Automatic Detection of Rice Disease in Images of Various Leaf Sizes

TL;DR

This paper presents a computer vision method combining CNN-based leaf width estimation and image tiling to improve rice disease detection accuracy across various leaf sizes in field images.

Contribution

It introduces a novel image tiling approach based on automatic leaf width estimation to enhance CNN-based rice disease detection in real-world conditions.

Findings

Leaf width prediction achieved 11.18% MAPE.

Disease detection mAP improved from 87.56% to 91.14%.

Technique enhances detection efficiency in variable-sized leaf images.

Abstract

Fast, accurate and affordable rice disease detection method is required to assist rice farmers tackling equipment and expertise shortages problems. In this paper, we focused on the solution using computer vision technique to detect rice diseases from rice field photograph images. Dealing with images took in real-usage situation by general farmers is quite challenging due to various environmental factors, and rice leaf object size variation is one major factor caused performance gradation. To solve this problem, we presented a technique combining a CNN object detection with image tiling technique, based on automatically estimated width size of rice leaves in the images as a size reference for dividing the original input image. A model to estimate leaf width was created by small size CNN such as 18 layer ResNet architecture model. A new divided tiled sub-image set with uniformly sized object was generated and used as input for training a rice disease prediction model. Our technique was evaluated on 4,960 images of eight different types of rice leaf diseases, including blast, blight, brown spot, narrow brown spot, orange, red stripe, rice grassy stunt virus, and streak disease. The mean absolute percentage error (MAPE) for leaf width prediction task evaluated on all eight classes was 11.18% in the experiment, indicating that the leaf width prediction model performed well. The mean average precision (mAP) of the prediction performance on YOLOv4 architecture was enhanced from 87.56% to 91.14% when trained and tested with the tiled dataset. According to our study, the proposed image tiling technique improved rice disease detection efficiency.