Open Access
Subscription Access
Open Access
Subscription Access
A Hybrid Machine Learning Approach for Early Detection of Paddy Blight Disease
Subscribe/Renew Journal
Paddy blight is a widespread disease that affects various parts of the paddy plant, including leaves, bark, nodes, neck, part of rays, and leaves sheath. The symptoms of the disease manifest as pale yellow to pale green leaves with eye-shaped lesions, distorted margins, and gray or white centers. As the lesions expand, the leaves progressively wither and dry out, eventually leading to rot and death of the affected plant parts. In this study, we propose a machine learning algorithm for detecting paddy disease by analyzing changes in paddy leaves and correlating them with existing paddy images. The algorithm incorporates fuzzy logic and deep learning techniques to enhance disease detection accuracy and provide appropriate treatment recommendations. By leveraging the power of these advanced technologies, the proposed approach aims to facilitate early detection and effective management of paddy diseases, ultimately improving crop yield and ensuring food security.
Keywords
Paddy Blight, Disease Detection, Machine Learning, Fuzzy Logic, Deep Learning, Treatment Recommendation.
Subscription
Login to verify subscription
User
Font Size
Information
- V. Rajpoot and A.S. Jalal, “Automatic Early Detection of Rice Leaf Diseases using Hybrid Deep Learning and Machine Learning Methods”, Multimedia Tools and Applications, Vol. 34, pp. 1-27, 2023.
- A. Chug and D. Singh, “A Novel Framework for Image-Based Plant Disease Detection using Hybrid Deep Learning Approach”, Soft Computing, Vol. 74, No. 1, pp. 1-26, 2022.
- J. Gowrishankar and N. Narmadha, “Convolutional Neural Network
- Classification on 2D Craniofacial Images”, International Journal of Grid and Distributed Computing, Vol. 13, No. 1, pp. 1026-1032, 2020.
- R.D. Aruna and B. Debtera, “An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease using Nanotechnology Sensors”, Computational Intelligence and Neuroscience, Vol. 2022, pp. 1-12, 2022.
- K. Anandhan and A.S. Singh, “Detection of Paddy Crops Diseases and Early Diagnosis using Faster Regional Convolutional Neural Networks”, Proceedings of International Conference on Advance Computing and Innovative Technologies in Engineering, pp. 898-902, 2021.
- S. Lamba, S. Rani and S.H. Ahmed, “A Novel Hybrid Severity Prediction Model for Blast Paddy Disease using Machine Learning”, Sustainability, Vol. 15, No. 2, pp. 1502-1512, 2023.
- A. Sirohi and A. Malik, “A Hybrid Model for the Classification of Sunflower Diseases using Deep Learning”, Proceedings of International Conference on Intelligent Engineering and Management, pp. 58-62, 2021.
- S. Chidambaram and D. Shreecharan, “Hyperspectral Image Classification using Denoised Stacked Auto Encoder-Based Restricted Boltzmann Machine Classifier”, Proceedings of International Conference on Hybrid Intelligent Systems, pp. 213-221, 2022.
- R. Sharma, A. Bansal and A. Kaur, “Rice Leaf blight Disease Detection using Multi-Classification Deep Learning Model”, Proceedings of International Conference on Reliability, Infocom Technologies and Optimization Trends and Future Directions, pp. 1-5, 2022.
- Z. Liu, M. Tausif and Q. Umer, “Internet of Things (IoT) and Machine Learning Model of Plant Disease Prediction-
- Blister Blight for Tea Plant”, IEEE Access, Vol. 10, pp. 44934-44944, 2022.
- G. Dhiman and K. Srihari, “Multi-Modal Active Learning with Deep Reinforcement Learning for Target Feature Extraction in Multi-Media Image Processing Applications”, Multimedia Tools and Applications, Vol. 82, No. 4, pp. 5343-5367, 2023.
- R. Sangeetha and J. Lloret, “An Improved Agro Deep Learning Model for Detection of Panama Wilts Disease in Banana Leaves”, Agri Engineering, Vol. 5, No. 2, pp. 660-679, 2023.
- N. Jiwani and M. Alibakhshikenari, “Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) using Computational Deep Learning”, IEEE Access, Vol. 11, pp. 29541-29553, 2023.
- J. Zhang and Y. He, “Rice Bacterial Blight Resistant Cultivar Selection based on Visible/Near-Infrared Spectrum and Deep Learning”, Plant Methods, Vol. 18, No. 1, pp. 1-16, 2022.
- M.H. Tunio and I. Memon, “Identification and Classification of Rice Plant Disease using Hybrid Transfer Learning”, Proceedings of International Conference on Computer on Wavelet Active Media Technology and Information Processing, pp. 525-529, 2021.
Abstract Views: 112
PDF Views: 2