Dethier, J.-J. & Effenberger, A. Agriculture and development: A brief review of the literature. Economic systems 36, 175–205 (2012).
Seck, P. A., Diagne, A., Mohanty, S. & Wopereis, M. C. Crops that feed the world 7: Rice. Food security 4, 7–24 (2012).
Arnal Barbedo, J. G. Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus 2, 1–12 (2013).
Simhadri, C. G., Kondaveeti, H. K., Vatsavayi, V. K., Mitra, A. & Ananthachari, P. Deep learning for rice leaf disease detection: A systematic literature review on emerging trends, methodologies and techniques. Information Processing in Agriculture (2024).
Khairnar, K. & Dagade, R. Disease detection and diagnosis on plant using image processing-a review. International Journal of Computer Applications 108, 36–38 (2014).
Ramesh, S. et al. Plant disease detection using machine learning. In 2018 International conference on design innovations for 3Cs compute communicate control (ICDI3C), 41–45 (IEEE, 2018).
Singh, S. P., Pritamdas, K., Devi, K. J. & Devi, S. D. Custom convolutional neural network for detection and classification of rice plant diseases. Procedia Computer Science 218, 2026–2040 (2023).
Bhimavarapu, U. Prediction and classification of rice leaves using the improved pso clustering and improved cnn. Multimedia Tools and Applications 1–14 (2023).
Chen, J., Chen, W., Zeb, A., Yang, S. & Zhang, D. Lightweight inception networks for the recognition and detection of rice plant diseases. IEEE Sensors Journal 22, 14628–14638 (2022).
Google Scholar
Chen, L., Zou, J., Yuan, Y. & He, H. Improved domain adaptive rice disease image recognition based on a novel attention mechanism. Computers and Electronics in Agriculture 208, 107806 (2023).
Haridasan, A., Thomas, J. & Raj, E. D. Deep learning system for paddy plant disease detection and classification. Environmental Monitoring and Assessment 195, 120 (2023).
Upadhyay, S. K. & Kumar, A. A novel approach for rice plant diseases classification with deep convolutional neural network. International Journal of Information Technology 1–15 (2022).
Patel, B. & Sharaff, A. Automatic rice plant’s disease diagnosis using gated recurrent network. Multimedia Tools and Applications 1–20 (2023).
Simhadri, C. G. & Kondaveeti, H. K. Automatic recognition of rice leaf diseases using transfer learning. Agronomy 13, 961 (2023).
Yang, L. et al. Googlenet based on residual network and attention mechanism identification of rice leaf diseases. Computers and Electronics in Agriculture 204, 107543 (2023).
Al-Gaashani, M. S., Samee, N. A., Alnashwan, R., Khayyat, M. & Muthanna, M. S. A. Using a resnet50 with a kernel attention mechanism for rice disease diagnosis. Life 13, 1277 (2023).
Google Scholar
Stephen, A., Punitha, A. & Chandrasekar, A. Designing self attention-based resnet architecture for rice leaf disease classification. Neural Computing and Applications 35, 6737–6751 (2023).
Zhang, C., Ni, R., Mu, Y., Sun, Y. & Tyasi, T. L. Lightweight multi-scale convolutional neural network for rice leaf disease recognition. Computers, Materials & Continua 74 (2023).
Narmadha, R., Sengottaiyan, N. & Kavitha, R. Deep transfer learning based rice plant disease detection model. Intelligent Automation & Soft Computing 31 (2022).
Sudhesh, K., Sowmya, V., Kurian, S. & Sikha, O. Ai based rice leaf disease identification enhanced by dynamic mode decomposition. Engineering Applications of Artificial Intelligence 120, 105836 (2023).
Aggarwal, M. et al. Pre-trained deep neural network-based features selection supported machine learning for rice leaf disease classification. Agriculture 13, 936 (2023).
Haruna, Y., Qin, S. & Mbyamm Kiki, M. J. An improved approach to detection of rice leaf disease with gan-based data augmentation pipeline. Applied Sciences 13, 1346 (2023).
Google Scholar
Aggarwal, M. et al. Lightweight federated learning for rice leaf disease classification using non independent and identically distributed images. Sustainability 15, 12149 (2023).
Jain, S. et al. Automatic rice disease detection and assistance framework using deep learning and a chatbot. Electronics 11, 2110 (2022).
Kondaveeti, H. K., Ujini, K. G., Pavankumar, B. V. V., Tarun, B. S. & Gopi, S. C. Plant disease detection using ensemble learning. In 2023 2nd International Conference on Computational Systems and Communication (ICCSC), 1–6 (IEEE, 2023).
