Dosset Arailym, Dang L Minh, Alharbi Faisal, Habib Shabana, Alam Nur, Park Han Yong, Moon Hyeonjoon
Department of Computer Science and Engineering, Sejong University, Seoul, Republic of Korea.
Institute of Research and Development, Duy Tan University, Da Nang, Vietnam.
Pest Manag Sci. 2025 Feb;81(2):607-617. doi: 10.1002/ps.8456. Epub 2024 Oct 21.
Cassava is a high-carbohydrate crop that is at risk of viral infections. The production rate and quality of cassava crops are affected by several diseases. However, the manual identification of diseases is challenging and requires considerable time because of the lack of field professionals and the limited availability of clear and distinct information. Consequently, the agricultural management system is seeking an efficient and lightweight method that can be deployable to edged devices for detecting diseases at an early stage. To address these issues and accurately categorize different diseases, a very effective and lightweight framework called CDDNet has been introduced. We used MobileNetV3Small framework as a backbone feature for extracting optimized, discriminating, and distinct features. These features are empirically validated at the early intermediate stage. Additionally, we modified the soft attention module to effectively prioritize the diseased regions and enhance significant cassava plant disease-related features for efficient cassava disease detection.
Our proposed method achieved accuracies of 98.95%, 97.03%, and 98.25% on Cassava Image Dataset, Cassava Plant Disease Merged (2019-2020) Dataset, and the newly created Cassava Plant Composite Dataset, respectively. Furthermore, the proposed technique outperforms previous state-of-the-art methods in terms of accuracy, parameter count, and frames per second values, ultimately making the proposed CDDNet the best one for real-time processing.
Our findings underscore the importance of a lightweight and efficient technique for cassava disease detection and classification in a real-time environment. Furthermore, we highlight the impact of modified soft attention on model performance. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
木薯是一种高碳水化合物作物，面临病毒感染风险。木薯作物的产量和质量受多种病害影响。然而，由于缺乏田间专业人员且清晰明确的信息有限，病害的人工识别具有挑战性且需要大量时间。因此，农业管理系统正在寻求一种高效且轻量级的方法，该方法可部署到边缘设备上以早期检测病害。为解决这些问题并准确分类不同病害，引入了一种名为CDDNet的非常有效且轻量级的框架。我们使用MobileNetV3Small框架作为骨干特征来提取优化、有区分力且独特的特征。这些特征在早期中间阶段经过了实证验证。此外，我们修改了软注意力模块，以有效优先考虑患病区域并增强与木薯植物病害相关的重要特征，从而实现高效的木薯病害检测。
我们提出的方法在木薯图像数据集、木薯植物病害合并（2019 - 2020）数据集和新创建的木薯植物复合数据集上分别达到了98.95%、97.03%和98.25%的准确率。此外，所提出的技术在准确率、参数数量和每秒帧数方面优于先前的最先进方法，最终使所提出的CDDNet成为实时处理的最佳方法。
我们的研究结果强调了在实时环境中用于木薯病害检测和分类的轻量级高效技术的重要性。此外，我们突出了修改后的软注意力对模型性能的影响。© 2024作者。由John Wiley & Sons Ltd代表化学工业协会出版的《害虫管理科学》。
