Authentication of tomato (Solanum lycopersicum L.) cultivars using discriminative models based on texture parameters of flesh and skin images

Ropelewska, Ewa; Sabancı, Kadir; Aslan, Muhammet Fatih

doi:10.1007/s00217-022-04019-6

Cited by 8 publications

(2 citation statements)

References 31 publications

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“…In the t th convolutional layer of a CNN architecture, the feature map was calculated by the convolution kernels and features in ( 1) t − th layer. To take a frequency point of view, it was created by filtering frequency components of features in ( 1) t − th layer, and…”

Section: Low-frequency Attention For Domain Generalizationmentioning

confidence: 99%

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MLFAnet: A Tomato Disease Classification Method Focusing on OOD Generalization

Yin

Zhao

et al. 2023

Agriculture

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Tomato disease classification based on images of leaves has received wide attention recently. As one of the best tomato disease classification methods, the convolutional neural network (CNN) has an immense impact due to its impressive performance. However, better performance is verified by independent identical distribution (IID) samples of tomato disease, which breaks down dramatically on out-of-distribution (OOD) classification tasks. In this paper, we investigated the corruption shifts, which was a vital component of OOD, and proposed a tomato disease classification method to improve the performance of corruption shift generalization. We first adopted discrete cosine transform (DCT) to obtain the low-frequency components. Then, the weight of the feature map was calculated by multiple low-frequency components, in order to reduce the influence of high-frequency variation caused by corrupted perturbation. The proposed method, termed as a multiple low-frequency attention network (MLFAnet), was verified by the benchmarking of ImageNet-C. The accuracy result and generalization performance confirmed the effectiveness of MLFAnet. The satisfactory generalization performance of our proposed classification method provides a reliable tool for the diagnosis of tomato disease.

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Section: Low-frequency Attention For Domain Generalizationmentioning

confidence: 99%

“…Prior to diagnosis, approaches of computer vision focused on the manual feature extraction. Ropelewska et al [1] extracted the texture parameters to discriminate the cultivars of tomatoes. Several classical machine learning methods, such as HoeffdingTree and BayesNet, were also proved to be effective in classification [2].…”

Section: Introductionmentioning

confidence: 99%