Novel deep learning approaches for crop leaf disease classification: A review

E. Ekanayake, Ruwan Dharshana Nawarathna
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引用次数: 2

Abstract

To encourage sustainable progress, it is suggested that in a world connected by virtual platforms, modern society should merge big data, artificial intelligence, machine learning, information and communication technology (ICT), as well as the “Internet of Things” (IoT). When real-life problems are considered, the above technology processes are essential in solving the issues. Food is an essential need of human beings. Food supply has become crucial, and it is very important to increase the adequate cultivation of plants for large populations due to huge population growth. At the same time, farmers are struggling with a variety of food plant diseases that significantly affect the harvesting and production in agricultural fields. Nevertheless, the agricultural productivity of rural areas is directly involved with the increase in the economic growth of developing countries such as Sri Lanka, India, Myanmar and Indonesia. Early identification of crop disease, using a well-established modern technique, is vital. It necessitates a number of processes observing large-scale agricultural fields as a disease can infect different parts of the plant such as leaf, roots, stem and fruit. Most diseases appear in plant leaves and have the potential to spread them all over the field within a very short time. This paper reviews several state-of-the-art methods that can be used for plant leaf disease recognition with a special reference to deep learning based methods.
农作物叶片病害分类的深度学习新方法综述
为了鼓励可持续发展,建议在虚拟平台连接的世界中,现代社会应该融合大数据,人工智能,机器学习,信息和通信技术(ICT)以及“物联网”(IoT)。当考虑到现实生活中的问题时,上述技术过程对于解决问题至关重要。食物是人类的基本需求。粮食供应变得至关重要,由于人口的巨大增长,为大量人口增加足够的植物种植是非常重要的。与此同时,农民正在与各种严重影响农业收获和生产的粮食植物病害作斗争。然而,农村地区的农业生产力直接关系到斯里兰卡、印度、缅甸和印度尼西亚等发展中国家经济增长的增加。利用成熟的现代技术及早发现作物病害至关重要。由于一种疾病可以感染植物的不同部分,如叶、根、茎和果实,因此需要对大规模农田进行一系列观察。大多数病害出现在植物叶片上,并有可能在很短的时间内传播到整个田地。本文综述了几种可用于植物叶片病害识别的最新方法,并特别提到了基于深度学习的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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