Identification and recognition of rice diseases and pests using convolutional neural networks

• Published in: Biosystems engineering Vol. 194; pp. 112 - 120
• Main Authors: Rahman, Chowdhury R., Arko, Preetom S., Ali, Mohammed E., Iqbal Khan, Mohammad A., Apon, Sajid H., Nowrin, Farzana, Wasif, Abu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2020
• Subjects: Convolutional neural network; Dataset; Memory efficient; Pest; Rice disease; Two stage training; Pest; Memory efficient; Two stage training; Rice disease; Convolutional neural network; Dataset
• ISSN: 1537-5110; 1537-5129
• DOI: 10.1016/j.biosystemseng.2020.03.020

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). •Rice disease dataset (1426 images, nine classes) collected in real life scenario.•Three different training methods compared on state-of-the-art CNN architectures.•Two stage training concept implemented on memory efficient Simple CNN.•Simple CNN performance comparison with state-of-the-art memory efficient CNNs.