利用DenseSwin变压器模型对玉米生长早期和晚期植物病理学进行分类:赞比亚农民对其利用的案例研究

IF 5.3 3区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chiuzu Chilumbu;Qi-Xian Huang;Hung-Min Sun
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引用次数: 0

摘要

玉米是包括赞比亚在内的许多撒哈拉以南国家的主要作物,它容易受到对粮食生产产生重大影响的各种疾病的影响。为了应对这一挑战并提高病害检测效率,人们采用深度学习方法对植物病害进行准确分类和识别。最近,在赞比亚许多地区,人工检查玉米田以检测病害已成为标准做法。然而,这种方法不仅耗时,而且不适合大规模农业经营。因此,开发精确、自动化的分类模型在现代农业中变得至关重要。在这项研究中,我们提出了一种新的深度学习模型,称为DenseSwin,专门用于玉米疾病早期可见阶段和晚期无可争辩阶段的分类。DenseSwin将密集连接的卷积块的优势与基于移位窗口的多头自注意机制相结合。这种独特的技术融合使该模型能够有效地捕获玉米植物图像中的复杂模式和特征,从而提高疾病分类性能。通过广泛的实验和评估,DenseSwin达到了令人印象深刻的97.18%的准确率。这些结果突出了该模型在准确检测和分类玉米病害方面的卓越能力,为在农业环境中,特别是在赞比亚的实际应用提供了有希望的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilizing a DenseSwin Transformer Model for the Classification of Maize Plant Pathology in Early and Late Growth Stages: A Case Study of Its Utilization Among Zambian Farmers
Maize, which is the primarycrop in many sub-Saharan countries, including Zambia, is susceptible to a wide range of diseases that have a significant impact on food production. To tackle this challenge and improve disease detection efficiency, deep learning methods have been employed to accurately classify and identify plant diseases. In recent times, manual inspection of maize fields for disease detection has been the standard practice in many parts of Zambia. However, this approach is not only time-consuming but also impractical for large-scale agricultural operations. Hence, the development of precise and automated classification models has become crucial in modern agriculture. In this study, we propose a novel deep-learning model called DenseSwin, specifically designed for maize disease classification in both the early visible stage and late indisputable stage of the disease. DenseSwin combines the strengths of densely connected convolution blocks with a shifted windows-based multi-head self-attention mechanism. This unique fusion of techniques enables the model to effectively capture intricate patterns and features in maize plant images, thereby enhancing disease classification performance. Through extensive experimentation and evaluation, DenseSwin achieves an impressive accuracy of 97.18%. These results highlight the model's remarkable ability to accurately detect and classify maize diseases, offering promising potential for real-world applications in agricultural settings, particularly in Zambia.
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来源期刊
CiteScore
10.30
自引率
7.50%
发文量
147
期刊介绍: The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys. TETCI is an electronics only publication. TETCI publishes six issues per year. Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.
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