Comparative Study Of Deep Learning Algorithms For Disease And Pest Detection In Rice Crops

Burhan, Syeda Airas; Minhas, Sidra; Tariq, Amara; Hassan, Muhammad Nabeel

doi:10.1109/ecai50035.2020.9223239

Cited by 37 publications

(13 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many different studies have been done specifically on rice leaves; for example, [1] used an artificial neural network (ANN) to detect the symptoms of rice blast disease in rice plants. Burhan et al [2] used five different models (VGG16, VGG19, ResNet50, ResNet101V2, and ResNet50V2) and compared their performance levels. The models tested on both artificial data as well as real images that were collected from fields.…”

Section: Literature Surveymentioning

confidence: 99%

Plant Disease Detection using Ensembled CNN Framework

Mondal¹,

Banerjee²,

Mukherjee³

et al. 2022

csci

View full text Add to dashboard Cite

Agriculture exhibits the prime driving force for growth of agro-based economies globally. In the field of agriculture, detecting and preventing crops from attacks of pests is the major concern in today's world. Early detection of plant disease becomes necessary to prevent the degradation in the yield of crop production. In this paper, we propose an ensemble based Convolutional Neural Network (CNN) architecture that detects plant disease from the images of the leaves of the plant. The proposed architecture takes into account CNN architectures like VGG-19, ResNet-50, and InceptionV3 as its base models, and the prediction from these models is used as an input for our meta-model (Inception-ResNetV2). The approach helped us in building a generalized model for disease detection with an accuracy of 97.9 % under test conditions.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Plant Disease Detection using Ensembled CNN Framework

Mondal¹,

Banerjee²,

Mukherjee³

et al. 2022

csci

View full text Add to dashboard Cite

show abstract

“…Among the methods used to identify and classify rice pests and diseases, there are traditional multilayer convolutional neural networks (Lu et al, 2017 ) and the fine-tuning methods of VGG-16, Inception-V3, DenseNet, and so on, based on transfer learning (Burhan et al, 2020 ; Chen et al, 2020 , 2021 ; Mathulaprangsan et al, 2020 ). 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.…”

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%

“…Most of the researches on DL for rice pest and disease classification uses a convolutional neural network (CNN) based on transfer learning technology (Burhan et al, 2020 ; Chen et al, 2020 , 2021 ; Mathulaprangsan et al, 2020 ). Although these models have achieved a high level of accuracy in their respective studies, they rely mainly on two dataset features to achieve their results.…”

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

“…Alfarisy et al [17] collected 4511 training samples using a search engine and used CaffeNet [18] for rice pest identification. Burhan et al [19] comparatively studied the performances of five deep learning models (Vgg16, Vgg19, ResNet50, ResNet50V2 and ResNet101V2). Overall, these depth-feature-based efforts lack sufficient samples to optimize the large number of hyperparameters of CNNs.…”

Section: Introductionmentioning

confidence: 99%

Classification Method of Significant Rice Pests Based on Deep Learning

Jiang

Jia

et al. 2022

Agronomy

View full text Add to dashboard Cite

Rice pests are one of the main factors affecting rice yield. The accurate identification of pests facilitates timely preventive measures to avoid economic losses. Some existing open source datasets related to rice pest identification mostly include only a small number of samples, or suffer from inter-class and intra-class variance and data imbalance challenges, which limit the application of deep learning techniques in the field of rice pest identification. In this paper, based on the IP102 dataset, we first reorganized a large-scale dataset for rice pest identification by Web crawler technique and manual screening. This dataset was given the name IP_RicePests. Specifically, the dataset includes 8248 images belonging to 14 categories. The IP_RicePests dataset was then expanded to include 14,000 images via ARGAN data augmentation technique to address the difficulties in obtaining large samples of rice pests. Finally, the parameters trained on the public image ImageNet dataset using VGGNet, ResNet and MobileNet networks were used as the initial values of the target data training network to achieve image classification in the field of rice pests. The experimental results show that all three classification networks combined with transfer learning have good recognition accuracy, among which the highest classification accuracy can be obtained on the IP_RicePests dataset via fine-tuning the parameters of the VGG16 network. In addition, following ARGAN data augmentation the dataset demonstrates high accuracy improvements in all three models, and fine-tuning the VGG16 network parameters obtains the highest accuracy in the augmented IP_RicePests dataset. It is demonstrated that CNN combined with transfer learning can employ the ARGAN data augmentation technique to overcome difficulties in obtaining large sample sizes and improve the efficiency of rice pest identification. This study provides foundational data and technical support for rice pest identification.

show abstract

Comparative Study Of Deep Learning Algorithms For Disease And Pest Detection In Rice Crops

Cited by 37 publications

References 5 publications

Plant Disease Detection using Ensembled CNN Framework

Plant Disease Detection using Ensembled CNN Framework

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

Classification Method of Significant Rice Pests Based on Deep Learning

Contact Info

Product

Resources

About