Rice Plant Leaf Disease Detection and Classification Using Optimization Enabled Deep Learning

IF 6 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
T. Daniya, S. Vigneshwari
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引用次数: 3

Abstract

An automatic identification and classification of rice diseases are very important in the domain of agriculture. Deep learning (DL) is an effective research area in the identification of agriculture pattern identification where it can effectively resolve the issues of diseases identification. In this paper, a hybrid optimization algorithm is developed to categorize the plant diseases. The pre-processing is made using Region of Interest (ROI) extraction and the input image is created by combining the Rice plant dataset, and Rice disease dataset. The segmentation is accomplished using Deep fuzzy clustering. The features, like statistical features, entropy, Convolutional Neural Network (CNN) features, Local Optimal-Oriented Pattern (LOOP), and Local Gabor XOR Pattern (LGXP) is considered for extracting the appropriate features for further processing. The data augmentation is employed to enlarge the volume of extracted features. Then, the first level classification is made by deep neuro-fuzzy network (DNFN), which is trained using Rider Henry Gas Solubility Optimization (RHGSO) that categories into healthy and unhealthy plants. The RHGSO is the integration of Rider Optimization Algorithm (ROA) and Henry gas solubility optimization (HGSO). After that, second-level classification is made by a Deep residual network (DRN) that is tuned by RHGSO. Thus, the RHGSO-based DRN categorizes the unhealthy plants into Bacterial Leaf Blight (BLB), Blast, and Brown spot. Thus, the implementation of the proposed RHGSO-based deep learning approach offered better accuracy, sensitivity, specificity, and F1-score of 0.9304, 0.9459, 0.8383, and 0.9142.
基于深度学习的水稻叶片病害检测与分类
水稻病害的自动识别与分类在农业领域具有十分重要的意义。深度学习是农业模式识别中一个有效的研究领域,它可以有效地解决病害识别问题。本文提出了一种植物病害分类的混合优化算法。利用感兴趣区域(Region of Interest, ROI)进行预处理,并结合水稻植物数据集和水稻病害数据集生成输入图像。使用深度模糊聚类实现分割。考虑了统计特征、熵、卷积神经网络(CNN)特征、局部最优导向模式(LOOP)和局部Gabor XOR模式(LGXP)等特征,以提取适当的特征进行进一步处理。数据增强是为了扩大提取的特征量。然后,采用基于Rider Henry气体溶解度优化(RHGSO)的深度神经模糊网络(DNFN)进行第一级分类,该网络将植物分为健康植物和不健康植物。RHGSO是Rider优化算法(ROA)和Henry气体溶解度优化算法(HGSO)的结合。然后,通过RHGSO调优的深度残差网络(Deep residual network, DRN)进行二级分类。因此,基于rhgso的DRN将有害植物分为细菌性叶枯病(BLB)、Blast和Brown spot。因此,基于rhgso的深度学习方法具有更好的准确率、灵敏度、特异性,f1评分分别为0.9304、0.9459、0.8383和0.9142。
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来源期刊
Journal of Environmental Informatics
Journal of Environmental Informatics ENVIRONMENTAL SCIENCES-
CiteScore
12.40
自引率
2.90%
发文量
7
审稿时长
24 months
期刊介绍: Journal of Environmental Informatics (JEI) is an international, peer-reviewed, and interdisciplinary publication designed to foster research innovation and discovery on basic science and information technology for addressing various environmental problems. The journal aims to motivate and enhance the integration of science and technology to help develop sustainable solutions that are consensus-oriented, risk-informed, scientifically-based and cost-effective. JEI serves researchers, educators and practitioners who are interested in theoretical and/or applied aspects of environmental science, regardless of disciplinary boundaries. The topics addressed by the journal include: - Planning of energy, environmental and ecological management systems - Simulation, optimization and Environmental decision support - Environmental geomatics - GIS, RS and other spatial information technologies - Informatics for environmental chemistry and biochemistry - Environmental applications of functional materials - Environmental phenomena at atomic, molecular and macromolecular scales - Modeling of chemical, biological and environmental processes - Modeling of biotechnological systems for enhanced pollution mitigation - Computer graphics and visualization for environmental decision support - Artificial intelligence and expert systems for environmental applications - Environmental statistics and risk analysis - Climate modeling, downscaling, impact assessment, and adaptation planning - Other areas of environmental systems science and information technology.
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