A deep learning approach to detect diseases in pomegranate fruits via hybrid optimal attention capsule network

IF 5.8 2区 环境科学与生态学 Q1 ECOLOGY Ecological Informatics Pub Date : 2024-11-12 DOI:10.1016/j.ecoinf.2024.102859
P. Sajitha , A. Diana Andrushia , N. Anand , M.Z. Naser , Eva Lubloy
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Abstract

In 2022, the production rate of pomegranate is estimated at approximately 4.8 million metric tons. Unfortunately, these fruits are susceptible to many different kinds of diseases caused by bacterial, viral, and fungal infections. Such diseases can have a major negative impact on fruit quality, production, and the profitability of pomegranate cultivation. Nowadays, several machine learning and deep learning methods are used to identify pomegranate fruit diseases automatically and effectively. In post-harvest pomegranate fruit disease detection, deep learning has great potential to extract complex patterns and features from large datasets. This can improve disease identification accuracy, enabling more efficient disease control, lower crop losses, and better resource management. The proposed work introduces an intelligent deep learning-based approach for accurately detecting pomegranate diseases, begins with Improved Guided Image Filtering (Improved GIF) and resizing to pre-process fruit images, followed by feature extraction (shape, color, texture) using GLCM and GLRLM to streamline classification. Extracted features are then fed into a novel Hybrid Optimal Attention Capsule Network (Hybrid OACapsNet), which classifies the images as normal or diseased, conditions such as bacterial blight, heart rot, and scab. Our analysis indicates that the proposed classifier has a classification accuracy of 99.19 %, precision of 98.45 %, recall of 98.41 %, F1-score of 98.43 %, and specificity of 99.45 % compared to other techniques. So this approach offers a framework, which is a feasible solution for automated detection of diseases in fruits, thereby benefiting farmers and supporting their farming operations.

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通过混合最优注意力胶囊网络检测石榴果实病害的深度学习方法
2022 年,石榴产量预计约为 480 万公吨。不幸的是,这些水果很容易受到细菌、病毒和真菌感染引起的多种不同病害的侵袭。这些病害会对石榴果实的质量、产量和种植收益产生严重的负面影响。如今,一些机器学习和深度学习方法被用于自动有效地识别石榴果实病害。在采后石榴果实病害检测方面,深度学习在从大型数据集中提取复杂模式和特征方面具有巨大潜力。这可以提高病害识别的准确性,从而实现更高效的病害控制、更低的作物损失和更好的资源管理。拟议的工作引入了一种基于深度学习的智能方法来准确检测石榴病害,首先是改进的引导图像过滤(改进的 GIF)和调整大小来预处理水果图像,然后使用 GLCM 和 GLRLM 提取特征(形状、颜色、纹理)以简化分类。然后将提取的特征输入一个新颖的混合最佳注意力胶囊网络(Hybrid OACapsNet),该网络可将图像分为正常或有病的图像,如细菌性枯萎病、心腐病和疮痂病。我们的分析表明,与其他技术相比,所提出的分类器的分类准确率为 99.19%,精确率为 98.45%,召回率为 98.41%,F1 分数为 98.43%,特异性为 99.45%。因此,这种方法提供了一个框架,是自动检测水果病害的可行解决方案,从而使农民受益并支持他们的农业生产。
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来源期刊
Ecological Informatics
Ecological Informatics 环境科学-生态学
CiteScore
8.30
自引率
11.80%
发文量
346
审稿时长
46 days
期刊介绍: The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.
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