the 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR) (pp. 130–133). IEEE. [Google Scholar] [Crossref]

23. Poyatos, J., Molina, D., Martinez, A. D., Ser, J. D., & Herrera, F. (2023). EvoPruneDeepTL: An evolutionary pruning model for transfer learning-based deep neural networks. Neural Networks, 158, 59–82. https://doi.org/10.1016/j.neunet.2022.12.012 [Google Scholar] [Crossref]

24. Rathore, N. P. S., & Prasad, L. (2021). A comprehensive review of deep learning models for plant disease identification and prediction. International Journal of Engineering Systems Modelling and Simulation, 12(3), 165–179. https://doi.org/10.1504/IJESMS.2021.100400 [Google Scholar] [Crossref]

25. Shamsul-Kamar, N. A., Rahim, S., Ambrose, A., Nha, N. H., Samdin, Z., Hassan, A., Nazre, M., &Terhem, R. (2023). Pest and disease incidence of coniferous species in Taman Saujana Hijau, Putrajaya Urban Park, Malaysia. Journal of Forestry Research, 34(6), 2065–2077. https://doi.org/10.1007/s11676-022-01505-1 [Google Scholar] [Crossref]

26. Shin, J., Mahmud, M. S., Rehman, T. U., Ravichandran, P., Heung, B., & Chang, Y. K. (2023). Trends and prospect of machine vision technology for stresses and diseases detection in precision agriculture. AgriEngineering, 5(1), 20–39. https://doi.org/10.3390/agriengineering5010003 [Google Scholar] [Crossref]

27. Shreyamsha Kumar, B. K. (2012). Image denoising based on Gaussian/bilateral filter and its method noise thresholding. Journal of Signal, Image and Video Processing. https://doi.org/10.1007/s11760-0120372-7 [Google Scholar] [Crossref]

28. Sochima V.E. Asogwa T.C., Lois O.N. Onuigbo C.M., Frank E.O., Ozor G.O., Ebere U.C. (2025)”; Comparing multi-control algorithms for complex nonlinear system: An embedded programmable logic control applications; DOI:http://doi.org/10.11591/ijpeds.v16.i1.pp212-224 [Google Scholar] [Crossref]

29. Sony, A. (2019). Prediction of rice diseases using convolutional neural network (in Rstudio). International Journal of Innovative Science and Research Technology, 4(12), 595–602. [Google Scholar] [Crossref]

30. Thambawita, V., Strümke, I., Hicks, S. A., Halvorsen, P., Parasa, S., & Riegler, M. A. (2021). Impact of image resolution on deep learning performance in endoscopy image classification: An experimental study using a large dataset of endoscopic images. Diagnostics, 11(12), Article 2183. https://doi.org/10.3390/diagnostics11122183 [Google Scholar] [Crossref]

31. Ulagwu-Echefu A., Eneh .I.I. Ebere U.C. (2021). Enhancing realtime supervision and control of industrial processes over wireless network architecture using model predictive controller. International Journal of Research and Innovation in Applied Science (IJRIAS); vol 6; Issue 9. https://rsisinternational.org/journals/ijrias/DigitalLibrary/volume-6-issue-9/56-61.pdf [Google Scholar] [Crossref]

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