Yang, L. et al. Stacking-based and improved convolutional neural network: a new approach in rice leaf disease identification. Frontiers in Plant Science 14, 1165940 (2023).
Google Scholar
Ahad, M. T., Li, Y., Song, B. & Bhuiyan, T. Comparison of cnn-based deep learning architectures for rice diseases classification. Artificial Intelligence in Agriculture 9, 22–35 (2023).
Zhang, Y., Zhong, L., Ding, Y., Yu, H. & Zhai, Z. Resvit-rice: A deep learning model combining residual module and transformer encoder for accurate detection of rice diseases. Agriculture 13, 1264 (2023).
Rajpoot, V., Tiwari, A. & Jalal, A. S. Automatic early detection of rice leaf diseases using hybrid deep learning and machine learning methods. Multimedia Tools and Applications 1–27 (2023).
Jiang, F., Lu, Y., Chen, Y., Cai, D. & Li, G. Image recognition of four rice leaf diseases based on deep learning and support vector machine. Computers and Electronics in Agriculture 179, 105824 (2020).
Islam, S. R., Eberle, W., Ghafoor, S. K. & Ahmed, M. Explainable artificial intelligence approaches: A survey. arXiv preprint arXiv:2101.09429 (2021).
Minh, D., Wang, H. X., Li, Y. F. & Nguyen, T. N. Explainable artificial intelligence: a comprehensive review. Artificial Intelligence Review 1–66 (2022).
Gerlings, J., Shollo, A. & Constantiou, I. Reviewing the need for explainable artificial intelligence (xai). arXiv preprint arXiv:2012.01007 (2020).
Razak, S. F. A., Yogarayan, S., Sayeed, M. S. & Derafi, M. Agriculture 5.0 and explainable ai for smart agriculture: A scoping review. Emerging Science Journal 8, 744–760 (2024).
Naga Srinivasu, P., Ijaz, M. F. & Woźniak, M. Xai-driven model for crop recommender system for use in precision agriculture. Computational Intelligence 40, e12629 (2024).
Mohan, R. J., Rayanoothala, P. S. & Sree, R. P. Next-gen agriculture: integrating ai and xai for precision crop yield predictions. Frontiers in Plant Science 15, 1451607 (2025).
Google Scholar
Shams, M. Y., Gamel, S. A. & Talaat, F. M. Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making. Neural Computing and Applications 36, 5695–5714 (2024).
Zhang, Y., Weng, Y. & Lund, J. Applications of explainable artificial intelligence in diagnosis and surgery. Diagnostics 12 (2022).
Guo, W. et al. Lemna: Explaining deep learning based security applications. In proceedings of the 2018 ACM SIGSAC conference on computer and communications security, 364–379 (2018).
Bach, S. et al. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one 10, e0130140 (2015).
Google Scholar
Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. Advances in neural information processing systems 30 (2017).
Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, 618–626 (2017).
Simonyan, K., Vedaldi, A. & Zisserman, A. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013).
Ribeiro, M. T., Singh, S. & Guestrin, C.“why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 1135–1144 (2016).
Bhandari, M., Shahi, T. B., Neupane, A. & Walsh, K. B. Botanicx-ai: Identification of tomato leaf diseases using an explanation-driven deep-learning model. Journal of Imaging 9, 53 (2023).
Google Scholar
Nahiduzzaman, M. et al. Explainable deep learning model for automatic mulberry leaf disease classification. Frontiers in Plant Science 14 (2023).
Wei, K. et al. Explainable deep learning study for leaf disease classification. Agronomy 12, 1035 (2022).
Hughes, D., Salathé, M. et al. An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060 (2015).
Zhou, C., Zhong, Y., Zhou, S., Song, J. & Xiang, W. Rice leaf disease identification by residual-distilled transformer. Engineering Applications of Artificial Intelligence 121, 106020 (2023).
Kisten, M., Ezugwu, A.E.-S. & Olusanya, M. O. Explainable artificial intelligence model for predictive maintenance in smart agricultural facilities. IEEE Access 12, 24348–24367. https://doi.org/10.1109/ACCESS.2024.3365586 (2024).
Bijoy, M. H. et al. Towards sustainable agriculture: A novel approach for rice leaf disease detection using dcnn and enhanced dataset. IEEE Access (2024).
Shovon, M. S. H. et al. Plantdet: A robust multi-model ensemble method based on deep learning for plant disease detection. IEEE Access (2023).
