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

## TL;DR

This paper develops deep learning models, including fine-tuned large CNNs and a new small CNN architecture, for accurate detection of rice diseases and pests, balancing high accuracy with mobile device suitability.

## Contribution

It adapts large CNN architectures for rice disease detection and proposes a compact CNN model optimized for mobile devices, demonstrating high accuracy and reduced model size.

## Key findings

- VGG16 and InceptionV3 achieved high accuracy on rice datasets.
- The proposed small CNN attained 93.3% accuracy with 99% less size.
- The small CNN outperforms other lightweight architectures in efficiency.

## Abstract

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).