SavedAdd your journal

Journals / Journal of Agricultural Sciences / 2026 / Volume: 31 Issue: 2

Mobile Device-Based Detection System of Diseases and Pests in Rose Plants Using Deep Convolutional Neural Networks and Quantization

Burhan Duman(ISPARTA UNIVERSITY OF APPLIED SCIENCES, FACULTY OF TECHNOLOGY, DEPARTMENT OF COMPUTER ENGINEERING, COMPUTER SCIENCES)0000-0001-5614-1556
Pages
—
Publication Date
2025-03-25
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These results demonstrate the effectiveness of using a mobile device to detect rose plant diseases and pests efficiently in natural environments.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In agriculture, the rapid and accurate identification of plant diseases and pests is crucial for maintaining the quality and yield of agricultural products.
📊
Key Findings & Data:For this approach, pre-training was performed with mixed- Convolutional Neural Network (CNN) models DenseNet169, ResNet152, MobileNetV2, VGG19, and NasNet.
💡
Conclusion & Impact:These results demonstrate the effectiveness of using a mobile device to detect rose plant diseases and pests efficiently in natural environments.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Mobile aplication · Image classification · Deep learning

Abstract

In agriculture, the rapid and accurate identification of plant diseases and pests is crucial for maintaining the quality and yield of agricultural products. This study focuses on detecting diseases and pests affecting Rosa damascena Mill. plants through an ensemble learning approach and deploying the model in an Android mobile application-a rarity in similar research. A new dataset was created using images from the natural habitat and season of Rosa damascena Mill., covering seven different diseases and pests. For this approach, pre-training was performed with mixed- Convolutional Neural Network (CNN) models DenseNet169, ResNet152, MobileNetV2, VGG19, and NasNet. DenseNet169 and MobileNetV2, which are the models with the highest classification success obtained from mixed-CNN models, were combined in the new model by fine- tuning with the ensemble learning method. In the performance tests of the model, an accuracy of 95.17% was obtained. In addition, this study introduces an Android mobile application integrating these models, a distinctive feature compared to other similar studies. The best performances of these models, DenseNet169 and MobileNetV2 in both flat buffered and quantized forms, were performed separately on a computer, a physical mobile device, and an Android emulator. MobileNetV2 outperformed DenseNet169 (2271 ms) by having the lowest average inference time (301 ms) on mobile devices. These results demonstrate the effectiveness of using a mobile device to detect rose plant diseases and pests efficiently in natural environments.

🔖

Cite This Article

Duman, B. (2025). Mobile Device-Based Detection System of Diseases and Pests in Rose Plants Using Deep Convolutional Neural Networks and Quantization. Journal of Agricultural Sciences, Volume: 31 Issue: 2. https://doi.org/10.15832/ankutbd.1514972

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (53)

