将机器学习与智能手机相结合,检测并推荐植物疾病的正确治疗方法

Q4 Mathematics
Dr. Nidhi Mishra, Dr. F Rahman, Dr. PritiKumari
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引用次数: 0

摘要

植物病害在世界范围内造成了重大的作物歉收,为了使植物病害的鉴别证据和治疗方法更加可行,人们进行了大量的探索和努力。对于牧场主来说,选择利用正在进行的创新来解决园艺创造面临的困难,从而进一步发展作物创造和活动生产力,将是非常有利的。在这项工作中,我们计划并执行了一个易于理解的基于手机的植物疾病发现和治疗建议框架,利用人工智能(ML)方法。利用CNN提取包括,利用ANN和KNN排序植物感染;采用基于物质的筛选建议计算方法,对分类后公认的植物病害进行有效药物推荐。执行后的效果表明,该框架能够准确识别植物病害并提出治疗建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Apply Machine Learning with Smartphones to Detect and Recommend the Right Treatment for Plant Diseases
Plant sicknesses cause significant harvest creation misfortunes around the world, and a great deal of huge exploration exertion has been coordinated toward making plant infection distinguishing proof and therapy methods more viable. It would be of extraordinary advantage to ranchers to have the option to use the ongoing innovation to use the difficulties confronting horticultural creation and thus further develop crop creation and activity productivity. In this work, we planned and executed an easy-to-understand cell phone-based plant illness discovery and treatment suggestion framework utilizing AI (ML) methods. CNN was utilized for include extraction while the ANN and KNN were utilized to order the plant infections; a substance-based sifting proposal calculation was utilized to recommend significant medicines for the recognized plant illnesses after grouping. The aftereffect of the execution shows that the framework accurately identified and suggested treatment for plant illnesses.
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来源期刊
Philippine Statistician
Philippine Statistician Mathematics-Statistics and Probability
CiteScore
0.50
自引率
0.00%
发文量
92
期刊介绍: The Journal aims to provide a media for the dissemination of research by statisticians and researchers using statistical method in resolving their research problems. While a broad spectrum of topics will be entertained, those with original contribution to the statistical science or those that illustrates novel applications of statistics in solving real-life problems will be prioritized. The scope includes, but is not limited to the following topics:  Official Statistics  Computational Statistics  Simulation Studies  Mathematical Statistics  Survey Sampling  Statistics Education  Time Series Analysis  Biostatistics  Nonparametric Methods  Experimental Designs and Analysis  Econometric Theory and Applications  Other Applications
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