EADD-YOLO: An efficient and accurate disease detector for apple leaf using improved lightweight YOLOv5

Zhu, Shisong; Ma, Wanli; Wang, Jianlong; Yang, Mo; Wang, Yongmao; Wang, Chunyang

doi:10.3389/fpls.2023.1120724

Cited by 9 publications

(3 citation statements)

References 38 publications

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“…Experimental outcomes indicated that the proposed method surpassed several recent DL approaches in crop disease identification, achieving an accuracy of 99.16% on the PlantVillage dataset. ( Zhu et al., 2023 ) introduced EADD-YOLO, a model for accurate and efficient apple leaf disease detection model based on YOLOv5. EADD-YOLO utilized the shufflenet inverted residual blocks in the backbone and utilizing depthwise convolution to propose an efficient feature learning module in the neck.…”

Section: Literature Reviewmentioning

confidence: 99%

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8

Uddin,

Mazumder,

Prity

et al. 2024

Front. Plant Sci.

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Cauliflower cultivation plays a pivotal role in the Indian Subcontinent’s winter cropping landscape, contributing significantly to both agricultural output, economy and public health. However, the susceptibility of cauliflower crops to various diseases poses a threat to productivity and quality. This paper presents a novel machine vision approach employing a modified YOLOv8 model called Cauli-Det for automatic classification and localization of cauliflower diseases. The proposed system utilizes images captured through smartphones and hand-held devices, employing a finetuned pre-trained YOLOv8 architecture for disease-affected region detection and extracting spatial features for disease localization and classification. Three common cauliflower diseases, namely ‘Bacterial Soft Rot’, ‘Downey Mildew’ and ‘Black Rot’ are identified in a dataset of 656 images. Evaluation of different modification and training methods reveals the proposed custom YOLOv8 model achieves a precision, recall and mean average precision (mAP) of 93.2%, 82.6% and 91.1% on the test dataset respectively, showcasing the potential of this technology to empower cauliflower farmers with a timely and efficient tool for disease management, thereby enhancing overall agricultural productivity and sustainability

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Section: Literature Reviewmentioning

confidence: 99%

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8

Uddin,

Mazumder,

Prity

et al. 2024

Front. Plant Sci.

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“…In addition, new approaches such as transfer learning offer a way to overcome the typical challenges of training a deep learning algorithm (e.g., high variance, low accuracy or bias) and allow an easy adaption of pretrained models to a specific dataset [14]. This makes it quick and easy to establish models for a customised dataset that achieve a high level of precision as demonstrated in studies on rice leaf diseases [15,16], grape leaf lesions [17] and apple leaf diseases [18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

YOLO-Based Phenotyping of Apple Blotch Disease (Diplocarpon coronariae) in Genetic Resources after Artificial Inoculation

Reim,

Richter,

Leonhardt

et al. 2024

Agronomy

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Phenotyping of genetic resources is an important prerequisite for the selection of resistant varieties in breeding programs and research. Computer vision techniques have proven to be a useful tool for digital phenotyping of diseases of interest. One pathogen that is increasingly observed in Europe is Diplocarpon coronariae, which causes apple blotch disease. In this study, a high-throughput phenotyping method was established to evaluate genetic apple resources for susceptibility to D. coronariae. For this purpose, inoculation trials with D. coronariae were performed in a laboratory and images of infested leaves were taken 7, 9 and 13 days post inoculation. A pre-trained YOLOv5s model was chosen to establish the model, which was trained with an image dataset of 927 RGB images. The images had a size of 768 × 768 pixels and were divided into 738 annotated training images, 78 validation images and 111 background images without symptoms. The accuracy of symptom prediction with the trained model was 95%. These results indicate that our model can accurately and efficiently detect spots with acervuli on detached apple leaves. Object detection can therefore be used for digital phenotyping of detached leaf assays to assess the susceptibility to D. coronariae in a laboratory.

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“…The EADD-YOLO by improving lightweight YOLOv5 was presented. It reconstructs the backbone network with lightweight inverted residual modules and introduces them into the network to reduce feature extraction and fusion, thereby improving the efficiency of segmenting leaves ( Zhu et al., 2023b ). The above papers all focus on the segmentation of apple leaves and diseases from the lightweighting, and they perform well in real-time detection on mobile devices, but there may be shortcomings in disease segmentation.…”

Section: Introductionmentioning

confidence: 99%

RAAWC-UNet: an apple leaf and disease segmentation method based on residual attention and atrous spatial pyramid pooling improved UNet with weight compression loss

Wang,

Jia,

Zhang

et al. 2024

Front. Plant Sci.

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IntroductionEarly detection of leaf diseases is necessary to control the spread of plant diseases, and one of the important steps is the segmentation of leaf and disease images. The uneven light and leaf overlap in complex situations make segmentation of leaves and diseases quite difficult. Moreover, the significant differences in ratios of leaf and disease pixels results in a challenge in identifying diseases.MethodsTo solve the above issues, the residual attention mechanism combined with atrous spatial pyramid pooling and weight compression loss of UNet is proposed, which is named RAAWC-UNet. Firstly, weights compression loss is a method that introduces a modulation factor in front of the cross-entropy loss, aiming at solving the problem of the imbalance between foreground and background pixels. Secondly, the residual network and the convolutional block attention module are combined to form Res_CBAM. It can accurately localize pixels at the edge of the disease and alleviate the vanishing of gradient and semantic information from downsampling. Finally, in the last layer of downsampling, the atrous spatial pyramid pooling is used instead of two convolutions to solve the problem of insufficient spatial context information.ResultsThe experimental results show that the proposed RAAWC-UNet increases the intersection over union in leaf and disease segmentation by 1.91% and 5.61%, and the pixel accuracy of disease by 4.65% compared with UNet.DiscussionThe effectiveness of the proposed method was further verified by the better results in comparison with deep learning methods with similar network architectures.

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EADD-YOLO: An efficient and accurate disease detector for apple leaf using improved lightweight YOLOv5

Cited by 9 publications

References 38 publications

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8

YOLO-Based Phenotyping of Apple Blotch Disease (Diplocarpon coronariae) in Genetic Resources after Artificial Inoculation

RAAWC-UNet: an apple leaf and disease segmentation method based on residual attention and atrous spatial pyramid pooling improved UNet with weight compression loss

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