Novel Approach to Detect Verticillium Wilt Using Transfer Learning

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ID: 312721
2025
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Abstract
The detection of Verticillium wilt at an early and accurate stage is crucial for successful disease control measures. Aside from causing large cop losses worldwide year after year, this disease is very common in Taiwan. Standard laboratory detection techniques, such as pathogen isolation and serological testing, are time-consuming, labor-intensive, and require professional expertise. Deep learning methods, especially convolutional neural networks (CNNs), have only become available in recent years and are now proving to be effective tools for disease detection using images. This holds whether they're being used as satellites monitoring agriculture fields or by farmers keeping close tabs on their crops. Among these approaches is transfer learning -one branch of deep learning-uses pre-trained models to speed the development process and improve image classification. The goals of this research are to establish a new, precise, and frugal way of detecting Verticillium wilt in cotton plants by using transfer learning based on pre-trained deep networks (like ResNets or convolutional neural networks). The results of this research provide a significant contribution to the current discussion on using sophisticated convolutional neural network architectures for image classification applications. The results offer a road map for scholars and professionals who want to choose models that strike a compromise between precision, computational effectiveness, and generalization. It is possible to utilize several ready-to-hand image databases. Furthermore, the system attempts to address some of the traditional detection methods 'shortcomings with earlier and less invasive diagnosis to improve disease management and sustainable cotton production.
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imported_1777056247_69ebb9f75935d Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Atiqa Safdar
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/1062
Keywords Keywords not found

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