Plant disease and insect pest identification based on vision transformer

Li, Hang; Li, Sufang; Yu, Jiguo; Han, Yubing; Dong, Anming

doi:10.1117/12.2628467

Cited by 11 publications

(3 citation statements)

References 4 publications

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“…Li et al [13] developed an automated pest detection technique based on Vision Transformer (ViT). To prevent over-fitting, the plant disease and insect pest datasets are optimized using techniques like Laplacian, Gamma Transformation, Histogram Equalization, Retinex-SSR, Retinex-MSR, and CLAHE.…”

Section: Literature Reviewmentioning

confidence: 99%

Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

Yonbawi¹,

Alahmari²,

murthy³

et al. 2023

Computer Systems Science and Engineering

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Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged, and due to the pest attacks, the quality is degraded. They are the major reason behind crop quality degradation and diminished crop productivity. Hence, accurate pest detection is essential to guarantee safety and crop quality. Conventional identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological features. Lately, some progress has been made in agriculture by employing machine learning (ML) to classify and detect pests. This study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification for Agricultural Crops (MMTL-IPCAC) technique. The presented MMTL-IPCAC technique applies contrast limited adaptive histogram equalization (CLAHE) approach for image enhancement. The neural architectural search network (NASNet) model is applied for feature extraction, and a modified grey wolf optimization (MGWO) algorithm is employed for the hyperparameter tuning process, showing the novelty of the work. At last, the extreme gradient boosting (XGBoost) model is utilized to carry out the insect classification procedure. The simulation analysis stated the enhanced performance of the MMTL-IPCAC technique in the insect classification process with maximum accuracy of 98.73%.

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Section: Literature Reviewmentioning

confidence: 99%

Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

Yonbawi¹,

Alahmari²,

murthy³

et al. 2023

Computer Systems Science and Engineering

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“…Qi et al [21] designed a novel multi-head cross-attention module using the Detection Transformer (DETR) method for pest detection, achieving an accuracy of 72.5%. In pursuit of higher accuracy, Li et al [22] proposed an automatic pest identification method based on the Vision Transformer (ViT), which achieved 96.71% accuracy in automatic classification of plant pests through experiments. Dai et al [23] incorporated the SWin Transformer (SWinTR) and Transformer (C3TR) mechanisms into the YOLOv5m network, reaching 95.7% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Fruit Fly Detection in Complex Backgrounds Using Transformer Architecture with Step Attention Mechanism

Zhang,

Chen,

Zheng

et al. 2024

Agriculture

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This study introduces a novel high-accuracy fruit fly detection model based on the Transformer structure, specifically aimed at addressing the unique challenges in fruit fly detection such as identification of small targets and accurate localization against complex backgrounds. By integrating a step attention mechanism and a cross-loss function, this model significantly enhances the recognition and localization of fruit flies within complex backgrounds, particularly improving the model’s effectiveness in handling small-sized targets and its adaptability under varying environmental conditions. Experimental results demonstrate that the model achieves a precision of 0.96, a recall rate of 0.95, an accuracy of 0.95, and an F1-score of 0.95 on the fruit fly detection task, significantly outperforming leading object detection models such as YOLOv8 and DETR. Specifically, this research delves into and optimizes for challenges faced in fruit fly detection, such as recognition issues under significant light variation, small target size, and complex backgrounds. Through ablation experiments comparing different data augmentation techniques and model configurations, the critical contributions of the step attention mechanism and cross-loss function to enhancing model performance under these complex conditions are further validated. These achievements not only highlight the innovativeness and effectiveness of the proposed method, but also provide robust technical support for solving practical fruit fly detection problems in real-world applications, paving new paths for future research in object detection technology.

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“…Recently, Transformer-based backbones have shown potential performance and expanded cutting-edge applications. Li et al (2022a) proposed an automatic pest recognition method based on Vision Transformer (ViT) in PlantVillage (a public dataset of plant pests and diseases) (Hughes and Salathe ́, 2015). Reedha et al (2022) proposed a novel crop recognition model using ViT based on unmanned aerial vehicles (UAV) remote sensing images.…”

mentioning

confidence: 99%

Cotton leaf segmentation with composite backbone architecture combining convolution and attention

Yan

et al. 2023

Front. Plant Sci.

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Plant leaf segmentation, especially leaf edge accurate recognition, is the data support for automatically measuring plant phenotypic parameters. However, adjusting the backbone in the current cutting-edge segmentation model for cotton leaf segmentation applications requires various trial and error costs (e.g., expert experience and computing costs). Thus, a simple and effective semantic segmentation architecture (our model) based on the composite backbone was proposed, considering the computational requirements of the mainstream Transformer backbone integrating attention mechanism. The composite backbone was composed of CoAtNet and Xception. CoAtNet integrated the attention mechanism of the Transformers into the convolution operation. The experimental results showed that our model outperformed the benchmark segmentation models PSPNet, DANet, CPNet, and DeepLab v3+ on the cotton leaf dataset, especially on the leaf edge segmentation (MIoU: 0.940, BIoU: 0.608). The composite backbone of our model integrated the convolution of the convolutional neural networks and the attention of the Transformers, which alleviated the computing power requirements of the Transformers under excellent performance. Our model reduces the trial and error cost of adjusting the segmentation model architecture for specific agricultural applications and provides a potential scheme for high-throughput phenotypic feature detection of plants.

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Plant disease and insect pest identification based on vision transformer

Cited by 11 publications

References 4 publications

Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

Enhancing Fruit Fly Detection in Complex Backgrounds Using Transformer Architecture with Step Attention Mechanism

Cotton leaf segmentation with composite backbone architecture combining convolution and attention

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