A Unified Survey of Supervised, Unsupervised, and Semi-Supervised Learning Techniques for Plant Leaf Disease Detection – Volume 12 Issue 5

The detection and classification of plant leaf diseases is critical for ensuring sustainable agricultural productivity. This survey presents a unified and comprehensive overview of supervised, unsupervised, and semi-supervised learning techniques applied to plant leaf disease detection and classification. Supervised approaches such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and ensemble models have demonstrated high accuracy but require extensive labeled datasets. Unsupervised methods, including K-means clustering, autoencoders, and anomaly detection algorithms, offer promising results with unlabeled data, especially in early-stage disease detection. Meanwhile, semi-supervised learning bridges the gap by leveraging limited labeled and abundant unlabeled data through frameworks such as self-training, GANs, and hybrid models. This paper compares model accuracies, datasets used, tools and platforms (Python, TensorFlow, MATLAB), and evaluation metrics across recent literature. Emphasis is placed on the role of machine learning in real-time disease monitoring, data augmentation, and resource-efficient farming. The study provides practical insights and future directions for researchers and agritech developers aiming to integrate AI-driven solutions into precision agriculture.

The comparative effectiveness of supervised, unsupervised, and semi-supervised learning approaches in image-based classification tasks is highlighted in this survey. When a lot of labeled data is available, supervised learning methods like Ensemble Learning with CNN and Hybrid CNN-SVM models consistently produced the highest accuracy (up to 96%) among the three. By successfully fusing a smaller amount of labeled data with a larger amount of unlabeled data, semi-supervised techniques demonstrated encouraging results (up to 92%) and provided a balanced solution in situations where labeled data is limited. Unsupervised methods are useful for exploratory data analysis and situations without labels, despite having a slightly lower accuracy (peaking at 91%). are valuable for exploratory data analysis and scenarios lacking labels. Researchers, developers, and data scientists can use this survey to help them select the best machine learning techniques based on factors like computational efficiency, accuracy requirements, and data availability. It also acts as a roadmap for future advancements, promoting the creation of more reliable models with less supervision, especially in semi-supervised and unsupervised learning.

