Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

Uzair Aslam Bhatti, Sibghat Ullah Bazai, Shumaila Hussain, Shariqa Fakhar, Chin Soon Ku, Shah Marjan, Por Lip Yee, Liu Jing
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Abstract

Crop diseases have a significant impact on plant growth and can lead to reduced yields. Traditional methods of disease detection rely on the expertise of plant protection experts, which can be subjective and dependent on individual experience and knowledge. To address this, the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease identification. In this paper, we propose a novel approach that utilizes a convolutional neural network (CNN) model in conjunction with Inception v3 to identify plant leaf diseases. The research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific diseases. The models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct classes. Through rigorous training and evaluation, the proposed system achieved an impressive accuracy rate of 99%. This mobile application serves as a convenient and valuable advisory tool, providing early detection and guidance in real agricultural environments. The significance of this research lies in its potential to revolutionize plant disease detection and management practices. By automating the identification process through deep learning algorithms, the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual expertise. The integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.
基于深度学习的树木病害识别与分类研究
作物病害对植物生长有重大影响,并可能导致产量下降。传统的疾病检测方法依赖于植物保护专家的专业知识,这可能是主观的,依赖于个人的经验和知识。为了解决这个问题,数字图像识别技术和深度学习算法的使用已经成为自动化植物病害识别的一种有前途的方法。在本文中,我们提出了一种利用卷积神经网络(CNN)模型结合Inception v3来识别植物叶片疾病的新方法。这项研究的重点是开发一个移动应用程序,利用这一机制来识别植物中的疾病,并为克服特定疾病提供建议。这些模型使用一个由80,848张图像组成的数据集进行训练,这些图像代表了21种不同的植物叶片,分为60个不同的类别。经过严格的训练和评估,所提出的系统达到了令人印象深刻的99%的准确率。这个移动应用程序作为一个方便和有价值的咨询工具,在真实的农业环境中提供早期检测和指导。这项研究的意义在于它有可能彻底改变植物病害检测和管理实践。通过深度学习算法自动化识别过程,该系统消除了基于专家的诊断的主观性,减少了对个人专业知识的依赖。移动技术的整合进一步提高了可及性,使农民和农业从业者能够迅速、准确地识别作物中的疾病。
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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