Abstract Accurate and timely detection of diseases and pests in rice plants can help farmers in applying timely treatment on the plants and thereby can reduce the economic losses substantially. Recent developments in deep learning-based convolutional neural networks (CNN) have greatly improved image classification accuracy. Being motivated by the success of CNNs in image classification, deep learning-based approaches have been developed in this paper for detecting diseases and pests from rice plant images. The contribution of this paper is two fold: (i) State-of-the-art large scale architectures such as VGG16 and InceptionV3 have been adopted and fine tuned for detecting and recognising rice diseases and pests. Experimental results show the effectiveness of these models with real datasets. (ii) Since large scale architectures are not suitable for mobile devices, a two-stage small CNN architecture has been proposed, and compared with the state-of-the-art memory efficient CNN architectures such as MobileNet, NasNet Mobile and SqueezeNet. Experimental results show that the proposed architecture can achieve the desired accuracy of 93.3% with a significantly reduced model size (e.g., 99% smaller than VGG16).

中文翻译：

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基于卷积神经网络的水稻病虫害识别与识别

摘要 准确及时地发现水稻病虫害，可以帮助农民及时对水稻进行防治，从而大大减少经济损失。基于深度学习的卷积神经网络 (CNN) 的最新发展极大地提高了图像分类的准确性。受 CNN 在图像分类方面的成功启发，本文开发了基于深度学习的方法，用于从水稻植株图像中检测病虫害。本文的贡献有两个方面：(i) 采用并微调了最先进的大规模架构，如 VGG16 和 InceptionV3，用于检测和识别水稻病虫害。实验结果表明这些模型对真实数据集的有效性。(ii) 由于大规模架构不适合移动设备，因此提出了两阶段小型 CNN 架构，并与最先进的内存高效 CNN 架构（如 MobileNet、NasNet Mobile 和 SqueezeNet）进行比较。实验结果表明，所提出的架构可以在显着减小模型尺寸（例如，比 VGG16 小 99%）的情况下实现 93.3% 的期望精度。