scholarly journals Leaf Spot Attention Networks Based on Spot Feature Encoding for Leaf Disease Identification and Detection

2021 ◽  
Vol 11 (17) ◽  
pp. 7960
Author(s):  
Chang-Hwan Son
Keyword(s):  
Feature Extraction ◽  
Object Detection ◽  
Leaf Spot ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Disease Detection ◽  
Discriminative Power ◽  
Leaf Disease ◽  
Feature Encoding ◽  
Feature Extractor

This study proposes a new attention-enhanced YOLO model that incorporates a leaf spot attention mechanism based on regions-of-interest (ROI) feature extraction into the YOLO framework for leaf disease detection. Inspired by a previous study, which revealed that leaf spot attention based on the ROI-aware feature extraction can improve leaf disease recognition accuracy significantly and outperform state-of-the-art deep learning models, this study extends the leaf spot attention model to leaf disease detection. The primary idea is that spot areas indicating leaf diseases appear only in leaves, whereas the background area does not contain useful information regarding leaf diseases. To increase the discriminative power of the feature extractor that is required in the object detection framework, it is essential to extract informative and discriminative features from the spot and leaf areas. To realize this, a new ROI-aware feature extractor, that is, a spot feature extractor was designed. To divide the leaf image into spot, leaf, and background areas, the leaf segmentation module was first pretrained, and then spot feature encoding was applied to encode spot information. Next, the ROI-aware feature extractor was connected to an ROI-aware feature fusion layer to model the leaf spot attention mechanism, and to be joined with the YOLO detection subnetwork. The experimental results confirm that the proposed ROI-aware feature extractor can improve leaf disease detection by boosting the discriminative power of the spot features. In addition, the proposed attention-enhanced YOLO model outperforms conventional state-of-the-art object detection models.

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

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 474
Author(s):  
Jun Wang ◽  
Liya Yu ◽  
Jing Yang ◽  
Hao Dong
Keyword(s):  
Feature Extraction ◽  
Target Detection ◽  
Attention Mechanism ◽  
Detection Methods ◽  
Disease Detection ◽  
Plant Leaves ◽  
Plant Leaf ◽  
Detection Algorithms ◽  
Leaf Disease ◽  
Image Dataset

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.

scholarly journals A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification

2021 ◽  
Vol 13 (10) ◽  
pp. 1950
Author(s):  
Cuiping Shi ◽  
Xin Zhao ◽  
Liguo Wang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
State Of The Art ◽  
Rapid Development ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Scene Classification

In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.

scholarly journals DETECTION OF PLANT LEAF DISEASES IN AGRICULTURE USING RECENT IMAGE PROCESSING TECHNIQUES – A REVIEW

2019 ◽  
Vol 7 (1) ◽  
pp. 29-33
Author(s):  
Arpan Singh Rajput ◽  
Shailja Shukla ◽  
S. S. Thakur
Keyword(s):  
Image Processing ◽  
State Of The Art ◽  
Disease Classification ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Processing Techniques ◽  
Large Work ◽  
Respective Product ◽  
Automatic Technique

Purpose: Agricultural productivity is something on which the economy highly depends in India as well in all over the world. India is an agriculture-dependent country; wherein about 70% of the population depends on agriculture. Methodology: This is one of the main reasons that disease detection in agriculture plays an important role, as having the disease in plant leaf is quite natural. If proper observations are not taken in the agriculture field then it causes serious effects on plants due to which respective product quality and productivity are affected. Detection of plant leaf disease through effective and accurate automatic technique is beneficial at the starting stage as it reduces a large work of monitoring in big farms of crops. Result: This paper presents the review on the state of the art disease classification techniques presently used using image processing that can be used for plant leaf disease detection in agriculture.

scholarly journals Road Object Detection of YOLO Algorithm with Attention Mechanism

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Jiacheng Li ◽  
◽  
Huazhang Wang ◽  
Yuan Xu ◽  
Fan Liu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Object Detection ◽  
Spatial Attention ◽  
High Precision ◽  
Detection Algorithm ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Automatic Driving ◽  
Key Features ◽  
Detection Effect

In auto-driving cars, incorrect object detection can lead to serious accidents, so high-precision object detection is the key to automatic driving. This paper improves on the YOLOv3 object detection algorithm, and introduces the channel attention mechanism and spatial attention mechanism into the feature extraction network, which is used to autonomously learn the weight of each channel, enhance key features, and suppress redundant features. Experimental results show that the detection effect of the improved network algorithm is significantly higher than that of the YOLOv3 algorithm.

scholarly journals Identification of Leaf Disease Using Image Processing Technique

2021 ◽  
pp. 881-885
Author(s):  
Ankit Wagh ◽  
Ritwik Chumble ◽  
Santosh Kangane ◽  
Pranav Bakare
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Plant Disease ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Disease Detection ◽  
Revolutionary Change ◽  
Leaf Disease ◽  
Important Time

The increased availability of smartphones has made it easier for taking technology to every individual. Technology can play important role in the field of agriculture to improve outcomes. Plant disease is one of the important reasons for the decrement of yield. Bridging technology with agriculture can give revolutionary change. It is challenging to monitor the disease of plants manually. It consumes important time, resources, and need efforts. Hence faster image processing technique is used as a solution for it. Disease detection using image processing involves multiple steps like the acquisition of images, pre-processing, segmentation, feature extraction, and classification. This paper discusses a faster image processing technique using the KNN method. This algorithm gives an asymptotically faster method for image processing.

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