RICE PLANT DISEASE CLASSIFICATION USING TRANSFER LEARNING OF DEEP CONVOLUTION NEURAL NETWORK

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1. Advances in Hyperspectral Image Classification Based on Convolutional Neural Networks: A Review S. Bera et al. 10.32604/cmes.2022.020601
2. Past, present and future of deep plant leaf disease recognition: A survey R. George et al. 10.1016/j.compag.2025.110128
3. Çeltik yanıklığı hastalığının görüntü işleme teknikleri kullanılarak tespit edilmesi O. SOYDAN & A. TANER 10.7161/omuanajas.987368
4. Change Detection in Remote Sensing Image Data Comparing Algebraic and Machine Learning Methods A. Goswami et al. 10.3390/electronics11030431
5. Deep Learning model of sequential image classifier for crop disease detection in plantain tree cultivation M. Nandhini et al. 10.1016/j.compag.2022.106915
6. RiPa-Net: Recognition of Rice Paddy Diseases with Duo-Layers of CNNs Fostered by Feature Transformation and Selection O. Attallah 10.3390/biomimetics8050417
7. Recognizing rice diseases with modern computer vision techniques I. Arinicheva et al. 10.32634/0869-8155-2021-345-2-90-94
8. The Role of Genetic Resistance in Rice Disease Management A. Danso Ofori et al. 10.3390/ijms26030956
9. Detection of rice plant diseases based on deep transfer learning J. Chen et al. 10.1002/jsfa.10365
10. Federated Learning for Heterogeneous Multi-Site Crop Disease Diagnosis W. Chorney et al. 10.3390/math13091401
11. A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment V. Gautam et al. 10.3390/su142013610
12. AI and Neural Network-Based Approach for Paddy Disease Identification and Classification S. V et al. 10.54392/irjmt2438
13. SHUFFLED SHEPHERD SOCIAL POLITICAL OPTIMIZED DEEP LEARNING FOR RICE LEAF DISEASE CLASSIFICATION AND SEVERITY PERCENTAGE PREDICTION M. Geetha et al. 10.4015/S1016237224500133
14. Early diagnosis of rice plant disease using machine learning techniques M. Sharma et al. 10.1080/03235408.2021.2015866
15. Multi-Class Pixel Certainty Active Learning Model for Classification of Land Cover Classes Using Hyperspectral Imagery C. Yadav et al. 10.3390/electronics11172799
16. Lightweight Multi-scale Convolutional Neural Network for Rice Leaf Disease燫ecognition C. Zhang et al. 10.32604/cmc.2023.027269
17. Optimized classification model for plant diseases using generative adversarial networks S. Lamba et al. 10.1007/s11334-022-00507-w
18. Comparison of Standard and Pretrained CNN Models for Potato, Cotton, Bean and Banana Disease Detection S. KIZILOLUK 10.46572/naturengs.1007532
19. Using a Resnet50 with a Kernel Attention Mechanism for Rice Disease Diagnosis M. Al-Gaashani et al. 10.3390/life13061277
20. Rice Disease Detection: TLI-YOLO Innovative Approach for Enhanced Detection and Mobile Compatibility Z. Li et al. 10.3390/s25082494
21. Derin Öğrenme Modelleri ve Veri Ön İşleme Yöntemleri ile Çeltik Yaprak Hastalıklarının Erken Teşhisi C. ÖZDEMİR 10.21605/cukurovaumfd.1377763
22. A systematic review of deep learning applications for rice disease diagnosis: current trends and future directions P. Seelwal et al. 10.3389/fcomp.2024.1452961
23. An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation S. Chen et al. 10.3390/agriculture11050420
24. Advances and Challenges in Computer Vision for Image-Based Plant Disease Detection: A Comprehensive Survey of Machine and Deep Learning Approaches S. Qadri et al. 10.1109/TASE.2024.3382731
25. X-ResFormer: A Model to Detect Infestation of Pest and Diseases on Crops D. Mondal et al. 10.1007/s42979-023-02393-w
26. Exponential Rider-Henry Gas Solubility optimization-based deep learning for rice plant disease detection T. Daniya & S. Vigneshwari 10.1007/s41870-022-01022-8
27. Image‐Based Detection of Plant Diseases: From Classical Machine Learning to Deep Learning Journey R. Khan et al. 10.1155/2021/5541859
