  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.
