# Identification and Recognition of Rice Diseases and Pests Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1812.01043 · HTML: https://mlsanity.com/paper/1812.01043/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2018.

## Built on by (most cited first, 6 of 6)

- [The Power of Transfer Learning in Agricultural Applications: AgriNet](https://mlsanity.com/paper/2207.03881/) (2022)
- [Rice Diseases Detection and Classification Using Attention Based Neural Network and Bayesian Optimization](https://mlsanity.com/paper/2201.00893/) (2022)
- [Automatic Detection of Rice Disease in Images of Various Leaf Sizes](https://mlsanity.com/paper/2206.07344/) (2022)
- [Rice paddy disease classifications using CNNs](https://mlsanity.com/paper/2303.08415/) (2023)
- [Neural Network-based Study for Rice Leaf Disease Recognition and Classification: A Comparative Analysis Between Feature-based Model and Direct Imaging Model](https://mlsanity.com/paper/2507.02322/) (2025)
- [N-EIoU-YOLOv9: A Signal-Aware Bounding Box Regression Loss for Lightweight Mobile Detection of Rice Leaf Diseases](https://mlsanity.com/paper/2601.09170/) (2026)

## Abstract (arXiv)

An 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 the 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 recognizing 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% less size compared to that of VGG16).