Scholarlink

<p>Traditional supervised learning approaches require large, labeled datasets, which can be expensive and time-consuming to obtain. Semi-supervised learning (SSL) approaches, such as soft labelling and pseudo-labelling, have drawn a lot of interest to overcome this difficulty. This study investigates how to combine these methods to improve leaf disease prediction models’ accuracy while reducing their dependency on labelled data. While pseudo-labelling iteratively generates labels for unlabelled samples using reliable model predictions, soft labelling employs uncertainty information to apply probabilistic labels to unlabelled data. Combining these techniques in a semi-supervised framework enhances the model’s robustness and generalization in practical situations. Our approach’s promise for real-world agricultural applications is demonstrated by experimental results that indicate it performs better than fully supervised techniques with limited labelled data. This proposal helps to dispense confidence to the unlabeled data using class probabilities and high confidence predictions are used to redevelop the model gradually. Experimental results demonstrate that the proposed model achieves superior performance compared to fully supervised methods, achieving an accuracy of 96.4% with a significant reduction in labeling effort.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Pseudo CNN: An Updated CNN Based Semi Supervised Learning Model for Leaf Disease Detection

  • 期刊论文

  • 发表日期：

    2025年8月27日

查看全文
  • Monali Sanyal,
  • Suparna Dasgupta,
  • Rituparna Chaki,
  • Soumyabrata Saha

摘要

Traditional supervised learning approaches require large, labeled datasets, which can be expensive and time-consuming to obtain. Semi-supervised learning (SSL) approaches, such as soft labelling and pseudo-labelling, have drawn a lot of interest to overcome this difficulty. This study investigates how to combine these methods to improve leaf disease prediction models’ accuracy while reducing their dependency on labelled data. While pseudo-labelling iteratively generates labels for unlabelled samples using reliable model predictions, soft labelling employs uncertainty information to apply probabilistic labels to unlabelled data. Combining these techniques in a semi-supervised framework enhances the model’s robustness and generalization in practical situations. Our approach’s promise for real-world agricultural applications is demonstrated by experimental results that indicate it performs better than fully supervised techniques with limited labelled data. This proposal helps to dispense confidence to the unlabeled data using class probabilities and high confidence predictions are used to redevelop the model gradually. Experimental results demonstrate that the proposed model achieves superior performance compared to fully supervised methods, achieving an accuracy of 96.4% with a significant reduction in labeling effort.