[1] J. Zhang, H. Wang, and Y. Li, “Leaf Disease Detection Using Convolutional Neural Networks and SVM,” Int. J. Comput. Agric. Sci., vol. 21, no. 3, pp. 201–210, 2023. [2] R. Kumar and A. Singh, “Comparative Analysis of RF, DT, and KNN for Plant Leaf Classification,” R J. Stat. Learn., vol. 19, no. 4, pp. 145–158, 2022. [3] P. Patel and S. Desai, “Deep Learning-Based Leaf Disease Detection Using CNN and Transfer Learning,” J. Adv. Deep Learn., vol. 17, no. 2, pp. 89–101, 2022. [4] R. Singh and N. Gupta, “Image Segmentation Based Plant Disease Classification Using CNN and SVM,” MATLAB Appl. Agric., vol. 18, no. 2, pp. 110–124, 2020. [5] Y. Chen and L. Zhao, “Hybrid CNN-SVM Model for Plant Disease Detection,” Comput. Vis. Plant Health, vol. 20, no. 1, pp. 34–48, 2022. [6] R. Kumar and A. Singh, “Performance Evaluation of Supervised Models in R for Leaf Disease Diagnosis,” R J. Stat. Learn., vol. 20, no. 1, pp. 78–90, 2023. [7] P. Patel and S. Desai, “Leaf Disease Classification Using PyTorch-Based Deep Learning Models,” J. Adv. Deep Learn., vol. 18, no. 3, pp. 105–116, 2023. [8] R. Singh and N. Gupta, “Enhanced Plant Leaf Classification Using Segmented Images and CNN,” MATLAB Appl. Agric., vol. 19, no. 1, pp. 60–74, 2022. [9] Y. Chen and L. Zhao, “Improved Accuracy in Leaf Disease Detection with CNN-SVM Hybrid,” Comput. Vis. Plant Health, vol. 21, no. 2, pp. 95–108, 2023. [10] M. Almeida and R. Costa, “A Systematic Review on Machine Learning Techniques for Plant Disease Detection,” J. Agric. AI, vol. 16, no. 4, pp. 200–213, 2022. [11] V. Ravi and P. Kumar, “Real-Time Plant Disease Detection Using YOLO and CNN Frameworks,” AI Agric. Appl., vol. 15, no. 1, pp. 50–63, 2023. [12] J. Fernandes and T. Silva, “Leaf Disease Recognition Using KNN, CNN and Image Processing Techniques,” Int. Conf. Adv. Agric. Inf. Tech., pp. 88–99, 2021. [13] M. Nair and R. Reddy, “Combining CNN and Gradient Boosting for High-Accuracy Leaf Disease Detection,” J. Plant ML Res., vol. 17, no. 2, pp. 77–90, 2022. [14] J. Lee and S. Park, “Ensemble Deep CNNs for Superior Plant Disease Detection Accuracy,” IEEE Trans. Agric. Comput., vol. 28, no. 4, pp. 310–322, 2023. [15] R. Kumar and A. Singh, “Unsupervised Classification of Plant Leaf Images Using K-Means Clustering,” J. Agric. Informatics, vol. 25, no. 3, pp. 101–110, 2021. [16] P. Patel and S. Desai, “PCA-Based Feature Reduction for Tomato Leaf Disease Detection,” Int. J. Comput. Appl., vol. 179, no. 4, pp. 45–52, 2022. [17] R. Singh and N. Gupta, “Anomaly Detection in Leaf Images Using Autoencoders,” J. Plant Dis. Prot., vol. 128, no. 2, pp. 88–97, 2021. [18] Y. Chen and L. Zhao, “Visualization of Leaf Clusters Using t-SNE,” Proc. Conf. AI Agric., 2022. [19] M. Almeida and R. Costa, “Hierarchical Clustering for Soybean Leaf Disease Classification,” Agric. Sci., vol. 14, no. 1, pp. 33–40, 2022. [20] V. Ravi and P. Kumar, “Synthetic Image Generation Using GANs for Leaf Disease Detection,” J. Comput. Sci. Technol., vol. 22, no. 3, pp. 145–158, 2023. [21] M. Nair and R. Reddy, “Improving Leaf Disease Classification Through Unsupervised Feature Extraction,” J. Agric. Eng., vol. 31, no. 4, pp. 78–85, 2022. [22] J. Lee and S. Park, “Leaf Image Clustering Using Self-Organizing Maps,” Comput. Electron. Agric., vol. 170, pp. 105276, 2020. [23] J. Zhang and H. Wang, “Deep Learning-Based Unsupervised Clustering for Plant Disease Detection,” Plant Pathol. J., vol. 36, no. 2, pp. 149–160, 2021. [24] J. Fernando and T. Silva, “Clustering of Leaf Diseases Using Gaussian Mixture Models,” J. Hortic. Sci., vol. 29, no. 1, pp. 55–63, 2022. [25] R. Gupta and S. Sharma, “Unsupervised Texture Analysis for Plant Leaf Disease Detection,” Int. J. Agric. Technol., vol. 18, no. 3, pp. 100–110, 2023. [26] A. Joshi and P. Rao, “Clustering-Based Detection of Citrus Leaf Diseases,” J. Plant Pathol., vol. 102, no. 2, pp. 220–230, 2022. [27] S. Verma and S. Choudhury, “Unsupervised Feature Learning for Accurate Leaf Disease Classification,” Comput. Electron. Agric., vol. 172, pp. 105312, 2021. [28] V. Iyer and R. Kumar, “Detection of Fungal Diseases in Wheat Leaves Using Unsupervised Learning,” J. Agric. Eng. Res., vol. 54, no. 3, pp. 145–154, 2020. [29] T.Nagarathinam, Dr. K. Rameshkumar, “A Survey on Cluster Analysis Techniques for Plant Disease Diagnosis,” SSRG International Journal of Computer Science and Engineering, vol. 3, no. 6, pp. 11-17 , 2016. Crossref, https://doi.org/10.14445/23488387/IJCSE-V3I6P103 [30] Y. Zhang, L. Wang, and H. Li, “Convolutional Neural Networks with Sparse Labels for Leaf Disease Detection,” Plant Pathol. J., vol. 35, no. 4, pp. 321–330, 2021. [31] R. Kumar and A. Singh, “Self-Training Based Semi-Supervised Learning for Crop Disease Classification,” J. Agric. Inform., vol. 12, no. 2, pp. 45–53, 2022.

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