Using Deep Learning Techniques to Detect Rice Diseases from Images of Rice Fields

Kiratiratanapruk, Kantip; Temniranrat, Pitchayagan; Kitvimonrat, Apichon; Sinthupinyo, Wasin; Patarapuwadol, Sujin

doi:10.1007/978-3-030-55789-8_20

Cited by 35 publications

(20 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was found that the ResNet-101 achieved the highest detection accuracy [71]. In the study of Kiratiratanapruk et al [72], Mask R-CNN provided higher performance than other models including Faster R-CNN and RetinaNet in detection of rice leaf diseases, but YOLOv3 achieved the highest accuracy. Although the YOLOv3 detector had higher computation efficiency, the Faster-RCNN outperformed YOLOv3 in apple detection [73].…”

Section: Discussionmentioning

confidence: 95%

Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

Zhang

Yang

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.

show abstract

Section: Discussionmentioning

confidence: 95%

Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

Zhang

Yang

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Mass loss of rice crops can be prevented if correct diagnoses of the diseases are done during the initial stage. Rice plant suffers from different biotic diseases like rice leaf blast, neck blast, steath blight, steath rot, bakanae, tungro, bacterial leaf blight, brown spot, narrow brown spot, stunt, leaf streak, [5] etc. In this paper, 4 different major rice leaf diseases including bacterial blight, blast, brown spot and tungro [6] are diagnosed.…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Diagnosis and Image Classification Using Deep Learning

Sharma¹,

Singh²,

Kavita³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Indian agriculture is striving to achieve sustainable intensification, the system aiming to increase agricultural yield per unit area without harming natural resources and the ecosystem. Modern farming employs technology to improve productivity. Early and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop productivity. Plant disease experts are not available in remote areas thus there is a requirement of automatic low-cost, approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert's opinion. Deep learning-based computer vision techniques like Convolutional Neural Network (CNN) and traditional machine learning-based image classification approaches are being applied to identify plant diseases. In this paper, the CNN model is proposed for the classification of rice and potato plant leaf diseases. Rice leaves are diagnosed with bacterial blight, blast, brown spot and tungro diseases. Potato leaf images are classified into three classes: healthy leaves, early blight and late blight diseases. Rice leaf dataset with 5932 images and 1500 potato leaf images are used in the study. The proposed CNN model was able to learn hidden patterns from the raw images and classify rice images with 99.58% accuracy and potato leaves with 97.66% accuracy. The results demonstrate that the proposed CNN model performed better when compared with other machine learning image classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree and Random Forest.

show abstract

“…In recent years, researchers have tended to use convolutional neural networks to solve the problem of identification and classification. Most of this research has been concerned with only a few rice disease or pest categories (Bhattacharya et al, 2020;Chen et al, 2020Chen et al, , 2021Kiratiratanapruk et al, 2020;Mathulaprangsan et al, 2020). Only Rahman et al (2020) studied simultaneously five categories of rice diseases and three categories of rice pests, but these are far from covering common rice pest and disease categories.…”

Section: Image Classification Of Rice Pests and Diseasesmentioning

confidence: 99%

“…There is also the direct use of the popular object detection algorithms Faster R-CNN, RetinaNet, YOLOv3, and Mask RCNN, either to experiment with rice pests and diseases or to optimize these algorithms before performing experiments. However, these object detection algorithms depend on the location of parts or related annotations (Kiratiratanapruk et al, 2020 ). A two-stage strategy has recently been developed to perform a more refined classification of rice pests and diseases (Bhattacharya et al, 2020 ; Rahman et al, 2020 ).…”

Section: Related Workmentioning

confidence: 99%

“…We know that the size of the dataset can have a significant impact on the accuracy level of the image classification, because only the use of large-scale datasets can improve the accuracy of any DL model (Hasan et al, 2020 ). Most previous studies use small-scale, roughly balanced rice pest and disease image datasets created under laboratory conditions (Bhattacharya et al, 2020 ; Burhan et al, 2020 ; Chen et al, 2020 , 2021 ; Kiratiratanapruk et al, 2020 ; Mathulaprangsan et al, 2020 ; Rahman et al, 2020 ). These datasets are used to emphasize or reveal the efficiency of the proposed method for diagnosing rice diseases and pests.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Convolutional Rebalancing Network for the Classification of Large Imbalanced Rice Pest and Disease Datasets in the Field

et al. 2021

View full text Add to dashboard Cite

The accurate classification of crop pests and diseases is essential for their prevention and control. However, datasets of pest and disease images collected in the field usually exhibit long-tailed distributions with heavy category imbalance, posing great challenges for a deep recognition and classification model. This paper proposes a novel convolutional rebalancing network to classify rice pests and diseases from image datasets collected in the field. To improve the classification performance, the proposed network includes a convolutional rebalancing module, an image augmentation module, and a feature fusion module. In the convolutional rebalancing module, instance-balanced sampling is used to extract features of the images in the rice pest and disease dataset, while reversed sampling is used to improve feature extraction of the categories with fewer images in the dataset. Building on the convolutional rebalancing module, we design an image augmentation module to augment the training data effectively. To further enhance the classification performance, a feature fusion module fuses the image features learned by the convolutional rebalancing module and ensures that the feature extraction of the imbalanced dataset is more comprehensive. Extensive experiments in the large-scale imbalanced dataset of rice pests and diseases (18,391 images), publicly available plant image datasets (Flavia, Swedish Leaf, and UCI Leaf) and pest image datasets (SMALL and IP102) verify the robustness of the proposed network, and the results demonstrate its superior performance over state-of-the-art methods, with an accuracy of 97.58% on rice pest and disease image dataset. We conclude that the proposed network can provide an important tool for the intelligent control of rice pests and diseases in the field.

show abstract

Using Deep Learning Techniques to Detect Rice Diseases from Images of Rice Fields

Cited by 35 publications

References 10 publications

Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

Plant Disease Diagnosis and Image Classification Using Deep Learning

Convolutional Rebalancing Network for the Classification of Large Imbalanced Rice Pest and Disease Datasets in the Field

Contact Info

Product

Resources

About