  1. Abulwafa A E (2022). A Survey of Deep Learning Algorithms and its Applications. Nile Journal of Communication and Computer Science 3(1): 28-49
  2. Adebisi J, Srinu S & Mitonga V (2024). Deep Learning Algorithm Analysis of Potato Disease Classification for System on Chip Implementation. Journal of Digital Food, Energy & Water Systems 5(1)
  3. Ahila P, Arivazhagan R S, Arun M & Mirnalini A (2019). Maize leaf disease classification using deep convolutional neural networks. Neural Comput. Appl. 31: 8887-8895
  4. Ahn H, Chen T, Alnaasan N, Shafi A, Abduljabbar M & Subramoni H (2023). Performance Characterization of using Quantization for DNN Inference on Edge Devices: Extended Version arXiv preprint arXiv:2303.05016
  5. Astani M, Hasheminejad M & Vaghefi M (2022). A diverse ensemble classifier for tomato disease recognition. Computers and Electronics in Agriculture 198: 107054
  6. Baydar H (2016). Oil Rose Cultivation and Industry. Science and Technology of Medicinal and Aromatic Plants (5th Expanded Edition). Süleyman Demirel University Press, 51: 290-325 (In Turkish)
  7. Bıtrak O O & Hatırlı S A (2022). Global Oil Rose Market and Turkey’s Role. Selçuk University Akşehir Vocational School Journal of Social Sciences 13: 85-94 (In Turkish)
  8. Chen J, Chen J, Zhang D, Sun Y & Nanehkaran Y A (2020). Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture 173: 105393
  9. Chen Y, Zheng B, Zhang Z, Wang Q, Shen C & Zhang Q (2020). Deep learning on mobile and embedded devices: State-of-the-art, challenges, and future directions. ACM Computing Surveys (CSUR) 53(4): 1-37
  10. Dewangan O (2023). Study and Innovative Approach of Deep Learning Algorithms and Architecture. In Exploring Future Opportunities of Brain-Inspired Artificial Intelligence (pp. 28-45). IGI Global
  11. Ersan R & Başayiğit L (2022). Ecological modelling of potential Isparta Rosa areas (Rosa damascena Mill.). Industrial Crops and Products 176: 114427
  12. Fazili M A, Ganie I B & Hassan Q P (2024). Studies on pharmacological aspects, integrated pest management and economic importance of Rosa damascena L. South African Journal of Botany 174: 534-541
  13. Ferentinos K P (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145: 311-318
  14. Fuentes A F, Yoon S, Lee J & Park D S (2018). High-performance deep neural network-based tomato plant diseases and pests diagnosis system with refinement filter bank. Frontiers in Plant Science 9: 1162
  15. Gholami A, Kim S, Dong Z, Yao Z, Mahoney M W & Keutzer K (2022). A survey of quantization methods for efficient neural network inference. In Low-Power Computer Vision (pp. 291-326). Chapman and Hall/CRC
  16. Harbola G, Rawat M S, Gupta A & Gupta R (2024, May). Intelligent Diagnosis of Potato Leaf Diseases using Deep Learning. In 2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN) 372-377 IEEE
  17. Hemalatha N K, Brunda R N, Prakruthi G S, Prabhu B V B, Shukla A & Narasipura O S J (2022). Sugarcane leaf disease detection through deep learning. In Deep Learning for Sustainable Agriculture, 297-323 https://doi.org/10.1016/B978-0-323-85214-2.00003-3
  18. Huang G, Liu Z, Van Der Maaten L & Weinberger K Q (2017). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700-4708
  19. Iman M, Arabnia H R & Rasheed K (2023). A review of deep transfer learning and recent advancements. Technologies 11(2): 40
  20. Jung K, Lee J I, Kim N, Oh S & Seo D W (2021). Classification of space objects by using deep learning with micro-Doppler signature images. Sensors 21(13): 4365
  21. Karanfil A (2021). Prevalence and molecular characterization of Turkish isolates of the rose viruses. Crop Protection 143: 105565
  22. Katumba A, Okello W S, Murindanyi S, Nakatumba-Nabende J, Bomera M, Mugalu B W & Acur A (2024). Leveraging edge computing and deep learning for the real-time identification of bean plant pathologies. Smart Agricultural Technology 9: 100627. https://doi.org/10.1016/j.atech.2024.100627
  23. Khaleel M I, Sai P G, Kumar A U R, Raja P & Hoang V T (2022). Rose Plant Leaves: Disease Detection and Pesticide Management using CNN. 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), 1067-1072
  24. Khitthuk C, Srikaew A, Attakitmongcol K & Kumsawat P (2018). Plant leaf disease diagnosis from color imagery using co-occurrence matrix and artificial intelligence system. 2018 International Electrical Engineering Congress (IEECON), 1-4
  25. Lee S H (2020). Deep learning based face mask recognition for access control. Journal of the Korea Academia-Industrial Cooperation Society 21(8): 395-400
