SD-YOLOv8: An Accurate Seriola dumerili Detection Model Based on Improved YOLOv8.

Accurate identification of Seriola dumerili (SD) offers crucial technical support for aquaculture practices and behavioral research of this species. However, the task of discerning S. dumerili from complex underwater settings, fluctuating light conditions, and schools of fish presents a challenge. This paper proposes an intelligent recognition model based on the YOLOv8 network called SD-YOLOv8. By adding a small object detection layer and head, our model has a positive impact on the recognition capabilities for both close and distant instances of S. dumerili, significantly improving them. We construct a convenient S. dumerili dataset and introduce the deformable convolution network v2 (DCNv2) to enhance the information extraction process. Additionally, we employ the bottleneck attention module (BAM) and redesign the spatial pyramid pooling fusion (SPPF) for multidimensional feature extraction and fusion. The Inner-MPDIoU bounding box regression function adjusts the scale factor and evaluates geometric ratios to improve box positioning accuracy. The experimental results show that our SD-YOLOv8 model achieves higher accuracy and average precision, increasing from 89.2% to 93.2% and from 92.2% to 95.7%, respectively. Overall, our model enhances detection accuracy, providing a reliable foundation for the accurate detection of fishes.

PDC: Pearl Detection with a Counter Based on Deep Learning.

Pearl detection with a counter (PDC) in a noncontact and high-precision manner is a challenging task in the area of commercial production. Additionally, sea pearls are considered to be quite valuable, so the traditional manual counting methods are not satisfactory, as touching may cause damage to the pearls. In this paper, we conduct a comprehensive study on nine object-detection models, and the key metrics of these models are evaluated. The results indicate that using Faster R-CNN with ResNet152, which was pretrained on the pearl dataset, mAP@0.5IoU = 100% and mAP@0.75IoU = 98.83% are achieved for pearl recognition, requiring only 15.8 ms inference time with a counter after the first loading of the model. Finally, the superiority of the proposed algorithm of Faster R-CNN ResNet152 with a counter is verified through a comparison with eight other sophisticated object detectors with a counter. The experimental results on the self-made pearl image dataset show that the total loss decreased to 0.00044. Meanwhile, the classification loss and the localization loss of the model gradually decreased to less than 0.00019 and 0.00031, respectively. The robust performance of the proposed method across the pearl dataset indicates that Faster R-CNN ResNet152 with a counter is promising for natural light or artificial light peal detection and accurate counting.

Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism.

Cassava disease is one of the leading causes to the serious decline of cassava yield. Because it is difficult to identify the characteristics of cassava disease, if not professional cassava growers, it will be prone to misjudgment. In order to strengthen the judgment of cassava diseases, the identification characteristics of cassava diseases such as different color of cassava leaf disease spots, abnormal leaf shape and disease spot area were studied. In this paper, deep convolutional neural network was used to classify cassava leaf diseases, and image classification technology was used to recognize and classify cassava leaf diseases. A lightweight module Multi-scale fusion model (MSFM) based on attention mechanism was proposed to extract disease features of cassava leaves to enhance the classification of disease features. The resulting feature map contained key disease identification information. The study used 22,000 cassava disease leaf images as a data set, including four different cassava leaf disease categories and healthy cassava leaves. The experimental results show that the cassava leaf disease classification model based on multi-scale fusion Convolutional Neural Network (CNN) improves EfficientNet compared with the original model, with the average recognition rate increased by nearly 4% and the average recognition rate up to 88.1%. It provides theoretical support and practical tools for the recognition and early diagnosis of plant disease leaves.

Amplification of lysozyme signal detected in capillary electrophoresis using mixed polymer brushes coating with switchable properties.

In this work, a mixed polymer brushes based on poly(2-methyl-2-oxazoline) (PMOXA) and poly(acrylic acid) (PAA) coated capillary with switchable protein adsorption/desorption properties was developed and applied for on-line extraction and preconcentration of lysozyme. The study of electroosmotic flow (EOF) and fluorescence microscope showed that the inner surface charge of PMOXA/PAA mixed brush coated capillary displayed the switchable behavior toward the change of pH value and ionic strength (I), and PMOXA/PAA mixed brushes coated capillary could adsorb high amounts of lysozyme at pH 7 (I = 10-5 M), and the most of adsorbed lysozyme could then be desorbed at pH 3 (I = 10-1 M). Subsequently, this coated capillary with switchable lysozyme adsorption/desorption ability was applied for on-line extraction and preconcentration of lysozyme during capillary electrophoresis (CE) performance. Under the process of on-line preconcentration, the detection signal (peak area) of lysozyme obtained in PMOXA/PAA coated capillary was 26 times that obtained in bare capillary under normal CE while the contour chain length of PAA was 1.56 times that of PMOXA. Moreover, the value of low detection limit (LOD) of lysozyme using above coated capillary under on-line preconcentration method reached to 4.5 × 10-9 mg/mL, and 1 × 105-fold sensitivity enhancement was realized for lysozyme as compared with the bare capillary under normal CE.