RETRACTED: A Hybrid Approach for Plant Leaf Disease Detection and Classification Using Digital Image Processing Methods

Rao, Anusha; Kulkarni, S.B.

doi:10.1177/0020720920953126

Cited by 40 publications

(16 citation statements)

References 27 publications

(31 reference statements)

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“…The performance is compared, and the result shows that the classification accuracy of the support vector machine classifier is up to 94.65%. Literature [24] introduced a hybrid method for detecting plant leaf disease. The first stage corresponds to the image enhancement and image conversion scheme to overcome the problems related to low illumination and noise.…”

Section: Related Workmentioning

confidence: 99%

DBA_SSD: A Novel End-to-End Object Detection Algorithm Applied to Plant Disease Detection

Wang

Yang

et al. 2021

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In response to the difficulty of plant leaf disease detection and classification, this study proposes a novel plant leaf disease detection method called deep block attention SSD (DBA_SSD) for disease identification and disease degree classification of plant leaves. We propose three plant leaf detection methods, namely, squeeze-and-excitation SSD (Se_SSD), deep block SSD (DB_SSD), and DBA_SSD. Se_SSD fuses SSD feature extraction network and attention mechanism channel, DB_SSD improves VGG feature extraction network, and DBA_SSD fuses the improved VGG network and channel attention mechanism. To reduce the training time and accelerate the training process, the convolutional layers trained in the Image Net image dataset by the VGG model are migrated to this model, whereas the collected plant leaves disease image dataset is randomly divided into training set, validation set, and test set in the ratio of 8:1:1. We chose the PlantVillage dataset after careful consideration because it contains images related to the domain of interest. This dataset consists of images of 14 plants, including images of apples, tomatoes, strawberries, peppers, and potatoes, as well as the leaves of other plants. In addition, data enhancement methods, such as histogram equalization and horizontal flip were used to expand the image data. The performance of the three improved algorithms is compared and analyzed in the same environment and with the classical target detection algorithms YOLOv4, YOLOv3, Faster RCNN, and YOLOv4 tiny. Experiments show that DBA_SSD outperforms the two other improved algorithms, and its performance in comparative analysis is superior to other target detection algorithms.

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Section: Related Workmentioning

confidence: 99%

DBA_SSD: A Novel End-to-End Object Detection Algorithm Applied to Plant Disease Detection

Wang

Yang

et al. 2021

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“…Literature [32] introduced a hybrid method for detecting plant leaf diseases and insect pests. The first stage corresponds to the image enhancement and image conversion scheme to overcome the problems related to low illumination and noise.…”

Section: Related Workmentioning

confidence: 99%

DBA_SSD ： A Novel End-to-End Object Detection Using Deep Attention Module for Helping Smart Device with Vegetable and Fruit Leaf Plant Disease Detection

Wang¹,

Yang²,

Yu³

et al. 2021

Preprint

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In response to the difficulty of detecting and classifying pests and vegetable and fruit leaves with pests and diseases, this study proposes a novel vegetable and fruit leaf pest detection method called deep block attention SSD (DBA_SSD) for the identification of pests and diseases and classification of the degree of pests and diseases of vegetable and fruit leaves. We propose three vegetable and fruit leaf pest detection methods, namely, squeeze-and excitation SSD (Se_SSD), DB_SSD, and DBA_SSD. Se_SSD fuses SSD feature extraction network and attention mechanism channel, DB_SSD improves VGG feature extraction network, and DBA_SSD fuses the improved VGG network and channel attention mechanism. To reduce the training time and accelerate the training process, the convolutional layers trained in the Image Net image dataset by the VGG model are migrated to this model, whereas the collected vegetable and fruit disease image dataset is randomly divided into training set, validation set, and test set in the ratio of 8:1:1. In addition, data enhancement methods, such as histogram equalization and horizontal flip were used to expand the image data. The performance of the three improved algorithms is compared and analyzed in the same environment and with the classical target detection algorithms YOLOv4, YOLOv3, Faster RCNN, and YOLOv4 tiny. Experiments show that DBA_SSD outperforms the two other improved algorithms, and its performance in comparative analysis is superior to other target detection algorithms.

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“…Plant N acquisition can be improved in the presence of N2-fixing symbiotic and associative symbiotic bacteria and arbuscular mycorrhizal fungi (AMF). [14] ML based techniques have achieved a great attraction in digital image processing and prediction. Tough there are various challenging technologies this paper has come with hybrid approach by enhancing image, conversion of image and removing noise and applying GLCM technique and finally neuro-fuzzy logic classifier is used to train the model and extract features.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Decision Support System Framework for Leaf Image Analysis to Improve Crop Productivity

Meeradevi¹,

Mundada²

2021

IJACSA

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Crop disease is one of the major problems with agriculture in India. Identifying the disease and classifying the type of disease is most important which can be made possible using the deep learning technique. To perform this verified dataset is required which consists of healthy and disease leaf images of all crops. The proposed model uses a hybrid approach which integrates VGG16 classifier with an attention mechanism, transfer learning approach and dropout operation. The proposed model uses a rice disease dataset and us i ng the proposed approach it achieves an train accuracy of 96.45 percent and train loss 0.09 and validation loss of 0.44. The dataset is collected from the plant village project for rice leaf which consists of 4955 images which include Brown Spot, Healthy, Hipsa, and Leaf Blast type of images. The proposed model use attention mechanism that focuses mainly on the part of the image rather than the whole part of the image using a glimpse ratio of 3:1. The traditional method of detecting crop diseases needs high experience and knowledge of experts in the field which is time consuming, ineffective, and high cost. In this study, Deep Convolutional Neural Networks (DCNN) and Transfer Learning with Attention models are used to detect diseases associated with rice plants without overfitting the model.

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RETRACTED: A Hybrid Approach for Plant Leaf Disease Detection and Classification Using Digital Image Processing Methods

Cited by 40 publications

References 27 publications

DBA_SSD: A Novel End-to-End Object Detection Algorithm Applied to Plant Disease Detection

DBA_SSD: A Novel End-to-End Object Detection Algorithm Applied to Plant Disease Detection

DBA_SSD ： A Novel End-to-End Object Detection Using Deep Attention Module for Helping Smart Device with Vegetable and Fruit Leaf Plant Disease Detection

Hybrid Decision Support System Framework for Leaf Image Analysis to Improve Crop Productivity

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