Accurate classification of plant disease by farming robot cameras can increase crop yield and reduce unnecessary agricultural chemicals, which is a fundamental task in the field of sustainable and precision agriculture. However, until now, disease classification has mostly been performed by manual methods, such as visual inspection, which are labor-intensive and often lead to misclassification of disease types. Therefore, previous studies have proposed disease classification methods based on machine learning or deep learning techniques; however, most did not consider real-world plant images with complex backgrounds and incurred high computational costs. To address these issues, this study proposes a computationally effective residual convolutional attention network (RCA-Net) for the disease classification of plants in field images with complex backgrounds. RCA-Net leverages attention mechanisms and multiscale feature extraction strategies to enhance salient features while reducing background noises. In addition, we introduce fractal dimension estimation to analyze the complexity and irregularity of class activation maps for both healthy plants and their diseases, confirming that our model can extract important features for the correct classification of plant disease. The experiments utilized two publicly available datasets: the sugarcane leaf disease and potato leaf disease datasets. Furthermore, to improve the capability of our proposed system, we performed fractal dimension estimation to evaluate the structural complexity of healthy and diseased leaf patterns. The experimental results show that RCA-Net outperforms state-of-the-art methods with an accuracy of 93.81% on the first dataset and 78.14% on the second dataset. Furthermore, we confirm that our method can be operated on an embedded system for farming robots or mobile devices at fast processing speed (78.7 frames per second).

Iris recognition is a biometric method using the pattern of the iris seated between the pupil and the sclera for recognizing people. It is widely applied in various fields owing to its high accuracy in recognition and high security. A spoof detection method for discriminating a spoof attack is essential in biometric recognition systems that include iris recognition. However, previous studies have mainly investigated spoofing attack detection methods based on printed or photographed images, video replaying, artificial eyes, and patterned contact lenses fabricated using iris images from information leakage. On the other hand, there have only been a few studies on spoof attack detection using iris images generated through a generative adversarial network (GAN), which is a method that has drawn considerable research interest with the recent development of deep learning, and the enhancement of spoof detection accuracy by the methods proposed in previous research is limited. To address this problem, the possibility of an attack on a conventional iris recognition system with spoofed iris images generated using cycle-consistent adversarial networks (CycleGAN), which was the motivation of this study, was investigated. In addition, a local region-based fake-iris detection network (LRFID-Net) was developed. It provides a novel method for discriminating fake iris images by segmenting the iris image into three regions based on the iris region. Experimental results using two open databases, the Warsaw LiveDet-Iris-2017 and the Notre Dame Contact Lens Detection LiveDet-Iris-2017 datasets, showed that the average classification error rate of spoof detection by the proposed method was 0.03% for the Warsaw dataset and 0.11% for the Notre Dame Contact Lens Detection dataset. The results confirmed that the proposed method outperformed the state-of-the-art methods.

With recent advancements in deep learning, spoofing techniques have developed and generative adversarial networks (GANs) have become an emerging threat to finger-vein recognition systems. Therefore, previous research has been performed to generate finger-vein images for training spoof detectors. However, these are limited and researchers still cannot generate elaborate fake finger-vein images. Therefore, we develop a new densely updated contrastive learning-based self-attention generative adversarial network (DCS-GAN) to create elaborate fake finger-vein images, enabling the training of corresponding spoof detectors. Additionally, we propose an enhanced convolutional network for a next-dimension (ConvNeXt)-Small model with a large kernel attention module as a new spoof detector capable of distinguishing the generated fake finger-vein images. To improve the spoof detection performance of the proposed method, we introduce fractal dimension estimation to analyze the complexity and irregularity of class activation maps from real and fake finger-vein images, enabling the generation of more realistic and sophisticated fake finger-vein images. Experimental results obtained using two open databases showed that the fake images by the DCS-GAN exhibited Frechet inception distances (FID) of 7.601 and 23.351, with Wasserstein distances (WD) of 18.158 and 10.123, respectively, confirming the possibility of spoof attacks when using existing state-of-the-art (SOTA) frameworks of spoof detection. Furthermore, experiments conducted with the proposed spoof detector yielded average classification error rates of 0.4% and 0.12% on the two aforementioned open databases, respectively, outperforming existing SOTA methods for spoof detection.

In vitro fertilization (IVF) is an efficacious form of aided reproduction to deal with infertility. Human embryos are taken from the body, and these are kept in a supervised laboratory atmosphere during the IVF technique until they exhibit blastocyst properties. A human expert manually analyzes the morphometric properties of the blastocyst and its compartments to predict viability through manual microscopic evaluation. A few deep learning-based approaches deal with this task via semantic segmentation, but they are inaccurate and use expensive architecture. To automatically detect the human blastocyst compartments, we propose a parallel stream fusion network (PSF-Net) that performs the semantic segmentation of embryo microscopic images with inexpensive shallow architecture. The PSF-Net has a shallow architecture that combines the benefits of feature aggregation through depth-wise concatenation and element-wise summation, which helps the network to provide accurate detection using 0.7 million trainable parameters only. In addition, we compute fractal dimension estimation for all compartments of the blastocyst, providing medical experts with significant information regarding the distributional characteristics of blastocyst compartments. An open dataset of microscopic images of the human embryo is used to evaluate the proposed approach. The proposed method also demonstrates promising segmentation performance for all compartments of the blastocyst compared with state-of-the-art methods, achieving a mean Jaccard index (MJI) of 87.69%. The effectiveness of PSF-Net architecture is also confirmed with the ablation studies.