28. Cereal fungal diseases detection using autoencoders I. Arinicheva et al. 10.1088/1755-1315/949/1/012048
29. Bayesian-Convolutional Neural Network Model Transfer Learning for Image Detection of Concrete Water-Binder Ratio H. Yang et al. 10.1109/ACCESS.2020.2975350
30. Land Quality Index for Paddy (Oryza sativa L.) Cultivation Area Based on Deep Learning Approach using Geographical Information System and Geostatistical Techniques N. ŞENYER et al. 10.29133/yyutbd.1177796
31. Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review S. Wang et al. 10.3390/rs17040698
32. The Influence of UHPFRC Jacket Steel Fiber Content on Strengthening Damaged Columns H. Alasmari 10.48084/etasr.6292
33. Rice Leaf Disease Classification Using Cnn P. Tejaswini et al. 10.1088/1755-1315/1032/1/012017
34. Rice plant disease diagnosing using machine learning techniques: a comprehensive review G. Udayananda et al. 10.1007/s42452-022-05194-7
35. Adaptive feature selection with deep learning MBi-LSTM model based paddy plant leaf disease classification R. Dubey & D. Choubey 10.1007/s11042-023-16475-7
36. Prediction of micro-hardness in thread rolling of St37 by convolutional neural networks and transfer learning M. Soleymani et al. 10.1007/s00170-022-10355-4
37. Deep Learning-Based Approach to Detect and Classify Signs of Crop Leaf Diseases and Pest Damage D. Mondal et al. 10.1007/s42979-022-01332-5
38. Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection V. Pandiyaraju et al. 10.1007/s00521-025-11012-z
39. Weakly-supervised learning method for the recognition of potato leaf diseases J. Chen et al. 10.1007/s10462-022-10374-3
40. Mobile convolution neural network for the recognition of potato leaf disease images W. Chen et al. 10.1007/s11042-022-12620-w
41. Transfer Öğrenme Temelli Bitki Yaprak Hastalıklarının Tespiti İçin Karşılaştırmalı Bir Çalışma S. Sazak et al. 10.18016/ksutarimdoga.vi.1571202
42. Agricultural plant diseases identification: From traditional approach to deep learning J. Kotwal et al. 10.1016/j.matpr.2023.02.370
43. Automatic identification of diseases in grains crops through computational approaches: A review R. Manavalan 10.1016/j.compag.2020.105802
44. Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images V. Balaji et al. 10.1155/2023/5644727
45. Rice Disease Detection Using Artificial Intelligence and Machine Learning Techniques to Improvise Agro-Business S. Aggarwal et al. 10.1155/2022/1757888
46. Classification of physiological disorders in apples fruit using a hybrid model based on convolutional neural network and machine learning methods B. Buyukarikan & E. Ulker 10.1007/s00521-022-07350-x
47. Identification of Paddy Rice Diseases Using Deep Convolutional Neural Networks H. ALTINBİLEK & Ü. KIZIL 10.29133/yyutbd.1140911
48. Multi-scale feature fusion-based lightweight dual stream transformer for detection of paddy leaf disease A. Kumar et al. 10.1007/s10661-023-11628-5
49. IoT Based Wireless Communication System for Smart Irrigation and Rice Leaf Disease Prediction Using ResNeXt-50 S. Sangeetha et al. 10.1142/S0218213024500040
50. Enhanced Sea Horse Optimization with Deep Learning-based Multimodal Fusion Technique for Rice Plant Disease Segmentation and Classification D. Anandhi & S. Sathiamoorthy 10.48084/etasr.6324
51. Shuffled shepherd social optimization based deep learning for rice leaf disease classification and severity percentage prediction T. Daniya & V. Srinivasan 10.1002/cpe.7523
52. Agry: a comprehensive framework for plant diseases classification via pretrained EfficientNet and convolutional neural networks for precision agriculture S. Saleki & J. Tahmoresnezhad 10.1007/s11042-023-17952-9
53. Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease U. Barman et al. 10.1016/j.compag.2020.105661
54. DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation A. Topal et al. 10.7717/peerj-cs.2406
