Rice Grain Detection and Counting Method Based on TCLE-YOLO Model.

Thousand-grain weight is the main parameter for accurately estimating rice yields, and it is an important indicator for variety breeding and cultivation management. The accurate detection and counting of rice grains is an important prerequisite for thousand-grain weight measurements. However, because rice grains are small targets with high overall similarity and different degrees of adhesion, there are still considerable challenges preventing the accurate detection and counting of rice grains during thousand-grain weight measurements. A deep learning model based on a transformer encoder and coordinate attention module was, therefore, designed for detecting and counting rice grains, and named TCLE-YOLO in which YOLOv5 was used as the backbone network. Specifically, to improve the feature representation of the model for small target regions, a coordinate attention (CA) module was introduced into the backbone module of YOLOv5. In addition, another detection head for small targets was designed based on a low-level, high-resolution feature map, and the transformer encoder was applied to the neck module to expand the receptive field of the network and enhance the extraction of key feature of detected targets. This enabled our additional detection head to be more sensitive to rice grains, especially heavily adhesive grains. Finally, EIoU loss was used to further improve accuracy. The experimental results show that, when applied to the self-built rice grain dataset, the precision, recall, and mAP@0.5 of the TCLE-YOLO model were 99.20%, 99.10%, and 99.20%, respectively. Compared with several state-of-the-art models, the proposed TCLE-YOLO model achieves better detection performance. In summary, the rice grain detection method built in this study is suitable for rice grain recognition and counting, and it can provide guidance for accurate thousand-grain weight measurements and the effective evaluation of rice breeding.

Study on the Solidification Effect of Dredger Fill by Microbial-Induced Calcium Precipitation (MICP).

This paper puts forward a new soft soil reinforcement technology-microbial-induced calcite precipitation (MICP) technology-which considers the problem of dredger fill soft-soil reinforcement in Dalian Taiping Bay. In this paper, the calcium carbonate content (CCC) and unconfined compressive strength (UCS) of microbial solidified dredger fill (MSDF) samples were determined using laboratory experiments. The microstructure and chemical composition of MSDF samples were studied by SEM-EDS and XRD. The failure and reinforcement mechanism of MSDF under different experimental conditions (ambient temperature, cementation solution concentration, and clay content) were investigated. The results showed that there was a certain residual strength after the peak strength of MSDF. With the increase of ambient temperature, the number of microorganisms increased, but the activities of urease, CCC, and UCS decreased. The UCS and CCC increased with the increase of cementation solution concentration, while they first increased and then decreased with the increase of clay content. The clay content enhanced the compactness of MSDF samples but reduced the soil permeability and weakened the mineralization. There were significant differences in the morphology of microbial-induced precipitation caused by different concentrations of cementation solution.