Altabaji, W. I., Umair, M., Tan, W.-H., Foo, Y.-L. & Ooi, C.-P. Comparative analysis of transfer learning, leafnet, and modified leafnet models for accurate rice leaf diseases classification. IEEE Access (2024).
Deng, R. et al. Automatic diagnosis of rice diseases using deep learning. Frontiers in Plant Science 12, 701038 (2021).
Google Scholar
Rahimiaghdam, S. & Alemdar, H. Evaluating the quality of visual explanations on chest x-ray images for thorax diseases classification. Neural Computing and Applications 1–17 (2024).
Quach, L.-D., Quoc, K. N., Quynh, A. N., Thai-Nghe, N. & Nguyen, T. G. Explainable deep learning models with gradient-weighted class activation mapping for smart agriculture. IEEE Access 11, 83752–83762 (2023).
Alqahtani, Y. et al. An improved deep learning approach for localization and recognition of plant leaf diseases. Expert Systems with Applications 230, 120717 (2023).
Mankodiya, H. et al. Od-xai: Explainable ai-based semantic object detection for autonomous vehicles. Applied Sciences 12, 5310 (2022).
Google Scholar
Kimori, Y. A morphological image preprocessing method based on the geometrical shape of lesions to improve the lesion recognition performance of convolutional neural networks. IEEE Access 10, 70919–70936 (2022).
Lu, Y., Zhang, X., Zeng, N., Liu, W. & Shang, R. Image classification and identification for rice leaf diseases based on improved woacw_simplenet. Frontiers in Plant Science 13, 1008819 (2022).
Google Scholar
Hasan, M. M. et al. Enhancing rice crop management: Disease classification using convolutional neural networks and mobile application integration. Agriculture 13, 1549 (2023).
Yang, H. et al. Disease detection and identification of rice leaf based on improved detection transformer. Agriculture 13, 1361 (2023).
Google Scholar
Saleem, M. A., Aamir, M., Ibrahim, R., Senan, N. & Alyas, T. An optimized convolution neural network architecture for paddy disease classification. Computers, Materials & Continua 71 (2022).
Rezk, N. G., Hemdan, E. E.-D., Attia, A.-F., El-Sayed, A. & El-Rashidy, M. A. An efficient iot based framework for detecting rice disease in smart farming system. Multimedia Tools and Applications 1–34 (2023).
Tripathy, R., Mandala, J., Pappu, S. R. & Gopisetty, G. K. D. Optimization based rice leaf disease classification in federated learning. Multimedia Tools and Applications 1–27 (2024).
Li, K. et al. Diagnosis and application of rice diseases based on deep learning. PeerJ Computer Science 9, e1384 (2023).
Google Scholar
Gogoi, M., Kumar, V., Begum, S. A., Sharma, N. & Kant, S. Classification and detection of rice diseases using a 3-stage cnn architecture with transfer learning approach. Agriculture 13, 1505 (2023).
Dogra, R. et al. Deep learning model for detection of brown spot rice leaf disease with smart agriculture. Computers and Electrical Engineering 109, 108659 (2023).
Zhao, D. et al. Study on the classification method of rice leaf blast levels based on fusion features and adaptive-weight immune particle swarm optimization extreme learning machine algorithm. Frontiers in Plant Science 13, 879668 (2022).
Google Scholar
Clinciu, M. A. & Hastie, H. F. A survey of explainable ai terminology. In 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence 2019, 8–13 (Association for Computational Linguistics, 2019).
Sterz, S., Baum, K., Lauber-Rönsberg, A. & Hermanns, H. Towards perspicuity requirements. In 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW), 159–163 (IEEE, 2021).
Speith, T. A review of taxonomies of explainable artificial intelligence (xai) methods. In Proceedings of the 2022 ACM conference on fairness, accountability, and transparency, 2239–2250 (2022).
PlantVillage. PlantVillage — plantvillage.psu.edu. https://plantvillage.psu.edu/. [Accessed 16-03-2024].
Kaggle. Tomato leaf disease detection — kaggle.com. https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf. [Accessed 16-03-2024].
Paul, H. et al. A study and comparison of deep learning based potato leaf disease detection and classification techniques using explainable ai. Multimedia Tools and Applications 1–34 (2023).
Ghosh, P. et al. Recognition of sunflower diseases using hybrid deep learning and its explainability with ai. Mathematics 11, 2241 (2023).
Sara, U. et al. An extensive sunflower dataset representation for successful identification and classification of sunflower diseases. Data in brief 42, 108043 (2022).
