用深度学习同时识别17种作物34种病害，准确率达99%
Detecting Multiple Diseases in Multiple Crops Using Deep Learning
- 构建统一数据集覆盖17种作物34种病害
- 准确率99%，比现有方法高7个百分点
- 专为印度农业多样性设计，适合农民实用
印度作为以农业为主的经济体，面临病害、虫害和环境胁迫导致的重大作物损失。早期精准识别多种作物的病害对提高产量、保障粮食安全至关重要。本文提出一种基于深度学习的多作物多病害检测方案，旨在覆盖印度多样的农业环境。我们首先整合了来自多个公开来源的图像，构建了一个包含17种作物和34种病害的统一数据集。所提出的深度学习模型在此数据集上训练，性能优于现有最先进方法，在准确率和覆盖范围上均实现突破。在统一数据集上达到99%的检测准确率，较仅处理14种作物26种病害的现有方法高出7个百分点。通过扩展可检测作物与病害种类，本方案旨在为印度农户提供更优解决方案。
原文摘要 · Abstract (English)
India, as a predominantly agrarian economy, faces significant challenges in agriculture, including substantial crop losses caused by diseases, pests, and environmental stress. Early detection and accurate identification of diseases across different crops are critical for improving yield and ensuring food security. This paper proposes a deep learning based solution for detecting multiple diseases in multiple crops, aimed to cover India's diverse agricultural landscape. We first create a unified dataset encompassing images of 17 different crops and 34 different diseases from various available repositories. Proposed deep learning model is trained on this dataset and outperforms the state-of-the-art in terms of accuracy and the number of crops, diseases covered. We achieve a significant detection accuracy, i.e., 99 percent for our unified dataset which is 7 percent more when compared to state-of-the-art handling 14 crops and 26 different diseases only. By improving the number of crops and types of diseases that can be detected, proposed solution aims to provide a better product for Indian farmers.
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