基于深度学习的植物病害检测研究综述

Anshul Tripathi, Uday Chourasia, P. Dixit, Victor I. Chang
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引用次数: 2

摘要

自原始时代以来,农业一直是印度的主要职业。如今,该国在威胁全球变暖的主要职业中排名第二。除此之外,植物病害对这一主要生计来源构成挑战。本研究有助于识别植物间的不同病害,并找到解决或治疗病害的方法和防御机制。植物间病害的发现被认为是最完美、最准确的范例。四种标签分别是“细菌性斑疹”、“黄卷叶病毒”、“晚疫病”和“健康叶片”。无人机的一个范例模型也被设计为目的。该模型将用于大面积农田的现场报告。在这个范例无人机模型中,附加了一个高分辨率摄像机。捕获的植物图像将作为软件输入。在此基础上,软件将立即告诉哪些植物是健康的,哪些是患病的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey: Plant Disease Detection Using Deep Learning
Agriculture occupation has been the prime occupation in India since the primeval era. Nowadays, the country is ranked second in the prime occupations threatening global warming. Apart from this, diseases in plants are challenging to this prime source of livelihood. The present research can help in recognition of different diseases among plants and help to find out the solution or remedy that can be a defense mechanism in counter to the diseases. Finding diseases among plant DL is considered to the most perfect and exact paradigms. Four labels are classified as “bacterial spot,” “yellow leaf curl virus,” “late blight,” and “healthy leaf.” An exemplar model of the drone is also designed for the purpose. The said model will be utilized for a live report for extended large crop fields. In this exemplar drone model, a high-resolution camera is attached. The captured images of plants will act as software input. On this basis, the software will immediately tell which plants are healthy and which are diseased.
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