HSSNet: A End-to-End Network for Detecting Tiny Targets of Apple Leaf Diseases in Complex Backgrounds.

Apple leaf diseases are one of the most important factors that reduce apple quality and yield. The object detection technology based on deep learning can detect diseases in a timely manner and help automate disease control, thereby reducing economic losses. In the natural environment, tiny apple leaf disease targets (a resolution is less than 32 × 32 pixel2) are easily overlooked. To address the problems of complex background interference, difficult detection of tiny targets and biased detection of prediction boxes that exist in standard detectors, in this paper, we constructed a tiny target dataset TTALDD-4 containing four types of diseases, which include Alternaria leaf spot, Frogeye leaf spot, Grey spot and Rust, and proposed the HSSNet detector based on the YOLOv7-tiny benchmark for professional detection of apple leaf disease tiny targets. Firstly, the H-SimAM attention mechanism is proposed to focus on the foreground lesions in the complex background of the image. Secondly, SP-BiFormer Block is proposed to enhance the ability of the model to perceive tiny targets of leaf diseases. Finally, we use the SIOU loss to improve the case of prediction box bias. The experimental results show that HSSNet achieves 85.04% mAP (mean average precision), 67.53% AR (average recall), and 83 FPS (frames per second). Compared with other standard detectors, HSSNet maintains high real-time detection speed with higher detection accuracy. This provides a reference for the automated control of apple leaf diseases.

Tribology of Copper Metal Matrix Composites Reinforced with Fluorinated Graphene Oxide Nanosheets: Implications for Solid Lubricants in Mechanical Switches.

The potential for the use of copper coatings on steel switching mechanisms is abundant owing to the high conductivities and corrosion resistance that they impart on the engineered assemblies. However, applications of these coatings on such moving parts are limited due to their poor tribological properties; tendencies to generate high friction and susceptibility to degradative wear. In this study, we have fabricated a fluorinated graphene oxide-copper metal matrix composite (FGO-CMMC) on an AISI 52100 bearing steel substrate by a simple electrodeposition process in water. The FGO-CMMC coatings exhibited excellent lubrication performance under pin-on-disk (PoD) tribological sliding at 1N load, which reduced CoF by 63 and 69%, compared to the GO-CMMC and pure copper coatings that were also prepared. Furthermore, FGO-CMMC achieved low friction and low wear at higher sliding loads. The lubrication enhancement of the FGO-CMMCs is attributed to the tribochemical reaction of FGO with the AISI 52100 steel counterface initiated by the sliding load. The formation of an asymmetric tribofilm structure on the sliding track is critical; the performance of the FGO/Cu tribofilm formed in the track is boosted by the continued fluorination of the counterface surface during PoD sliding, passivating the tribosystem from adhesion-driven breakdown. The FGO-CMMC and GO-CMMC coatings also provide increased corrosion protection reaching 94.2 and 91.6% compared to the bare steel substrate, allowing for the preservation of the long-term low-friction performance of the coating. Other influences include the improved interlaminar shear strength of the FGO-containing composite. The excellent lubrication performance of the copper matrix composite coatings facilitated by FGO incorporation makes it a promising solid lubricant candidate for use in mechanical engineering applications.

An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet.

Tomato disease control is an urgent requirement in the field of intellectual agriculture, and one of the keys to it is quantitative identification and precise segmentation of tomato leaf diseases. Some diseased areas on tomato leaves are tiny and may go unnoticed during segmentation. Blurred edge also makes the segmentation accuracy poor. Based on UNet, we propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module (MC-UNet). First, a Multi-scale Convolution Module is proposed. This module obtains multiscale information about tomato disease by employing 3 convolution kernels of different sizes, and it highlights the edge feature information of tomato disease using the Squeeze-and-Excitation Module. Second, a Cross-layer Attention Fusion Mechanism is proposed. This mechanism highlights tomato leaf disease locations via gating structure and fusion operation. Then, we employ SoftPool rather than MaxPool to retain valid information on tomato leaves. Finally, we use the SeLU function appropriately to avoid network neuron dropout. We compared MC-UNet to the existing segmentation network on our self-built tomato leaf disease segmentation dataset and MC-UNet achieved 91.32% accuracy and 6.67M parameters. Our method achieves good results for tomato leaf disease segmentation, which demonstrates the effectiveness of the proposed methods.