玉米叶片病害检测的深度学习模型综述

Jagrati Paliwal, S. Joshi
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引用次数: 0

摘要

农业是印度经济的重要组成部分,印度是全球前三大农产品生产国之一。保护农作物和生产健康的产量是农业工业的首要目标。农作物易患病害,需要积极的早期诊断和治疗。目前正在进行研究,寻找准确诊断作物病害的智能方法和技术,以防止重大产量损失和经济损失。本研究概述了深度学习在作物病害检测中的作用,并讨论了玉米病害检测的未来进展。重点介绍了深度学习在玉米叶片病害识别中的作用,介绍了几种常见的玉米病害及其分类方法。本文将帮助读者深入了解深度学习技术来解决分类问题,并鼓励他们继续在相关领域的未来工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Overview of Deep Learning Models for Foliar Disease Detection in Maize Crop
Agriculture is an important sector of Indian economy and India is among the top three global producers of agricultural products. Protecting the crops and producing healthy yields is a prime goal of the agriculture industries. The agricultural crops are susceptible to diseases and demands proactive early diagnosis and treatment. Studies and Research are in progress to find smart methods and techniques for accurate diagnosis of crop diseases to prevent major yield losses and financial losses. The present study outlines the role of Deep Learning in the crop disease detection and discusses the future advancements in maize disease detection. The paper focuses on the role of Deep Learning in identification of diseases on maize plant leaf and describes about some common maize diseases and its classification methods. The paper shall help readers to gain insight on Deep Learning techniques to solve classification problems and encourage them to proceed for future work in the concerned domain.
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