An Image Processing Framework To Identify Rice Blast

机译：识别稻瘟病的图像处理框架

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An early detection of rice plant disease especially rice plant leaves disease detection can assist farmers to take necessary precaution at the early stage and can achieve better quality of crops. Rice plant can be affected by various types of fungal infec

机译：早期发现水稻病害，尤其是水稻叶片病害，可以帮助农民在早期阶段采取必要的预防措施，并获得更好的农作物质量。水稻植物会受到各种真菌感染的影响

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《International Congress on Human-Computer Interaction, Optimization and Robotic Applications》|2020年|1-5|共5页
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T. M. Shahriar Sazzad; Ayrin Anwar; Mahiya Hasan; Md. Ismile Hossain;
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Diseases; Image color analysis; Image segmentation; Agriculture; Feature extraction; Computer science;

机译：疾病;图像颜色分析;图像分割;农业;特征提取;计算机科学;

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1. CRBLASTER: A parallel-processing computational framework for embarrassingly parallel image-analysis algorithms [J] . Mighell K.J. Publications of the Astronomical Society of the Pacific . 2010,第896期

机译：CRBLASTER：令人尴尬的并行图像分析算法的并行处理计算框架
2. RATINGS OF RICE LEAF BLAST DISEASE BASED ON IMAGE PROCESSING AND STEPWISE REGRESSION [J] . Xiao M., Deng Z., Ma Y., Applied Engineering in Agriculture . 2019,第6期

机译：基于图像处理和逐步回归的水稻叶毒病评级
3. A Step towards Precision Farming of Rice Crop by Estimating Loss Caused by Leaf Blast Disease Using Digital Image Processing and Fuzzy Clustering [J] . Toran Verma, Susanta Kumar Satpathy, Lokesh Kumar Sharma International Journal of Computer Trends and Technology . 2011,第2期

机译：利用数字图像处理和模糊聚类估算叶瘟病造成的损失，从而向水稻作物的精确农业迈出了一步
4. A Computer Based Image Processing Approach to Identify Rice Blast [C] . T. M. Shahriar Sazzad, Ayrin Anwar, Sabrin Islam, International Conference on Sustainable Technologies for Industry 4.0 . 2020

机译：基于计算机的图像处理方法来识别稻瘟病
5. Two new image processing algorithms and a framework for Internet-based image processing. [D] . Wang, Dongyan. 1998

机译：两种新的图像处理算法和基于Internet的图像处理框架。
6. Evaluation of image processing technique in identifying rice blast disease in field conditions based on KNN algorithm improvement by K‐means [O] . Mohammad Reza Larijani, Ezzatollah Askari Asli‐Ardeh, Ehsan Kozegar, 2019

机译：基于K均值改进KNN算法的田间鉴定稻瘟病图像处理技术评价。
7. ABSTRACT Various body parts or organs can be analysed to identify the different diseases in the human body. Fingernail analysis is one of the ways to identify disease in the human body. Nails are the body part which are farthest from the heart and therefore receive oxygen at last. As a result the nails are the first who show the symptoms of a disease in the human body. Fingernails can be easily captured for diagnosis and there are no heavy equipment or no specific conditions required to use nail image for disease diagnosis, like in other tests and scanning processes. Human nails deliver beneficial information about complaints or any nutritive imbalances in the human body depending upon their shape, texture and colour. In human beings, numerous systemic and skin diseases can be easily analyzed through careful examination of nails of both the limbs. A lot of nail illnesses have been found to be primary signs of numerous underlying systemic illnesses. The colour, texture or shape changes in nails are signs of many diseases mainly affecting nails. Considering all these properties of nails a system is proposed that uses digital image processing (DIP) methods for identifying such changes in the human nail to get more precise results and predict numerous diseases effortlessly. With the emerging Internet of Things (IOT) concept the generated report is made available remotely, this will help users to reduce transportation efforts. As the system has to deal with large and private data, the security of data must be ensured. To keep the data confidential, the Blockchain concept which is one of the most emerging concepts in the field of data management is used. The paper contains the implementation of the digital image processing for feature extraction of nail images, usage of IOT (ThingSpeak cloud) for data storage and implementation of Blockchain to keep the system secured and theft free. KEY WORDS: Int ernet of thin gs (IOT), Image proc essin g, Thin gSpeak, RG B vavalues, Mean pi xel vavalues, Bloc kchain , Hash key. Disease Diagnostic System: Abnormalities in Human Nail [O] . Pranav S. Wazarkar 2020

机译：摘要的各个身体部位或器官可被分析以识别在人体内的不同的疾病。指甲分析来识别人体疾病的方法之一。指甲是身体一部分是离心脏最远，因此在最后接受氧气。作为结果，指甲是第一谁表现出人体疾病的症状。指甲可以容易地捕获用于诊断和没有重装或需要使用指甲图像用于疾病诊断，比如在其他测试和扫描过程没有特定的条件。人的指甲提供有关投诉或取决于它们的形状，纹理和色彩在人体内的任何营养失衡有益的信息。在人类中，许多全身性皮肤疾病是可以很容易地通过两个四肢指甲的仔细检查分析。很多指甲病已发现众多潜在系统性疾病的主要症状。在指甲的颜色，质地和形状的变化是许多疾病主要影响指甲的迹象。考虑到所有的指甲的这些性能的系统被提出，用于识别人指甲这样的变化以获得更精确的结果，并毫不费力预测许多疾病用途的数字图像处理（DIP）方法。随着物联网（IOT）的概念，新兴的互联网将生成的报告提供远程，这将帮助用户降低运输工作。由于系统必须处理大量的私人数据，数据的安全性必须得到保证。为了保持数据的机密性，使用Blockchain的概念，它是在数据管理领域的大多数新兴的概念之一。本文包含了数字图像处理的指甲图像，IOT（ThingSpeak云）的使用为数据存储和执行Blockchain的特征提取的执行，以保持固定的系统和盗窃免费。关键词：诠释薄GS（IOT），图像的ERNET PROC essin克，薄型gSpeak，RG乙vavalues，平均数PI XEL vavalues，阵营kchain，哈希密钥。疾病诊断系统：在人类指甲异常

An Image Processing Framework To Identify Rice Blast

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