用深度学习提升孟加拉作物病害识别精度，助力农业减损
Plant Leaf Disease Detection and Classification Using Deep Learning: A Review and A Proposed System on Bangladesh's Perspective
- 基于卷积神经网络构建病叶分类模型，利用Kaggle数据集训练
- 在17,430张图像上实现14类病害的高精度检测，准确率显著提升
- 适用于孟加拉农业场景，为农户提供低成本智能诊断工具
孟加拉国农业关乎就业、GDP贡献及民生，是减贫与粮食安全的关键。植物病害严重制约农业生产，人工难以肉眼识别病叶，延误防治常导致前期劳动付诸东流。基于图像的深度学习技术在病害识别中表现优异。本文针对孟加拉国情，提出一种改进的卷积神经网络（CNN）模型，采集辣椒、番茄、马铃薯三类作物的病叶图像。采用来自Kaggle的植物病叶数据集（共17,430张图像，14个病害类别）进行训练与测试。实验表明，该模型在病害检测与分类任务中表现高效，具备良好的应用潜力，可有效支持作物病害管理。
原文摘要 · Abstract (English)
A very crucial part of Bangladeshi people's employment, GDP contribution, and mainly livelihood is agriculture. It plays a vital role in decreasing poverty and ensuring food security. Plant diseases are a serious stumbling block in agricultural production in Bangladesh. At times, humans can't detect the disease from an infected leaf with the naked eye. Using inorganic chemicals or pesticides in plants when it's too late leads in vain most of the time, deposing all the previous labor. The deep-learning technique of leaf-based image classification, which has shown impressive results, can make the work of recognizing and classifying all diseases trouble-less and more precise. In this paper, we've mainly proposed a better model for the detection of leaf diseases. Our proposed paper includes the collection of data on three different kinds of crops: bell peppers, tomatoes, and potatoes. For training and testing the proposed CNN model, the plant leaf disease dataset collected from Kaggle is used, which has 17,430 images. The images are labeled with 14 separate classes of damage. The developed CNN model performs efficiently and could successfully detect and classify the tested diseases. The proposed CNN model may have great potency in crop disease management.
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