Google Scholar
Batchuluun, G., Nam, S. H. & Park, K. R. Deep learning-based plant classification and crop disease classification by thermal camera. Journal of King Saud University-Computer and Information Sciences 34, 10474–10486 (2022).
Hughes, D., Salathé, M. et al. An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060 (2015).
Kaggle. Tomatoes Dataset — kaggle.com. https://www.kaggle.com/datasets/enalis/tomatoes-dataset. [Accessed 16-03-2024].
Bilal, A., Liu, X., Long, H., Shafiq, M. & Waqar, M. Increasing crop quality and yield with a machine learning-based crop monitoring system. Comput Mater Continua 76, 2401–2426 (2023).
Bilal, A. et al. Fuzzy deep learning architecture for cucumber plant disease detection and classification. Journal of Big Data 12, 1–21 (2025).
Tariq, M. et al. Corn leaf disease: insightful diagnosis using vgg16 empowered by explainable ai. Frontiers in Plant Science 15, 1402835 (2024).
Google Scholar
Natarajan, S., Chakrabarti, P. & Margala, M. Robust diagnosis and meta visualizations of plant diseases through deep neural architecture with explainable ai. Scientific Reports 14, 13695 (2024).
Google Scholar
Prashanthi, B., Krishna, A. & Rao, C. M. Levit-leaf disease identification and classification using an enhanced vision transformers (vit) model. Multimedia Tools and Applications 1–32 (2024).
Al-Gaashani, M. S., Muthanna, A., Chelloug, S. A. & Kumar, N. Eamultires-dspp: an efficient attention-based multi-residual network with dilated spatial pyramid pooling for identifying plant disease. Neural Computing and Applications 36, 16141–16161 (2024).
Al-Gaashani, M. S. et al. Mscpnet: A multi-scale convolutional pooling network for maize disease classification. IEEE Access (2025).
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778 (2016).
Szegedy, C., Ioffe, S., Vanhoucke, V. & Alemi, A. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI conference on artificial intelligence, 0–0 (2017).
Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 4700–4708 (2017).
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2818–2826 (2016).
Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, 6105–6114 (PMLR, 2019).
Chollet, F. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1251–1258 (2017).
Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).
Iandola, F. N. et al. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv preprint arXiv:1602.07360 (2016).
Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012).
Kaggle_Rice_Leaf_Disease. Rice Leafs Disease Dataset — kaggle.com. https://www.kaggle.com/datasets/dedeikhsandwisaputra/rice-leafs-disease-dataset. [Accessed 14-01-2024].
Kumar, S. V. & Kondaveeti, H. K. Bird species recognition using transfer learning with a hybrid hyperparameter optimization scheme (hhos). Ecological Informatics 102510 (2024).
Subramanian, M., Shanmugavadivel, K. & Nandhini, P. On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves. Neural Computing and Applications 34, 13951–13968 (2022).
LIME. Explain network predictions using LIME – MATLAB imageLIME – MathWorks India — in.mathworks.com. https://in.mathworks.com/help/deeplearning/ref/imagelime.html. [Accessed 13-12-2023].
Huff, D. T., Weisman, A. J. & Jeraj, R. Interpretation and visualization techniques for deep learning models in medical imaging. Physics in Medicine & Biology 66, 04TR01 (2021).
Bach, S. et al. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one 10, e0130140 (2015).
Google Scholar
Bag, S., Kumar, S. K. & Tiwari, M. K. An efficient recommendation generation using relevant jaccard similarity. Information Sciences 483, 53–64 (2019).
Yuan, Y., Chao, M. & Lo, Y.-C. Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance. IEEE transactions on medical imaging 36, 1876–1886 (2017).
Google Scholar
Matlab_Segmentation. Segment Image Using Graph Cut in Image Segmenter – MATLAB & Simulink – MathWorks India — in.mathworks.com. https://in.mathworks.com/help/images/segment-image-using-graph-cut.html. [Accessed 01-03-2024].
Spanhol, F. A., Oliveira, L. S., Petitjean, C. & Heutte, L. A dataset for breast cancer histopathological image classification. Ieee transactions on biomedical engineering 63, 1455–1462 (2015).
Google Scholar
Shorten, C. & Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. Journal of big data 6, 1–48 (2019).
Hung, Y.-H. & Lee, C.-Y. Bmb-lime: Lime with modeling local nonlinearity and uncertainty in explainability. Knowledge-Based Systems 294, 111732 (2024).
Xu, H., Ma, J. & Zhang, X.-P. Mef-gan: Multi-exposure image fusion via generative adversarial networks. IEEE Transactions on Image Processing 29, 7203–7216 (2020).
Google Scholar
