Biochar has shown significant potential to improve the quality of cultivated land and crop yields. This study aims to explore the research and application of biochar returning to the field for soil improvement and crop growth. A systematic review was conducted on the preparation and equipment of the biochar, the key influencing factors on the physicochemical properties of the biochar, as well as the effects of biochar on soil (physicochemical properties, nutrients, enzyme activities and microbial communities, inorganic heavy metals and organic pollutants, carbon sink reduction) and crops. The key influencing factors and mechanisms were summarized to evaluate the risks and countermeasures of biochar application. Some suggestions were proposed for future research. The results showed that the rotary-furnace reactor was successfully applied to the industrial-scale production of biochar, due to its good adaptability and convenient operation. Furthermore, the soil bulk density decreased after the application of biochar into the soil, whereas there was an increase in the water retention capacity, cation exchange capacity, the content of macronutrients and micronutrients in the soil, as well as the utilization rate of nutrients by plants. The microbial community structure was dominated by the biochar. And then the activities of soil enzymes were enhanced, such as urease and peroxidase. Heavy metals were also adsorbed and fixed by biochar after ion exchange and complexation. The transformation of heavy metals was promoted from the exchangeable to the residual state. Organic pollutants were adsorbed and degraded by biochar after hydrophobic interaction, electrostatic interaction, and free radical interaction. The toxicity and bioaccumulation of pollutants were reduced in the soil. The returning biochar to the field can be expected to mitigate climate change through soil carbon cycling. Taking Chinese umbrella grass biochar as an example, the annual output reached 1.96-3.21 Mt, and the long-term annual carbon sequestration was 0.75-0.79 Mt, and the long-term annual reduction of CO₂ equivalent was 2.76-2.88 Mt. Generally, the behavior of returning biochar to the field had a positive effect on the crop yield. However, the extensive application of biochar in the soil can pose environmental and health risks. The pollutants that are contained in biochar itself, and the pollutants were used to adsorb from the environment (such as heavy metals and polycyclic aromatic hydrocarbons), indicating the potential to be released into the soil under the influence of the environment and time. Eventually, the nano-biochar was obtained during biochar aging after disintegration, fragmentation, and pulverization. Currently, there is a significant lack of research on the safety, stability, and toxicity of nano-biochar, particularly on the health risks of biochar to humans. Therefore, it is very necessary to conduct a systematic assessment of the long-term and large-scale biochar application in fields. Some optimal measures were also proposed to evaluate the economic, social, and ecological benefits, in order to reduce or even avoid the application risks of biochar. The research can also provide a strong reference to promote the healthy development of the biochar industry in high-quality agriculture.

Potatoes have been a versatile food and cash crop to ensure food security and grain planting structure in China. The total planting area has declined in the planting industries at present. Some challenges also remain in the mechanization of the potato planting industry. The current process of seed potato cutting can rely heavily on manual and mechanical blind cutting, leading to labor-intensive and inefficient tasks. Moreover, there is a high rate of blind cuts and significant loss of seed potatoes. Therefore, it is highly urgent to accurately and rapidly identify the potato eyes before cutting. In this study, an improved model was proposed to detect the potato bud eyes using YOLOv5. Dutch 15 potato variety was taken as the experimental material. The high-quality samples of seed potatoes were carefully chosen to be free of diseases, dry rot, disease spots, and worm eyes. The dataset was used for training, verification, and testing. 1 400 pictures of seed potatoes were obtained with a ratio of 8:1:1. Data expansion techniques (such as mirroring, rotating, cropping, and adjusting brightness) were applied to enhance the dataset, thus resulting in a total of 5 600 images. Since the features of seed potato eyes were relatively simple, there was a decrease to even disappear after multiple convolutions. C3 Faster was integrated into the original framework. While the extraction of sprout eye features was enhanced to reduce the parameters. Additionally, the GD structure was incorporated from the Neck component of GOLD-YOLO to improve the detection accuracy of sprout eye. The bounding box loss function CIoU Loss was replaced with WIoU Loss to expedite the convergence of the network model for high detection accuracy. The hyperparameters of the original YOLO model were optimized for the COCO dataset. There were significant differences from the dataset of seed potato sprout eye in this experiment. Therefore, a genetic algorithm (GA) was employed to fine-tune the hyperparameters specifically for the detection of seed potato sprout eye at the end of the experiment. Furthermore, pruning and distillation techniques were used to reduce the running parameters and memory consumption, in order to make the model more suitable for the detection tasks of seed potato bud eye. The optimized model was presented with a size of 8.7 MB, which was only 61.3% of the original model. Params comprised approximately 57.1% of the original model. The final average accuracies of detection were 90.5% and 90.1%, respectively, in the test and validation sets of the self-made potato dataset. Compared with the lightweight networks YOLOv7-tiny, YOLOv8n, YOLOv5n, and YOLOv5s, the average accuracies of the improved model were 0.5, 1.3, 2.8, and 1.1 percentage points higher, respectively, in the test set of seed potato dataset. The average accuracy of the verification set was 2.9, 1.9, 3.2, and 1.6 percentage points higher. The detection speed on a local computer reached 27.5 frames per second, fully meeting the real-time requirements. Substantial benefits were offered to significantly enhance the efficiency and accuracy of seed potato bud eye detection in the potato cultivation industry.