Citation :

Arundhati Bora, Parismita Sarma, Dankan Gowda V, Manash P. Bhuyan, Jumi Sarmah, "Image Processing-Based Efficient Hybrid Feature Extraction Approach for Plant Leaf Disease Recognition," International Journal of Electronics and Communication Engineering, vol. 13, no. 9, pp. 101-119, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I9P107

Abstract

In this paper, a novel hybrid method for plant leaf disease recognition is presented, combining image processing techniques with Deep Learning and handcrafted feature analysis. Leaf images are processed using a Convolutional Neural Network (CNN) to automatically extract hierarchical features, while handcrafted features based on leaf shape, color, and texture provide complementary disease-related information. The combined feature set is fed into a multi-class classifier to accurately identify specific disease types. Evaluation on a diverse dataset of healthy and diseased leaves demonstrates an average accuracy of 98%, outperforming approaches that rely solely on deep learning or handcrafted features. The results highlight the effectiveness of integrating deep and handcrafted features, offering a reliable, scalable solution for precision agriculture and serving as a foundation for future research in automated plant disease detection.

Keywords

Image Processing, Plant Leaf Disease Recognition, Hybrid feature extraction, Deep learning, MobileNetV2, Transfer Learning, Hand-Crafted Features, Texture Analysis, Color Histograms, Precision Agriculture.

References

1. Saidulu Yerukala, and R.V. Prakash, “Comprehensive Study of Plant Leaf Disease Detection and Classification using Deep Learning Approach,” 2025 3^rd International Conference on Data Science and Network Security (ICDSNS), Tiptur, India, pp. 1-5, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
2. Sherihan Aboelenin et al., “A Hybrid Framework for Plant Leaf Disease Detection and Classification using Convolutional Neural Networks and Vision Transformer,” Complex & Intelligent Systems, vol. 11, pp. 1-17, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
3. B. Nithya et al., “Hybrid Deep Learning Approach for Accurate and Real-Time Detection of Rice and Potato Leaf Diseases,” 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI), Erode, India, pp. 1866-1871, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
4. Junde Chen et al., “Using Deep Transfer Learning for Image-based Plant Disease Identification,” Computers and Electronics in Agriculture, vol. 173, 2020.
   [CrossRef] [Google Scholar] [Publisher Link]
5. Jalla Vamshi et al., “Novel Plant Leaf Disease Detection Approach using Hybrid Deep Learning Strategy,” 2024 5^th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India, pp. 266-273, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
6. Shiva Mehta, and Vinay Kukreja, “Enhancing Soybean Disease Diagnosis using CNN-based Feature Extraction and Random Forest Classification,” 2025 International Conference on Computing Technologies (ICOCT), Bengaluru, India, pp. 1-6, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
7. Hao Yang et al., “Multi-Level Convolutional Feature Fusion for Plant Disease Identification,” 2025 International Conference on Computer Vision, Image Processing and Computational Photography (CVIP), Hangzhou, China, pp. 261-267, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
8. Mayank Raj et al., “CNN and Random Forest Hybrid Model for Tea Leaf Diseases Multiclassification,” 2024 International Conference on Automation and Computation (AUTOCOM), Dehradun, India, pp. 52-56, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
9. Bikash Chandra Karmokar et al., “Tea Leaf Diseases Recognition using Neural Network Ensemble,” International Journal of Computer Applications, vol. 114, no. 17, pp. 27-30, 2015.
   [CrossRef] [Google Scholar] [Publisher Link]
10. Harminder Kaur, Neeraj Raheja, and Vaishali Mehta Wadhwa, “Hybrid Deep Learning Approach Using Resnet-50 BiLSTM and AdaBoost for High Precision Potato Leaf Disease Classification,” 2025 14^th International Conference on System Modeling & Advancement in Research Trends (SMART), Moradabad, Uttar Pradesh, India, pp. 554-560, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
11. Varun Kumar, and Rituranis, “Integrative Framework for Accurate Corriander Disease Classification: Unifying Deep Learning and Machine Learning Approaches with Feature-Rich Hybrid Models,” 2024 4^th International Conference on Technological Advancements in Computational Sciences (ICTACS), Tashkent, Uzbekistan, pp. 121-127, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
12. V. Sunil Kumar et al., “Recognition and Classification of Apple and Sugarcane Plant Leaf Diseases using SVM with DAE Models,” 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT), Bengaluru, India, pp. 1-7, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
13. X.E. Pantazi, D. Moshou, and A.A. Tamouridou, “Automated Leaf Disease Detection in Different Crop Species through Image Features Analysis and One Class Classifiers,” Computers and Electronics in Agriculture, vol. 156, pp. 96-104, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
14. Saiful Islam et al., “CNN-Transformer Fusion Network for Multi-Class Leaf Disease Classification,” 2026 5^th International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE), Rajshahi, Bangladesh, pp. 1-6, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
15. Anuruk Prommakhot et al., “Hybrid CNN and Transformer-Based Sequential Learning Techniques for Plant Disease Classification,” IEEE Access, vol. 13, pp. 122876-122887, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
16. Himanshu B. Patel, and Nitin J. Patil, “Enhanced CNN for Fruit Disease Detection and Grading Classification Using SSDAE-SVM for Postharvest Fruits,” IEEE Sensors Journal, vol. 24, no. 5, pp. 6719-6732, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
17. Nishant Garg et al., “Classification of Tomato Diseases using Hybrid Model (CNN-SVM),” 2022 10^th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, pp. 1-5, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
18. Ankita Suryavanshi et al., “A Hybrid CNN-SVM-Deep Learning Approach for Multi-Level Downy Mildew Disease Recognition in Spinach Leaves,” 2023 1^st International Conference on Optimization Techniques for Learning (ICOTL), Bengaluru, India, pp. 1-6 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
19. Vrinda Mishra, Vineet Sharma, and Upendra Mishra, “A Hybrid Approach for Leaf Classification Using Machine Learning and Deep Learning,” 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT), Kollam, India, pp. 1589-1593 2023. [CrossRef] [Google Scholar] [Publisher Link]
20. Mahantesh Sajjan et al., “A Comparative Analysis of Color Features for Classification of Bulk Chilli,” 2016 2^nd International Conference on Contemporary Computing and Informatics (IC3I), Greater Noida, India, pp. 427-432, 2016.
    [CrossRef] [Google Scholar] [Publisher Link]
21. Yashu et al., “Advancing Pear Disease Recognition: A Hybrid CNN-Random Forest Approach,” 2023 3^rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), Bangalore, India, pp. 1-5, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
22. Arundhati Bora, and Parismita Sarma, “A Multi-Feature Fusion Framework for Comprehensive Evaluation of Classifier Performance in Automated Leaf Disease Detection,” 2025 IEEE 2^nd International Conference for Women in Computing (InCoWoCo), India, pp. 1-6, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]