Improved Deep Learning Models for Plants Diseases Detection for Smart Farming

Hiyam Hatem
International Journal of Computational and Electronic Aspects in Engineering
Volume 6: Issue 1, March 2025, pp 12-23

Author's Information
Hiyam Hatem1 
Corresponding Author
1Collage of Computer Science and Information Technology, University of Sumer, Iraq
hiamhatim2005@gmail.com
Research Paper -- Peer Review
First online on – 10 March 2025

Open Access article under Creative Commons License

Cite this article –Hiyam Hatem“Improved Deep Learning Models for Plants Diseases Detection for Smart Farming”, International Journal of Computational and Electronic Aspects in Engineering, RAME Publishers, vol. 6, Issue 1, pp. 12-23, 2025.
https://doi.org/10.26706/ijceae.6.1.20250204

Abstract:-
Traditional farming methods consume time and effort, which affects productivity. Smart agriculture aims to improve decision-making and crop management using IoT's connectivity and data analysis capabilities. Plant diseases result in significant financial losses in the farming sector. Accurately detecting of diseases is crucial for ensuring the long-term sustainability of agriculture. Deep learning, has recently garnered significant attention for plant and weed detection, disease diagnosis, and pest classification in agricultural industries. In this paper, the most previous studies have been discussed focusing on a several plant species and a specific type of disease. The dataset used in these models is (Plant Village). The dataset includes 14 types of plants images with 39 different classes of plant diseases. We propose an enhanced deep learning models (EfficientNetB2, Xception, ResNet50) by adding a custom classification layer, which significantly improved the model's accuracy and classification performance. The improved models achieved accuracy as: (EfficientNetB2 is 97.70%, ResNet50 is 97.86% and Xception is 98.97%). The main aim of this paper is to enables the farmers to detect plant diseases in early stage of disease without consulting experts.
Index Terms:-
Artificial Intelligence, Computer Vision, Deep Learning Models, Image Classification, Plant Disease Detection
REFERENCES
  1. D. R. Vincent, N. Deepa, D. Elavarasan, K. Srinivasan, S. H. Chauhdary, and C. Iwendi, “Sensors driven ai-based agriculture recommendation model for assessing land suitability,” Sensors (Switzerland), vol. 19, no. 17, 2019, doi: 10.3390/s19173667.
  2. A. Ksibi, M. Ayadi, B. O. Soufiene, M. M. Jamjoom, and Z. Ullah, “MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases,” Appl. Sci., vol. 12, no. 20, 2022, doi: 10.3390/app122010278.
  3. K. Bakthavatchalam et al., “IoT Framework for Measurement and Precision Agriculture: Predicting the Crop Using Machine Learning Algorithms,” Technologies, vol. 10, no. 1, p. 13, 2022, doi: 10.3390/technologies10010013.
  4. H. Ulutaş and V. Aslantaş, “Design of Efficient Methods for the Detection of Tomato Leaf Disease Utilizing Proposed Ensemble CNN Model,” Electron., vol. 12, no. 4, 2023, doi: 10.3390/electronics12040827.
  5. Y. Guo et al., “Plant disease identification based on deep learning algorithm in smart farming,” Discret. Dyn. Nat. Soc., vol. 2020, no. 1, p. 2479172, 2020.
  6. H. Jabbar, R. M. Hatem, and D. A. Fattah, “Segmentation of brain tissue using improved kernelized rough-fuzzy c-means technique,” Indones. J. Electr. Eng. Comput. Sci., vol. 32, no. 1, pp. 216–226, 2023.
  7. V. Tiwari, R. C. Joshi, and M. K. Dutta, “Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images,” Ecol. Inform., vol. 63, p. 101289, 2021.
  8. R. Katafuchi and T. Tokunaga, “Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection based on Reconstructability of Colors,” Proc. Int. Conf. Image Process. Vis. Eng. Improv. 2021, pp. 112–120, 2021, doi: 10.5220/0010463201120120.
  9. S. M. Hassan and A. K. Maji, “Plant Disease Identification Using a Novel Convolutional Neural Network,” IEEE Access, vol. 10, pp. 5390–5401, 2022, doi: 10.1109/ACCESS.2022.3141371.
  10. G. Geetharamani and A. Pandian, “Identification of plant leaf diseases using a nine-layer deep convolutional neural network,” Comput. Electr. Eng., vol. 76, pp. 323–338, 2019.
  11. M. Prabhakar, R. Purushothaman, and D. P. Awasthi, “Deep learning based assessment of disease severity for early blight in tomato crop,” Multimed. Tools Appl., vol. 79, pp. 28773–28784, 2020.
  12. M. A. Jasim and J. M. Al-Tuwaijari, “Plant leaf diseases detection and classification using image processing and deep learning techniques,” in 2020 International Conference on Computer Science and Software Engineering (CSASE), 2020, pp. 259–265.
  13. L. Falaschetti, L. Manoni, D. Di Leo, D. Pau, V. Tomaselli, and C. Turchetti, “A CNN-based image detector for plant leaf diseases classification,” HardwareX, vol. 12, p. e00363, 2022.
  14. S. Jasrotia, J. Yadav, N. Rajpal, M. Arora, and J. Chaudhary, “Convolutional neural network based maize plant disease identification,” Procedia Comput. Sci., vol. 218, pp. 1712–1721, 2023.
  15. W. Shafik, A. Tufail, C. De Silva Liyanage, and R. A. A. H. M. Apong, “Using transfer learning-based plant disease classification and detection for sustainable agriculture,” BMC Plant Biol., vol. 24, no. 1, p. 136, 2024.
  16. D. Hughes and M. Salathé, “An open access repository of images on plant health to enable the development of mobile disease diagnostics,” arXiv Prepr. arXiv1511.08060, 2015.

    17. To view full paper, Download here

For authors

Author's guidelines Publication Ethics Publication Policies Artical Processing Charges Call for paper Frequently Asked Questions(FAQS) View All Volumes and Issues

Publishing with