基于CNN的番茄叶病预测识别

N. Saxena, Dr. Neha Sharma
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引用次数: 0

摘要

在印度,番茄基本上是蔬菜作物。然而,热带环境是番茄植株生长的理想环境,特定的气候条件等因素影响番茄植株的生长。除了这些环境因素和自然灾害外,植物病害是造成经济损失的严重农业生产问题。因此,早期疾病检测可能产生比现有检测方法更好的结果。因此,基于计算机视觉的深度学习方法可能用于早期发现疾病。疾病分类和检测策略,用于鉴定番茄叶片疾病进行了彻底的研究。本研究还讨论了所提出方法的优点和缺点。毕竟,本研究使用混合深度学习架构,为番茄叶病提供了一种早期疾病检测方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IDENTIFICATION OF TOMATO LEAF DISEASE PREDICTION USING CNN
In India tomatoes are broadly vegetable crop. However, the tropical environment is ideal for tomato plant growth, specific climatic conditions and other factors influence tomato plant growth. Aside from these environmental factors and natural disasters, plant disease is a serious agricultural production issue that causes economic loss. As an outcome, early illness detection may produce better results than existing detection methods. As a result, deep learning approaches based on computer vision might be used to detect diseases early. The disease categorization and detection strategies used to identify tomato leaf diseases are thoroughly examined in this study. This study also discusses the benefits and drawbacks of the approaches presented. After all, using a hybrid deep-learning architecture, this study provides an early disease detection method for tomato leaf disease.
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