AgriCure: A web application based layered augmentation-enhanced YOLOv8 for disease and nutrient deficiency detection in bitter gourd leaves

IF 4.5 Q1 PLANT SCIENCES
Kamaldeep Joshi , Sumit Kumar , Varun Kumar , Rainu Nandal , Yogesh Kumar , Narendra Tuteja , Ritu Gill , Sarvajeet Singh Gill
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引用次数: 0

Abstract

Bitter gourd is an important cucurbitaceous vegetable widely grown in India and other tropical and subtropical regions and appreciated for its nutritional, medicinal, and economic values. Traditional way of detecting diseases and nutrient deficiencies in bitter gourd leaves requires significant effort and expertise whereas, precision farming and automated disease detection methods can greatly support farmers by facilitating sustainable agriculture To address this challenge a novel web based application AgriCure was developed which incorporated a multilevel approach to detect the plant disease and nutrient deficiency with high level. It uses a hybrid augmentation-based YOLOv8 DL model for image analysis. The study focuses on detecting diseases like Downy Mildew, Leaf Spot, and Jassid, as well as nutrient deficiencies such as Potassium, Magnesium, and Nitrogen Deficiency and their combinations. The initial dataset of 785 images was increased to 2430 images using advanced data augmentation. The results on the augmented dataset after 100 epochs demonstrated high effectiveness with the augmented dataset. The model achieved an impressive mean Average Precision (mAP50) of 92.9 % at an Intersection over Union (IoU) threshold of 0.50 and a mAP50–95 of 91.5 % across IoU thresholds from 0.50 to 0.95. Nearly all predicted positive instances were true positives, with a precision rate of 89.6 % and a recall of 86.6 %, which showed the capacity of the model in identifying true positives. The F1 score of 91.66 % highlighted balanced performance of the model between precision and recall, emphasising its reliability and accuracy. The model shows low losses, with a Box loss of 0.2435, a Class loss of 0.1689, and a Distribution Focal Loss (dfl loss) of 0.9024. This approach offered a valuable tool for early and accurate detection of disease and nutrient deficiency. Detection results indicate that, compared to previous methods, the proposed approach significantly improves overall performance and addresses challenges tied to limited dataset sizes.
农业:基于web应用程序的分层增强YOLOv8,用于苦瓜叶片的疾病和营养缺乏检测
苦瓜是一种重要的葫芦科蔬菜,广泛种植在印度和其他热带和亚热带地区,具有营养、药用和经济价值。传统的检测苦瓜叶片疾病和营养缺乏的方法需要大量的努力和专业知识,而精准农业和自动化疾病检测方法可以通过促进可持续农业极大地支持农民。为了解决这一挑战,开发了一种新的基于web的应用程序农业,该应用程序结合了多层次的方法来检测植物疾病和营养缺乏。它使用基于混合增强的YOLOv8 DL模型进行图像分析。该研究的重点是检测霜霉病、叶斑病和茉莉病等疾病,以及钾、镁、氮缺乏及其组合等营养缺乏症。使用高级数据增强技术,将初始数据集785张图像增加到2430张图像。在增强数据集上进行100次epoch后的结果表明,增强数据集具有较高的有效性。该模型实现了令人印象深刻的平均精度(mAP50),在交叉口交叉口(IoU)阈值为0.50时,平均精度(mAP50)为92.9 %,在IoU阈值为0.50至0.95时,平均精度(mAP50 - 95)为91.5 %。几乎所有预测的阳性实例都是真阳性,准确率为89.6 %,召回率为86.6 %,表明该模型具有识别真阳性的能力。F1得分为91.66 %,突出了模型在查准率和查全率之间的平衡表现,强调了模型的可靠性和准确性。该模型具有较低的损耗,盒损耗为0.2435,类损耗为0.1689,分布焦损耗(dfl损耗)为0.9024。这种方法为早期准确检测疾病和营养缺乏提供了有价值的工具。检测结果表明,与以前的方法相比,所提出的方法显着提高了整体性能,并解决了与有限数据集大小相关的挑战。
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来源期刊
Current Plant Biology
Current Plant Biology Agricultural and Biological Sciences-Plant Science
CiteScore
10.90
自引率
1.90%
发文量
32
审稿时长
50 days
期刊介绍: Current Plant Biology aims to acknowledge and encourage interdisciplinary research in fundamental plant sciences with scope to address crop improvement, biodiversity, nutrition and human health. It publishes review articles, original research papers, method papers and short articles in plant research fields, such as systems biology, cell biology, genetics, epigenetics, mathematical modeling, signal transduction, plant-microbe interactions, synthetic biology, developmental biology, biochemistry, molecular biology, physiology, biotechnologies, bioinformatics and plant genomic resources.
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