Disease Detection and Identification of Rice Leaf Based on Improved Detection Transformer

In recent years, the domain of diagnosing plant afflictions has predominantly relied upon the utilization of deep learning techniques for classifying images of diseased specimens; however, these classification algorithms remain insufficient for instances where a single plant exhibits multiple ailmen...

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Main Authors: Hua Yang, Xingquan Deng, Hao Shen, Qingfeng Lei, Shuxiang Zhang, Neng Liu
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:Agriculture
Subjects:
Online Access:https://www.mdpi.com/2077-0472/13/7/1361
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author Hua Yang
Xingquan Deng
Hao Shen
Qingfeng Lei
Shuxiang Zhang
Neng Liu
author_facet Hua Yang
Xingquan Deng
Hao Shen
Qingfeng Lei
Shuxiang Zhang
Neng Liu
author_sort Hua Yang
collection DOAJ
description In recent years, the domain of diagnosing plant afflictions has predominantly relied upon the utilization of deep learning techniques for classifying images of diseased specimens; however, these classification algorithms remain insufficient for instances where a single plant exhibits multiple ailments. Consequently, we view the region afflicted by the malady of rice leaves as a minuscule issue of target detection, and then avail ourselves of a computational approach to vision to identify the affected area. In this paper, we advance a proposal for a Dense Higher-Level Composition Feature Pyramid Network (DHLC-FPN) that is integrated into the Detection Transformer (DETR) algorithm, thereby proffering a novel Dense Higher-Level Composition Detection Transformer (DHLC-DETR) methodology which can effectively detect three diseases: sheath blight, rice blast, and flax spot. Initially, the proposed DHLC-FPN is utilized to supersede the backbone network of DETR through amalgamation with Res2Net, thus forming a feature extraction network. Res2Net then extracts five feature scales, which are coalesced through the deployment of high-density rank hybrid sampling by the DHLC-FPN architecture. The fused features, in concert with the location encoding, are then fed into the transformer to produce predictions of classes and prediction boxes. Lastly, the prediction classes and the prediction boxes are subjected to binary matching through the application of the Hungarian algorithm. On the IDADP datasets, the DHLC-DETR model, through the utilization of data enhancement, elevated mean Average Precision (mAP) by 17.3% in comparison to the DETR model. Additionally, mAP for small target detection was improved by 9.5%, and the magnitude of hyperparameters was reduced by 324.9 M. The empirical outcomes demonstrate that the optimized structure for feature extraction can significantly enhance the average detection accuracy and small target detection accuracy of the model, achieving an average accuracy of 97.44% on the IDADP rice disease dataset.
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spelling doaj.art-2d93385818104e71ba137874ca493fcc2023-11-18T17:52:44ZengMDPI AGAgriculture2077-04722023-07-01137136110.3390/agriculture13071361Disease Detection and Identification of Rice Leaf Based on Improved Detection TransformerHua Yang0Xingquan Deng1Hao Shen2Qingfeng Lei3Shuxiang Zhang4Neng Liu5School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430040, ChinaSchool of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430040, ChinaBaijuncheng Technology Co., Ltd., Wuhan 434000, ChinaSchool of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430040, ChinaSchool of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430040, ChinaSchool of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430040, ChinaIn recent years, the domain of diagnosing plant afflictions has predominantly relied upon the utilization of deep learning techniques for classifying images of diseased specimens; however, these classification algorithms remain insufficient for instances where a single plant exhibits multiple ailments. Consequently, we view the region afflicted by the malady of rice leaves as a minuscule issue of target detection, and then avail ourselves of a computational approach to vision to identify the affected area. In this paper, we advance a proposal for a Dense Higher-Level Composition Feature Pyramid Network (DHLC-FPN) that is integrated into the Detection Transformer (DETR) algorithm, thereby proffering a novel Dense Higher-Level Composition Detection Transformer (DHLC-DETR) methodology which can effectively detect three diseases: sheath blight, rice blast, and flax spot. Initially, the proposed DHLC-FPN is utilized to supersede the backbone network of DETR through amalgamation with Res2Net, thus forming a feature extraction network. Res2Net then extracts five feature scales, which are coalesced through the deployment of high-density rank hybrid sampling by the DHLC-FPN architecture. The fused features, in concert with the location encoding, are then fed into the transformer to produce predictions of classes and prediction boxes. Lastly, the prediction classes and the prediction boxes are subjected to binary matching through the application of the Hungarian algorithm. On the IDADP datasets, the DHLC-DETR model, through the utilization of data enhancement, elevated mean Average Precision (mAP) by 17.3% in comparison to the DETR model. Additionally, mAP for small target detection was improved by 9.5%, and the magnitude of hyperparameters was reduced by 324.9 M. The empirical outcomes demonstrate that the optimized structure for feature extraction can significantly enhance the average detection accuracy and small target detection accuracy of the model, achieving an average accuracy of 97.44% on the IDADP rice disease dataset.https://www.mdpi.com/2077-0472/13/7/1361rice leaf disease diagnosisobject detectionDETRdeep learningimage processing
spellingShingle Hua Yang
Xingquan Deng
Hao Shen
Qingfeng Lei
Shuxiang Zhang
Neng Liu
Disease Detection and Identification of Rice Leaf Based on Improved Detection Transformer
Agriculture
rice leaf disease diagnosis
object detection
DETR
deep learning
image processing
title Disease Detection and Identification of Rice Leaf Based on Improved Detection Transformer
title_full Disease Detection and Identification of Rice Leaf Based on Improved Detection Transformer
title_fullStr Disease Detection and Identification of Rice Leaf Based on Improved Detection Transformer
title_full_unstemmed Disease Detection and Identification of Rice Leaf Based on Improved Detection Transformer
title_short Disease Detection and Identification of Rice Leaf Based on Improved Detection Transformer
title_sort disease detection and identification of rice leaf based on improved detection transformer
topic rice leaf disease diagnosis
object detection
DETR
deep learning
image processing
url https://www.mdpi.com/2077-0472/13/7/1361
work_keys_str_mv AT huayang diseasedetectionandidentificationofriceleafbasedonimproveddetectiontransformer
AT xingquandeng diseasedetectionandidentificationofriceleafbasedonimproveddetectiontransformer
AT haoshen diseasedetectionandidentificationofriceleafbasedonimproveddetectiontransformer
AT qingfenglei diseasedetectionandidentificationofriceleafbasedonimproveddetectiontransformer
AT shuxiangzhang diseasedetectionandidentificationofriceleafbasedonimproveddetectiontransformer
AT nengliu diseasedetectionandidentificationofriceleafbasedonimproveddetectiontransformer