Plant Disease Diagnosis in the Visible Spectrum

Bustos, Lilí Guadarrama; Paredes-Orta, Carlos A.; Mercado, Omar

doi:10.3390/app12042199

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…Durmus used tomato photos from the PlantVillage dataset to train numerous deep neural networks, and the accuracy of networks such as SqueezeNet significantly improved due to this [7][8]. Using plant images in the visible spectrum, Lily proposed a straightforward and reliable method for diagnosing plant diseases [9]. In his research work, Kaur proposed the DAG-ResNet model and utilized it to discover a number of tomato illnesses [10].…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Classification and Adversarial Attack based CL-CondenseNetV2 and WT-MI-FGSM

Li¹,

Lü²

2023

IJACSA

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In recent years, deep learning has been increasingly used to the detection of pests and diseases. Unfortunately, deep neural networks are particularly vulnerable when attacked by adversarial examples. Hence it is vital to explore the creation of intensely aggressive adversarial examples to increase neural network robustness. This paper proposes a wavelet transform and histogram equalization-based adversarial attack algorithm: WT-MI-FGSM. In order to verify the performance of the WT-MI-FGSM, we propose a plant pests and diseases identification method based on the coordinate attention mechanism and CondenseNetV2: CL-CondenseNetV2. The accuracy of CL-CondenseNetV2 on the PlantVillage dataset is 99.45%, which indicates that the improved CondenseNetV2 model has a more significant classification performance. In adversarial sample experiments using WT-MI-FGSM and CL-CondenseNetV2, experimental results show that when CL-CondenseNetV2 is attacked by the adversarial algorithm WT-MI-FGSM, the error rate reaches 89.8%, with a higher attack success rate than existing adversarial attack algorithms. In addition, the accuracy of CL-CondenseNetV2 is improved to 99.71% by adding the adversarial samples generated by WT-MI-FGSM to the training set and performing adversarial training. The experiments demonstrate that the adversarial examples caused by WT-MI-FGSM can improve the model's performance.

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Section: Introductionmentioning

confidence: 99%

Plant Disease Classification and Adversarial Attack based CL-CondenseNetV2 and WT-MI-FGSM

Li¹,

Lü²

2023

IJACSA

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SubCoPLeD: Superpixel Based Color Distribution Driven Plant Leaf Disease Detection

Singh

2023

2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

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Plant Disease Classification and Adversarial Attack Using SimAM-EfficientNet and GP-MI-FGSM

You

Tang

2023

Sustainability

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Plant diseases have received common attention, and deep learning has also been applied to plant diseases. Deep neural networks (DNNs) have achieved outstanding results in plant diseases. Furthermore, DNNs are very fragile, and adversarial attacks in image classification deserve much attention. It is important to detect the robustness of DNNs through adversarial attacks. The paper firstly improves the EfficientNet by adding the SimAM attention module. The SimAM-EfficientNet is proposed in this paper. The experimental results show that the accuracy of the improved model on PlantVillage reaches 99.31%. The accuracy of ResNet50 is 98.33%. The accuracy of ResNet18 is 98.31%. The accuracy of DenseNet is 98.90%. In addition, the GP-MI-FGSM adversarial attack algorithm improved by gamma correction and image pyramid in this paper can increase the success rate of attack. The model proposed in this paper has an error rate of 87.6% whenattacked by the GP-MI-FGSM adversarial attack algorithm. The success rate of GP-MI-FGSM proposed in this paper is higher than other adversarial attack algorithms, including FGSM, I-FGSM, and MI-FGSM.

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Plant Disease Diagnosis in the Visible Spectrum

Cited by 3 publications

References 38 publications

Plant Disease Classification and Adversarial Attack based CL-CondenseNetV2 and WT-MI-FGSM

Plant Disease Classification and Adversarial Attack based CL-CondenseNetV2 and WT-MI-FGSM

SubCoPLeD: Superpixel Based Color Distribution Driven Plant Leaf Disease Detection

Plant Disease Classification and Adversarial Attack Using SimAM-EfficientNet and GP-MI-FGSM

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