  26. Liu Y, Sun Y, Xue B, Zhang M, Yen G G & Tan, K C (2021). A survey on evolutionary neural architecture search. IEEE Transactions on Neural Networks and Learning Systems. Ma J, Pang L, Yan L & Xiao J (2020). Detection of black spot of rose based on hyperspectral imaging and convolutional neural network. AgriEngineering 2(4): 556-567
  27. Mahlein AK (2016). Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease 100(2): 241-251
  28. Mutka A M & Bart R S (2015). Image-based phenotyping of plant disease symptoms. Frontiers in Plant Science 5: 734
  29. Nuanmeesri S (2021). A hybrid deep learning and optimized machine learning approach for rose leaf disease classification. Eng. Technol. Appl. Sci. Res. 11(5): 7678-7683
  30. Oikonomidis A, Catal C & Kassahun A (2023). Deep learning for crop yield prediction: a systematic literature review. New Zealand Journal of Crop and Horticultural Science 51(1): 1-26
  31. Pan W, Qin J, Xiang X, Wu Y, Tan Y & Xiang L (2019). A smart mobile diagnosis system for citrus diseases based on densely connected convolutional networks. IEEE Access 7: 87534-87542
  32. Prathiksha B J, Kumar V, Krishnamoorthi M, Poovizhi P, Sowmiya D & Thrishaa, B. (2024, April). Early Accurate Identification of Grape leaf Disease Detection using CNN based VGG-19 model. In 2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC-ROBINS) (pp. 263-269). IEEE
  33. Rajbongshi A, Sarker T, Ahamad M M & Rahman M M (2020). Rose diseases recognition using MobileNet. 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) 1-7
  34. Rakib A F, Rahman R, Razi A A & Hasan A T (2024). A lightweight quantized CNN model for plant disease recognition. Arabian Journal for Science and Engineering 49(3): 4097-4108
  35. Reddy D S, Rajalakshmi P & Mateen M A (2021). A deep learning based approach for classification of abdominal organs using ultrasound images. Biocybernetics and Biomedical Engineering 41(2): 779-791
  36. Sagi O & Rokach L (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8(4): e1249
  37. Sethy P K, Barpanda N K, Rath A K & Behera S K (2020). Deep feature based rice leaf disease identification using support vector machine. Computers and Electronics in Agriculture 175: 105527
  38. Sharma R & Singh G (2015). Access to modern agricultural technologies and farmer household welfare: Evidence from India. Millennial Asia 6(1): 19-43
  39. Simonyan K & Zisserman A (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
  40. Swetharani K & Prasad G V (2021). Design and implementation of an efficient rose leaf disease detection and classification using convolutional neural network. International Journal of Image Mining 4(1): 98-113
  41. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V & Rabinovich A (2015). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1-9
  42. Tao W, Al-Amin M, Chen H, Leu M C, Yin Z & Qin R (2020). Real-time assembly operation recognition with fog computing and transfer learning for human-centered intelligent manufacturing. Procedia Manufacturing 48: 926-931
  43. Uğuz S & Uysal N (2021). Classification of olive leaf diseases using deep convolutional neural networks. Neural Comput. & Applic. 33: 4133– 4149.https://doi.org/10.1007/s00521-020-05235-5
  44. Vallabhajosyula S, Sistla V & Kolli V K K (2022). Transfer learning-based deep ensemble neural network for plant leaf disease detection. J. Plant Dis. Prot. 129: 545-558. https://doi.org/10.1007/s41348-021-00465-8
  45. Vrbančič G & Podgorelec V (2020). Transfer learning with adaptive fine-tuning. IEEE Access 8: 196197-196211
  46. Wang C, Du P, Wu H, Li J, Zhao C & Zhu H (2021). A cucumber leaf disease severity classification method based on the fusion of DeepLabV3+ and U-Net. Computers and Electronics in Agriculture 189: 106373
  47. Weerakoon W M W et al. (2017). Effect of leaf color chart based nitrogen management on the performance of rice crop. Field Crops Research 151: 15-23
  48. Yang Y, Lv H & Chen N (2023). A survey on ensemble learning under the era of deep learning. Artificial Intelligence Review 56(6): 5545- 5589
  49. Yilmaz H (2015). Estimating the economic costs and level of pesticide use in oil rose (Rosa damascena Mill.) orchards: evidence from a survey for the lakes region of Turkey. Erwerbs-obstbau 57(4): 195-202
  50. Yin J, Yu D, Li Z, Guo W & Zhu H (2021). Chinese Rose flower disease recognition method based on deep learning. Frontier Computing: Proceedings of FC 2020, 1309-1319
  51. Yu Z, Wang K, Wan Z, Xie S & Lv Z (2023). Popular deep learning algorithms for disease prediction: a review. Cluster Computing 26(2): 1231-1251
  52. Zhong Y & Zhao M (2020). Research on deep learning in apple leaf disease recognition. Computers and Electronics in Agriculture 168: 105146
  53. Zoph B & Le Q V (2016). Neural architecture search with reinforcement learning. ArXiv Preprint, ArXiv:1611.01578