Identification of rice plant diseases using lightweight attention networks

Chen, Junde; Zhang, Defu; Zeb, Adnan; Nanehkaran, Yaser Ahangari

doi:10.1016/j.eswa.2020.114514

Cited by 125 publications

(44 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [ 56 ], authors presented disease prediction from Rice leaves using transfer learning based on InceptionResNetV2. Chen et al [ 57 ] also demonstrated the identification of Rice plant diseases using transfer learning. MobileNet-V2 was chosen for the backend, followed by an attention mechanism for learning inter-channel relationships.…”

Section: Comparative Analysismentioning

confidence: 99%

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Meena

Dhaka

Sinwar

et al. 2021

Sensors

212

View full text Add to dashboard Cite

In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.

show abstract

Section: Comparative Analysismentioning

confidence: 99%

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Meena

Dhaka

Sinwar

et al. 2021

Sensors

212

View full text Add to dashboard Cite

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%

“…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 2 more Smart Citations

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

Identification of Bacterial Diseases in Plants Using Re‐Trained Transfer Learning in Quantum Computing Environment

Padmanabhuni¹,

Reddy²,

Reddy

et al. 2023

Evolution and Applications of Quantum Computing

View full text Add to dashboard Cite

Identification of rice plant diseases using lightweight attention networks

Cited by 125 publications

References 37 publications

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

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

Identification of Bacterial Diseases in Plants Using Re‐Trained Transfer Learning in Quantum Computing Environment

Contact Info

Product

Resources

About