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首页> 外文期刊>Journal of computer sciences >Farmer-Friendly Mobile Application for Automated Leaf Disease Detection of Real-Time Augmented Data Set using Convolution Neural Networks
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Farmer-Friendly Mobile Application for Automated Leaf Disease Detection of Real-Time Augmented Data Set using Convolution Neural Networks

机译：使用卷积神经网络的自动叶病检测实时增强数据的农民友好的移动应用

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摘要

In farming, crops are prone to a wide variety of diseases. The impact of sudden climatic change has adverse effects on their growth, providing incubation to harmful viruses and bacteria. Diseases to crops imply a significant negative impact on health, economy and livelihood of the human population. According to the data from the Food and Agricultural Organization (FAO), an average of 1.3 billion tonnes of food crops succumb to such diseases annually. This paper presents an approach to prevent such diseases from propagating, by early diagnosis of such abnormalities in leaves using state of the art deep learning techniques. Convolution Neural Networks based model with additional pre-processing techniques are developed to classify the leaves into affected and healthy category. Various Deep Learning architectures and hyperparameter tuning were carried out and the resulting model produces an accuracy of up to 95% with 400 actual leaf images and up to 98% with 3600 augmented datasets. The models are trained on real-life leaf images of crops, captured from an actual agricultural field. A user-intuitive IoT Web Application is developed to capture, process and display the predicted result (disease status) from the model.
机译：在农业中，作物易患各种各样的疾病。突然气候变化的影响对其生长产生了不利影响，提供对有害病毒和细菌的孵育。作物的疾病意味着对人口的健康，经济和生计产生了显着的负面影响。根据粮食和农业组织（粮农组织）的数据，平均每年平均为13亿吨粮食作物屈服于此类疾病。本文呈现了一种方法，以防止这种疾病在使用艺术状态的深层学习技术的叶片中的这种异常的早期诊断。基于卷积的基于神经网络的模型具有额外的预处理技术，以将叶子分类为受影响和健康的类别。进行了各种深度学习架构和封锁统计调整，结果模型产生高达95％的精度，400个实际叶片图像，3600个增强数据集高达98％。这些模型培训了从实际农业领域捕获的作物的现实叶片图像上。开发了用户直观的IOT Web应用程序以从模型中捕获，处理和显示预测结果（疾病状态）。

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