Meta‐learning shows great potential in plant disease recognition under few available samples

Wu, Xin; Deng, Hongyu; Wang, Qi; Liu, Liang; Gao, Yangyang; Hao, Ge‐Fei

doi:10.1111/tpj.16176

Cited by 13 publications

(4 citation statements)

References 91 publications

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“…According to some review papers [ 38 – 41 ] in plant disease recognition and some trends in computer vision, we collect more than 60 categories of commonly used pre-train model structures. We first compile a list of common model structures to find the best pre-trained models for plant disease diagnosis.…”

Section: Methodsmentioning

confidence: 99%

PDDD-PreTrain: A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis

et al. 2023

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Plant diseases threaten global food security by reducing crop yield; thus, diagnosing plant diseases is critical to agricultural production. Artificial intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming, costly, inefficient, and subjective disadvantages. As a mainstream AI method, deep learning has substantially improved plant disease detection and diagnosis for precision agriculture. In the meantime, most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased leaves. However, the commonly used pre-trained models are from the computer vision dataset, not the botany dataset, which barely provides the pre-trained models sufficient domain knowledge about plant disease. Furthermore, this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic precision. To address this issue, we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease diagnosis. In addition, we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification, plant disease detection, plant disease segmentation, and other subtasks. The extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time, thereby supporting the better diagnosis of plant diseases. In addition, our pre-trained models will be open-sourced at https://pd.samlab.cn/ and Zenodo platform https://doi.org/10.5281/zenodo.7856293 .

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Section: Methodsmentioning

confidence: 99%

PDDD-PreTrain: A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis

et al. 2023

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“…Different types of crops may require different transfer methods as well. Considering that some studies have proved that meta-learning can still maintain good recognition accuracy under small samples [34], we can introduce the idea of meta-learning to optimize the tea leaf disease detection model. For the problem of scarcity of tea leaf disease and pest datasets, we can use oversampling or replicating multiple copies [35] for the further enhancement of sample images.…”

Section: Discussionmentioning

confidence: 99%

A Small Target Tea Leaf Disease Detection Model Combined with Transfer Learning

Yao,

Lin,

Bai

et al. 2024

Forests

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Tea cultivation holds significant economic value, yet the leaves of tea plants are frequently susceptible to various pest and disease infestations. Consequently, there is a critical need for research focused on precisely and efficiently detecting these threats to tea crops. The investigation of a model capable of effectively identifying pests and diseases in tea plants is often hindered by challenges, such as limited datasets of pest and disease samples and the small size of detection targets. To address these issues, this study has chosen TLB, a common pest and disease in tea plants, as the primary research subject. The approach involves the application of transfer learning in conjunction with data augmentation as a fundamental methodology. This technique entails transferring knowledge acquired from a comprehensive source data domain to the model, aiming to mitigate the constraints of limited sample sizes. Additionally, to tackle the challenge of detecting small targets, this study incorporates the decoupling detection head TSCODE and integrates the Triplet Attention mechanism into the E-ELAN structure within the backbone to enhance the model’s focus on the TLB’s small targets and optimize detection accuracy. Furthermore, the model’s loss function is optimized based on the Wasserstein distance measure to mitigate issues related to sensitivity in localizing small targets. Experimental results demonstrate that, in comparison to the conventional YOLOv7 tiny model, the proposed model exhibits superior performance on the TLB small sample dataset, with precision increasing by 6.5% to 92.2%, recall by 4.5% to 86.6%, and average precision by 5.8% to 91.5%. This research offers an effective solution for identifying tea pests and diseases, presenting a novel approach to developing a model for detecting such threats in tea cultivation.

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Section: Introductionmentioning

confidence: 99%

“…Specifically, the YOLO (You Only Look Once) series of first-order target detection algorithms have demonstrated noteworthy success in image processing and computer vision tasks. Furthermore, these algorithms have found extensive applications in the agricultural domain (Akkem et al, 2023;Farjon et al, 2023;Wu et al, 2023). Jintao Feng et al (2023) proposed a YOLOX-based real-time multitype surface defect detection algorithm (MSDD-YOLOX) for oranges in order to achieve real-time detection of orange surface defects on an orange sorter.…”

Section: Introductionmentioning

confidence: 99%

Fruits hidden by green: an improved YOLOV8n for detection of young citrus in lush citrus trees

Ang,

Zhiwei,

Wei

et al. 2024

Front. Plant Sci.

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In order to address the challenges of inefficiency and insufficient accuracy in the manual identification of young citrus fruits during thinning processes, this study proposes a detection methodology using the you only look once for complex backgrounds of young citrus fruits (YCCB-YOLO) approach. The method first constructs a dataset containing images of young citrus fruits in a real orchard environment. To improve the detection accuracy while maintaining the computational efficiency, the study reconstructs the detection head and backbone network using pointwise convolution (PWonv) lightweight network, which reduces the complexity of the model without affecting the performance. In addition, the ability of the model to accurately detect young citrus fruits in complex backgrounds is enhanced by integrating the fusion attention mechanism. Meanwhile, the simplified spatial pyramid pooling fast-large kernel separated attention (SimSPPF-LSKA) feature pyramid was introduced to further enhance the multi-feature extraction capability of the model. Finally, the Adam optimization function was used to strengthen the nonlinear representation and feature extraction ability of the model. The experimental results show that the model achieves 91.79% precision (P), 92.75% recall (R), and 97.32% mean average precision (mAP)on the test set, which were improved by 1.33%, 2.24%, and 1.73%, respectively, compared with the original model, and the size of the model is only 5.4 MB. This study could meet the performance requirements for citrus fruit identification, which provides technical support for fruit thinning.

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Meta‐learning shows great potential in plant disease recognition under few available samples

Cited by 13 publications

References 91 publications

PDDD-PreTrain: A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis

PDDD-PreTrain: A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis

A Small Target Tea Leaf Disease Detection Model Combined with Transfer Learning

Fruits hidden by green: an improved YOLOV8n for detection of young citrus in lush citrus trees

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