Enhanced-RICAP: a novel data augmentation strategy for improved deep learning-based plant disease identification and mobile diagnosis.

IF 4.1 2区 生物学 Q1 PLANT SCIENCES
Frontiers in Plant Science Pub Date : 2025-09-24 eCollection Date: 2025-01-01 DOI:10.3389/fpls.2025.1646611
Mamadou Bailo Diallo, Yue Li, Okafor Sylevester Chukwuka, Solomon Boamah, Yuhong Gao, Mohamed Meyer Kana Kone, Gelebo Rocho, Linjing Wei
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引用次数: 0

Abstract

Introduction: Plant diseases pose a significant threat to global food security and agricultural productivity, making accurate and timely disease identification essential for effective crop management and minimizing economic losses. Although data augmentation techniques such as RICAP improve model robustness, their reliance on randomly extracted image regions can introduce label noise, potentially misleading the training of deep learning models.

Methods: This study introduces Enhanced-RICAP, an advanced data augmentation technique designed to improve the accuracy of deep learning models for plant disease detection. Enhanced-RICAP replaces random patch selection with an attention module guided by class activation maps, focusing on discriminative regions, Enhanced-RICAP reduces label noise and improves model accuracy for plant disease detection, addressing a key limitation of traditional augmentation methods. The method was evaluated using several deep learning architectures, such as ResNet18, ResNet34, ResNet50, EfficientNet-b, and Xception, on the cassava leaf disease and PlantVillage tomato leaf disease datasets.

Results: The experimental results demonstrate that Enhanced-RICAP consistently outperforms existing augmentation methods, including CutMix, MixUp, CutOut, Hide-and-Seek, and RICAP, across key evaluation metrics: accuracy, precision, recall, and F1-score. The ResNet18+Enhanced-RICAP configuration achieved 99.86% accuracy on the tomato leaf disease dataset, whereas the Xception+Enhanced-RICAP model attained 96.64% accuracy in classifying four cassava leaf disease categories.

Discussion and conclusion: To bridge the gap between research and practical application, the ResNet18+Enhanced-RICAP model was deployed in PlantDisease, a mobile application that enables real-time disease identification and management recommendations. This approach supports sustainable agriculture and strengthens food security by providing farmers with accessible and reliable diagnostic tools.

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Abstract Image

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Enhanced-RICAP:一种新的数据增强策略,用于改进基于深度学习的植物病害识别和移动诊断。
植物病害对全球粮食安全和农业生产力构成重大威胁,因此准确及时地识别病害对有效的作物管理和尽量减少经济损失至关重要。尽管RICAP等数据增强技术提高了模型的鲁棒性,但它们对随机提取的图像区域的依赖可能会引入标签噪声,从而可能误导深度学习模型的训练。方法:本研究引入了Enhanced-RICAP,一种先进的数据增强技术,旨在提高植物病害检测的深度学习模型的准确性。增强的ricap用类激活图引导的关注模块取代了随机斑块选择,聚焦于判别区域,降低了标签噪声,提高了植物病害检测模型的准确性,解决了传统增强方法的一个关键局限性。利用ResNet18、ResNet34、ResNet50、EfficientNet-b和Xception等深度学习架构,在木薯叶病和PlantVillage番茄叶病数据集上对该方法进行了评估。结果:实验结果表明,增强的RICAP在准确率、精密度、召回率和f1分数等关键评估指标上始终优于现有的增强方法,包括CutMix、MixUp、CutOut、Hide-and-Seek和RICAP。ResNet18+Enhanced-RICAP配置在番茄叶病数据集上的准确率为99.86%,而Xception+Enhanced-RICAP模型在4种木薯叶病分类上的准确率为96.64%。讨论与结论:为了弥合研究与实际应用之间的差距,ResNet18+Enhanced-RICAP模型被部署在PlantDisease中,这是一个能够实时识别疾病并提供管理建议的移动应用程序。这种方法通过向农民提供可获得和可靠的诊断工具,支持可持续农业并加强粮食安全。
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来源期刊
Frontiers in Plant Science
Frontiers in Plant Science PLANT SCIENCES-
CiteScore
7.30
自引率
14.30%
发文量
4844
审稿时长
14 weeks
期刊介绍: In an ever changing world, plant science is of the utmost importance for securing the future well-being of humankind. Plants provide oxygen, food, feed, fibers, and building materials. In addition, they are a diverse source of industrial and pharmaceutical chemicals. Plants are centrally important to the health of ecosystems, and their understanding is critical for learning how to manage and maintain a sustainable biosphere. Plant science is extremely interdisciplinary, reaching from agricultural science to paleobotany, and molecular physiology to ecology. It uses the latest developments in computer science, optics, molecular biology and genomics to address challenges in model systems, agricultural crops, and ecosystems. Plant science research inquires into the form, function, development, diversity, reproduction, evolution and uses of both higher and lower plants and their interactions with other organisms throughout the biosphere. Frontiers in Plant Science welcomes outstanding contributions in any field of plant science from basic to applied research, from organismal to molecular studies, from single plant analysis to studies of populations and whole ecosystems, and from molecular to biophysical to computational approaches. Frontiers in Plant Science publishes articles on the most outstanding discoveries across a wide research spectrum of Plant Science. The mission of Frontiers in Plant Science is to bring all relevant Plant Science areas together on a single platform.
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