基于OkraNet框架的秋葵病害检测与成熟期分类

IF 2 3区 农林科学 Q2 AGRONOMY
Utkarsh Varman, Shoba Sivapatham, Vijayakumar K P, K. Pradeep, Dheeraj Sharma
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引用次数: 0

摘要

秋葵(Abelmoschus esculentus)是印度农业部门的重要作物,占其产量的三分之一。确定新鲜和成熟的秋葵植物以获得最大的产量和利润是非常具有挑战性的。成熟度可以通过形状、长度、颜色变化和水分含量来确定。然而,为了减少这种耗时的工作,本工作强调将新鲜和患病秋葵叶的分类作为第一步,并评估成熟度阶段,包括成熟、未成熟和过熟。从实时农场收集OkraFarm数据集,以确定成熟阶段。在最先进的卷积神经网络的基础上,进行了三个实验,对鲜熟秋葵进行了分层识别——实验一:使用预训练的VGG19模型进行叶病分类,准确率最高达到98.89%;实验2:使用YOLOv5模型检测秋葵果实,最高准确率达到84.5%;实验3:利用MLSMOTE算法处理数据不平衡,将秋葵植物的成熟阶段分为成熟、过熟和未成熟,在测试数据上达到了96.10%的最高准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An integrated OkraNet Framework for detection of disease and maturity stage classification in okra farming

Okra (Abelmoschus esculentus) is a vital crop in the Indian agriculture sector, producing one-third of its production. Identifying fresh and ripe okra plants for maximum yield and profit is significantly challenging. Ripeness can be determined by shape, length, color variation, and moisture content. However, to reduce this time-consuming effort, this work emphasizes the classification of fresh and diseased okra leaves as the initial step and assesses the maturity stages, including ripe, unripe, and overripe. The OkraFarm dataset was collected from the real-time farm to determine the maturity stage. Building on state-of-the-art convolutional neural networks, three experiments are performed to lay identification of fresh and ripe okra—Experiment 1: leaf disease classification using the pre-trained VGG19 model achieving a maximum accuracy of 98.89%; Experiment 2: detection of okra fruit using the YOLOv5 model, achieving a maximum accuracy of 84.5%; Experiment 3: handling data imbalance using the MLSMOTE algorithm and classifying the maturity stages of the okra plant into ripe, overripe, and unripe, achieving a maximum accuracy of 96.10% on the test data.

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来源期刊
Agronomy Journal
Agronomy Journal 农林科学-农艺学
CiteScore
4.70
自引率
9.50%
发文量
265
审稿时长
4.8 months
期刊介绍: After critical review and approval by the editorial board, AJ publishes articles reporting research findings in soil–plant relationships; crop science; soil science; biometry; crop, soil, pasture, and range management; crop, forage, and pasture production and utilization; turfgrass; agroclimatology; agronomic models; integrated pest management; integrated agricultural systems; and various aspects of entomology, weed science, animal science, plant pathology, and agricultural economics as applied to production agriculture. Notes are published about apparatus, observations, and experimental techniques. Observations usually are limited to studies and reports of unrepeatable phenomena or other unique circumstances. Review and interpretation papers are also published, subject to standard review. Contributions to the Forum section deal with current agronomic issues and questions in brief, thought-provoking form. Such papers are reviewed by the editor in consultation with the editorial board.
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