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整合EfficientNetV2与指导式状扩散,以提高叶疾病的识别能力.

V Vinoth Kumar1, P Rajesh2, N Krishnamoorthy2

  • 1School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore, 632014, India. vinothkumar.v@vit.ac.in.

Scientific reports
|March 14, 2026
PubMed
概括

这项研究引入了一种先进的AI方法来检测大米叶病,达到98.92%的准确性. 这种新的方法提高了图像质量和疾病识别,以实现可持续的米生产.

关键词:
细菌叶病是一种细菌叶病.棕色斑点是一个棕色斑点.有效的NetV2 有效的NetV2导向的状扩散导向的扩散叶子上的污秽.叶病是大米叶病的一种疾病.

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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