Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases

Zhu, Ruilin; H, Zou; Li, Zhenye; Ni, Ruitao

doi:10.3390/plants12010169

Cited by 14 publications

(5 citation statements)

References 54 publications

(57 reference statements)

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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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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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“…Additional research has delved into the branching and fruiting patterns in apple trees (5) (6). More recent investigations have centered on employing imagery for automating agricultural tasks such as pruning (7) and disease detection (8). This work aims to build upon existing research efforts and advance the development of decision support tools for growers.…”

Section: Introductionmentioning

confidence: 99%

Characterization of the length and the distance of insertion of the branches of apple trees

Bounsir,

Ibriz,

Zegoumou

et al. 2024

E3S Web Conf.

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The current study focused on the branches Cherry Gala variety of apple in Azrouregion.The objective is to characterize the length and the insertion distance of different structures of apple trees based on nondestructive and exhaustive measurements of the branches. In total 2982 branches were identified and measured in the two stages (stage 1, stage 2) on different 6 levels present at each stage known as A, B, C, D, E, and F. The length of the branches in each stage and level was classified into 1 to 5 homogeneous classes. The length of vegetative branch variation was not significant between stages 1 and 2 but the insertion distance was significant for level D and not for levels A, B, and C. For five different length structures bearing fruit, the variation was significantly different for B, C, and D levels between stages 1 and 2 (length of fruiting spurs/ Dard/ Bourse; insertion distance of Dard and Bourse). The analysis showed that the length of the branches decreases while passing from one first stage to the second. The average branch length for stage 1 is 4,84 cm against 4,05 cm for stage 2. In each stage, the length decreases progressively while passing from one level to a higher level, except for level E of stage 1 and levels E and F of stage 2.

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“…The research's target plants are the fruit above crops because of their high production and consumption rates. Manual disease identification takes a lot of time and resources, including people, knowledge of fruit crops, and agricultural equipment [8]. Furthermore, it is impossible to accurately carry out the classification work in the real-time environment or the field areas due to the complexity of disease symptoms and the similarity of different diseases [9].…”

Section: Introductionmentioning

confidence: 99%

An Artificial Intelligence-Based Framework for Fruits Disease Recognition Using Deep Learning

Haider,

Khan,

Nazir

et al. 2024

CSSE

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Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been employed in this work for the validation process, such as apple, grape, and Citrus. In the first step, a noisy dataset is prepared by employing the original images to learn the designed framework better. The EfficientNet-B0 deep model is fine-tuned on the next step and trained separately on the original and noisy data. After that, features are fused using a serial concatenation approach that is later optimized in the next step using an improved Path Finder Algorithm (PFA). This algorithm aims to select the best features based on the fitness score and ignore redundant information. The selected features are finally classified using machine learning classifiers such as Medium Neural Network, Wide Neural Network, and Support Vector Machine. The experimental process was conducted on each fruit dataset separately and obtained an accuracy of 100%, 99.7%, 99.7%, and 93.4% for apple, grape, Citrus fruit, and citrus plant leaves, respectively. A detailed analysis is conducted and also compared with the recent techniques, and the proposed framework shows improved accuracy.

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Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases

Cited by 14 publications

References 54 publications

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

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

Characterization of the length and the distance of insertion of the branches of apple trees

An Artificial Intelligence-Based Framework for Fruits Disease Recognition Using Deep Learning

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