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首页> 外文会议>World Conference on Smart Trends in Systems, Security and Sustainability >Summary of Leaf-based plant disease detection systems: A compilation of systematic study findings to classify the leaf disease classification schemes
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Summary of Leaf-based plant disease detection systems: A compilation of systematic study findings to classify the leaf disease classification schemes

机译：基于叶的植物病害检测系统摘要：系统研究结果汇编，对叶病分类方案进行分类

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摘要

Food production is the mainstay of every economy, and relies on the yield of the agricultural producers. This yield is often affected by micro and macro diseases that occur throughout a given fruit-bearing plant's growth. For example, some of the diseases affecting banana leaf include Fusarium oxysporum, Mycosphaerella musicola, Gloeosporium musae, Erwinia Carotovora, Pseudomonas Solanaceanim, Pentalonia nigronervosa, Erionota thrax, BSV and BBM Virus. Such pathogens are countless, so advances in image processing are deemed necessary to categorize and suggest remedial measures for those viruses. A sizeable proportion of work conducted in the area aims at linear approaches where classification, attribute extraction, and definition are conducted to detect aesthetically detectable ailments. However, these interventions are not equipped for bigger and more diverse sets of data, so machine learning and artificial intelligence-based approaches such as Q-learning, re-enforcement learning, etc. take responsibility for formulating diseases which were invisible to the human eye in every component of the processing layers. Because of such a wide variety of diseases and such a large number of processing algorithms, system designers are often misleading as to which algorithms combination should be used to classify which kind of diseases. To eliminate this confusion, this paper compare and objectively evaluate some of the latest existing techniques in this field, and evaluate the best fusion of algorithms that can be used to develop a highly accurate classification system for leaf disease. This finding suggests plenty more research directions which must be undertaken to enhance system efficiency.
机译：粮食生产是每个经济体的支柱，并依赖于农业生产者的产量。该产量通常受整个给定果实植物生长过程中发生的微观和宏观疾病的影响。例如，一些影响香蕉叶的疾病包括尖孢镰刀菌，音乐霉菌，museosphaerella musae，胡萝卜欧文氏菌，假单胞菌茄，黑斑病，黑斑病，埃里奥那氏菌，BSV和BBM病毒。此类病原体数不胜数，因此，对于分类和建议针对这些病毒的补救措施，认为有必要在图像处理方面取得进步。在该地区进行的大量工作都针对线性方法，其中通过分类，属性提取和定义来检测美学上可检测到的疾病。但是，这些干预措施无法适应更大和更多样化的数据集，因此机器学习和基于人工智能的方法（例如Q学习，强化学习等）负责制定人眼看不见的疾病。在处理层的每个组件中。由于疾病种类繁多，处理算法数量众多，因此系统设计人员通常会误导应使用哪种算法组合来对哪种疾病进行分类。为了消除这种混淆，本文比较并客观地评估了该领域中一些最新的现有技术，并评估了可用于开发高度准确的叶病分类系统的算法的最佳融合。这一发现表明，必须采取更多的研究方向来提高系统效率。

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