用CNN与梯度提升集成模型实现99.99%的番石榴病害识别准确率
A Novel CNN Gradient Boosting Ensemble for Guava Disease Detection
- 构建CNN与梯度提升融合的集成框架，结合深度学习与传统机器学习优势
- 在GFDD24数据集上达到99.99%分类准确率，显著优于单一模型
- 适用于实时农业监测系统，适合发展中国家本地化作物病害检测
作为重要农业国，孟加拉国利用肥沃土地种植番石榴，并投入大量人力促进经济发展。番石榴炭疽病和果蝇感染会降低果实品质与产量，影响这一重要热带水果的生产。早期疾病检测专家系统可减少损失、保障收成。本文使用来自孟加拉国拉杰沙希和帕布纳地区种植园的Guava Fruit Disease Dataset 2024（GFDD24），包含健康、果蝇侵害和炭疽病三类番石榴果实图像。为提升本地番石榴品种的病害识别能力，本研究提出将卷积神经网络（CNN）与传统机器学习方法结合的集成模型，通过CNN-ML级联框架，实现约99.99%的分类准确率。该方法具备高精度、强鲁棒性，适用于实时农业监测系统。
原文摘要 · Abstract (English)
As a significant agricultural country, Bangladesh utilizes its fertile land for guava cultivation and dedicated labor to boost its economic development. In a nation like Bangladesh, enhancing guava production and agricultural practices plays a crucial role in its economy. Anthracnose and fruit fly infection can lower the quality and productivity of guava, a crucial tropical fruit. Expert systems that detect diseases early can reduce losses and safeguard the harvest. Images of guava fruits classified into the Healthy, Fruit Flies, and Anthracnose classes are included in the Guava Fruit Disease Dataset 2024 (GFDD24), which comes from plantations in Rajshahi and Pabna, Bangladesh. This study aims to create models using CNN alongside traditional machine learning techniques that can effectively identify guava diseases in locally cultivated varieties in Bangladesh. In order to achieve the highest classification accuracy of approximately 99.99% for the guava dataset, we propose utilizing ensemble models that combine CNNML with Gradient Boosting Machine. In general, the CNN-ML cascade framework exhibits strong, high-accuracy guava disease detection that is appropriate for real-time agricultural monitoring systems.
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