Rice Leaf Disease Recognition using Local Threshold Based Segmentation and Deep CNN

Abstract

Timely detection of rice diseases can help farmers to take necessary action and thus reducing the yield loss substantially. Automatic recognition of rice diseases from the rice leaf images using computer vision and machine learning can be beneficial over the manual method of disease recognition through visual inspection. During the recent years, deep learning, a very popular and efficient machine learning algorithm, has shown great promise in image classification task. In this paper, a segmentation-based method using deep neural network for classifying rice diseases from leaf images has been proposed. Disease-affected regions of the rice leaves have been segmented using local segmentation method and the Convolutional Neural Network (CNN) has been trained with those images. Proposed method has been applied on three different datasets including the one created by us which consists of the rice leaf images collected from Bangladesh Rice Research Institute (BRRI). Three state-of-the-art CNN architectures VGG, ResNet and DenseNet, used in the proposed method, have been trained with these three datasets for classifying the diseases. Classification performance of the proposed method using the said three CNN architectures for the three datasets have been analyzed and compared. These results show that this model is quite promising in classifying rice leaf diseases. Outcome of this research is an enhancement in the performance of rice disease classification which is quite significant for the viability of this work to be transformed into a real-time application for the farmers.

Rice leaf disease detection based on enhanced feature fusion and target adaptation

“…Islam et al. (2021) made a count of CNN networks used for rice disease detection, and the top ranked networks were VGG networks, ResNet and Inception, with the disease characteristics of interest being texture, shape and colour. In this paper, rice disease images were enhanced and image samples were refined in terms of texture and features through operations such as conversion of colour channels.…”

Machine Learning and Image Processing Techniques for Rice Disease Detection: A Critical Analysis

“…In order to remove the background, suggestions are made for segmentation and background removal techniques. Color segmentation is performed using green pixel masks, followed by the application of Otsu thresholding methods to diseased images [73]. CNN utilizes characteristics, such as similarities, contradictions, energy, and clusters, to define shadows and their branches, enabling the determination of the forms of disease.…”

Convolutional neural network in rice disease recognition: accuracy, speed and lightweight

“…(Zhang et al, 2020) under the terms of the Creative Commons CC-BY license). Ning et al 10.3389/fpls.2023.1269371 and pooling layer map the input data to the feature representation space, and then the fully connected layer maps the feature representation space to the sample's label space to achieve the final classification or regression task (Islam et al, 2021).…”