Copper (Cu) is an essential nutrient for plant growth and development. This metal serves as a constituent element or enzyme cofactor that participates in many biochemical pathways and plays a key role in photosynthesis, respiration, ethylene sensing, and antioxidant systems. The physiological significance of Cu uptake and compartmentalization in plants has been underestimated, despite the importance of Cu in cellular metabolic processes. As a micronutrient, Cu has low cellular requirements in plants. However, its bioavailability may be significantly reduced in alkaline or organic matter-rich soils. Cu deficiency is a severe and widespread nutritional disorder that affects plants. In contrast, excessive levels of available Cu in soil can inhibit plant photosynthesis and induce cellular oxidative stress. This can affect plant productivity and potentially pose serious health risks to humans via bioaccumulation in the food chain. Plants have evolved mechanisms to strictly regulate Cu uptake, transport, and cellular homeostasis during long-term environmental adaptation. This review provides a comprehensive overview of the diverse functions of Cu chelators, chaperones, and transporters involved in Cu homeostasis and their regulatory mechanisms in plant responses to varying Cu availability conditions. Finally, we identified that future research needs to enhance our understanding of the mechanisms regulating Cu deficiency or stress in plants. This will pave the way for improving the Cu utilization efficiency and/or Cu tolerance of crops grown in alkaline or Cu-contaminated soils.

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.

Rice is the staple diet for most of the world’s population and is considered a major staple crop in China. Anhui province of China is among the leading provinces for rice production, consumption, and commodities; it is well-known as the “land of fish and rice”. Japonica rice cultivation in Anhui Province is mainly categorized into late-maturing medium, early-maturing medium, and early-maturing late japonica. This review explores the suitable ecological zone distribution and corresponding climate characteristics of the three types of japonica rice in Anhui Province. Data on japonica rice varieties approved in the province over the past twenty years were collected, illustrating the development process of japonica rice varieties in the province and their quality and resistance to rice blast disease. The review shows that the yield is positively and significantly correlated with agronomic traits, such as the number of effective panicles and the total number of grains per panicle, plant height, etc. In addition, it elucidates the major problems faced by Anhui’s japonica rice breeding and cultivation, such as frequent events of high temperatures, rice blast disease, and medium to low soil fertility levels. Considering the existing issues in breeding japonica rice varieties in Anhui Province, this review proposes a strategy for breeding high-yield and disease-resistant japonica rice varieties, particularly varieties adaptable to medium and low fertility soil conditions. In brief, this article provides a theoretical basis and practical recommendations for the sustainable development of japonica rice in the Anhui Province of China.