植物病害分类的异构轻量级网络

Theodora Sanida, Dimitris Tsiktsiris, Argyrios Sideris, M. Dasygenis
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引用次数: 8

摘要

世界上许多国家的经济依赖于农业部门。在农业中,植物栽培经常受到病害的影响。这些植物病害会对叶片和果实造成部分或完全的损害。这给农业造成了重大的经济损失。同时,如果不及早发现植物病害,就会对农作物的数量、质量和产量产生不利影响。因此,农业面临的最大挑战是准确、早期诊断和预防植物病害。在这项研究中,我们修改了一个流行的分类网络的结构,MobileNet V2,通过应用迁移学习技术和微调来提高它的整体准确性。实验结果表明,与近期文献中用于植物叶片病害检测的其他类似CNN轻量级架构相比,Modified MobileNet V2架构实现了高水平的整体精度。
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A Heterogeneous Lightweight Network for Plant Disease Classification
The economy of many countries worldwide depends on the agricultural sector. In agriculture, plant cultivation is often affected by diseases. The plant diseases can cause partial or complete damage to the leaves and fruit. This leads to significant economic loss in the agricultural industry. At the same time, plant diseases, if not detected in an early stage, have negative consequences on the quantity, quality and production of agricultural crops. Thus, the biggest challenge in agriculture is the accurate and early diagnosis and the prevention of plant diseases. In this study, we modified the structure of a popular classification network, the MobileNet V2 to improve the overall accuracy of it, by applying the transfer learning technique and fine-tuning. Experimental findings have shown that the Modified MobileNet V2 architecture achieves a high level of overall accuracy compared to other similar CNN lightweight architectures used in plant leaf disease detection in the recent literature.
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