55. Rice plant disease classification using color features: a machine learning paradigm V. Shrivastava & M. Pradhan 10.1007/s42161-020-00683-3
56. Applications of convolutional neural networks for the detection and classification of fungal rice diseases I. Arinichev et al. 10.1088/1755-1315/699/1/012020
57. Deteksi Penyakit pada Tanaman Padi Menggunakan Pengolahan Citra Digital dengan Metode Convolutional Neural Network A. Santosa et al. 10.31289/jesce.v6i2.7930
58. Deep learning techniques in concrete powder mix designing K. Ali Hadi & A. Ridha 10.1515/eng-2022-0588
59. Towards Highly Intelligent Image Processing Techniques for Rice Diseases Identification: A Review R. Manavalan 10.2174/2665997202666220608125036
60. Automatic Recognition of Rice Leaf Diseases Using Transfer Learning C. Simhadri & H. Kondaveeti 10.3390/agronomy13040961
61. Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture A. Upadhyay et al. 10.1007/s10462-024-11100-x
62. Rice Diseases Classification by Residual Network 50 (RESNET50) and Support Vector Machine (SVM) Modeling D. Alwan & M. Naji 10.31642/JoKMC/2018/100114
63. Extreme learning machine for plant diseases classification: a sustainable approach for smart agriculture D. Aqel et al. 10.1007/s10586-021-03397-y
64. Applied Deep learning approaches on canker effected leaves to enhance the detection of the disease using Image Embedding and Machine learning Techniques K. Badri Narayanan et al. 10.4108/eetiot.5346
65. CITRUS DISEASE CLASSIFICATION WITH TRANSFER LEARNING AND CNN BASED MODELS H. ÇETİNER 10.17780/ksujes.1170947
66. A precise model for accurate rice disease diagnosis: a transfer learning approach D. Mohapatra & N. Das 10.1007/s43538-022-00149-3
67. Plant Leaf Disease Detection, Classification, and Diagnosis Using Computer Vision and Artificial Intelligence: A Review A. Bhargava et al. 10.1109/ACCESS.2024.3373001
68. DF$$^2$$Net: deformable fourier filter network for hyperspectral image classification C. Zhong et al. 10.1007/s10489-025-06493-3
69. SerpensGate-YOLOv8: an enhanced YOLOv8 model for accurate plant disease detection Y. Miao et al. 10.3389/fpls.2024.1514832
70. Disease detection in rice leaves using transfer learning techniques G. Kathiresan et al. 10.1088/1742-6596/1911/1/012004
71. Performance Analysis of Rice Plant Diseases Identification and Classification Methodology M. Tholkapiyan et al. 10.1007/s11277-023-10333-3
72. Plant disease detection and classification techniques: A review S. Sankhe & A. Ambhaikar 10.1177/15741702241304087
73. RETRACTED: Application of Convolutional Neural Network in Modern Technology Field and Improvement by Time-space Version S. Hung 10.1088/1742-6596/2386/1/012026
74. Optimized deep learning network for plant leaf disease segmentation and multi-classification using leaf images M. Chilakalapudi & S. Jayachandran 10.1080/0954898X.2024.2337801
75. Hydra: An ensemble deep learning recognition model for plant diseases S. Pudumalar & S. Muthuramalingam 10.1016/j.jer.2023.09.033
76. Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications A. J. et al. 10.3390/agronomy12102395
77. An efficient approach for automated system to identify the rice crop disease using intensity level based multi-fractal dimension and twin support vector machine S. Chaudhary & U. Kumar 10.1080/03235408.2023.2222444
78. Evaluating Impacts between Laboratory and Field-Collected Datasets for Plant Disease Classification G. Fenu & F. Malloci 10.3390/agronomy12102359
79. Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification J. Lu et al. 10.3390/agriculture11080707
80. Foliar symptom-based disease detection in black pepper using convolutional neural network P. Sreethu et al. 10.1186/s42483-024-00305-1
81. Image based Plant leaf disease detection using Deep learning P. S & F. A 10.34256/ijcci2115