To address global climate change， reduce greenhouse gas （GHG） emissions， and support China's “carbon peaking and carbon neutrality” goals， this study reviews the current state of farmland carbon emissions in China and explores pathways for carbon sequestration and emission reduction from an agricultural perspective. A comprehensive analysis of carbon emissions across sectors， the composition of agricultural emission sources， and regional differences among provinces reveals that the evolution of China's GHG emissions can be divided into three phases： steady growth， rapid growth， and significant deceleration. The primary sources of greenhouse gas emissions in the agricultural sector include methane （CH₄） emissions from livestock enteric fermentation and rice production， as well as nitrous oxide （N₂O） emissions from microbial decomposition in agricultural soils. These sources exhibit significant regional and temporal variations， influenced by factors such as climate conditions， cropping patterns， and management practices. The study pointed out that changing current agricultural management practices is key to achieving emission reductions in agriculture. Optimizing management measures can not only mitigate agricultural carbon emissions but also enhance the carbon sequestration capacity of farmlands. Based on a cluster analysis of keywords and future research directions in the field of farmland carbon emissions in China， this study identifies current research hotspots and knowledge gaps. It emphasizes the importance of exploring regionally adaptive agronomic management practices to improve the effectiveness of carbon sequestration and emission reduction in different types of agricultural land， such as paddy fields and drylands. By analyzing the effects of high-frequency agronomic practices on increasing yields and reducing emissions， the study systematically elucidates the feasibility of measures such as optimizing water and fertilizer management， adjusting cropping structures， incorporating straw return and agricultural waste utilization， and adopting low-carbon agricultural equipment. This research provides systematic scientific evidence for theoretical studies and practical applications of farmland carbon sequestration and emission reduction in China. It also offers guidance for the promotion of relevant energy-saving and emission-reduction technologies. By focusing on the future pathways for green agricultural development， this study offers strategic insights for achieving the “dual-carbon” goals in China's agricultural sector.

Cable-driven parallel robots have become a hot research topic in the field of robotics in recent years due to the characteristics of simple structure， small motion inertia， reconfigurability， and fast response speed， etc. The cable-driven flexible manipulator provides a certain adaptability and softness for operation due to the flexibility and elasticity of driving cable，and consequently it can achieve good dynamic interaction between the operating objects and the manipulators， and improve the ability to prevent broken loss coupling， which has also attracted widespread attention from researchers in the field of agricultural robotics. Due to the working environment of agricultural picking robots has the characteristics of unstructured and uncertainty， and most of the fruit outer skin is fragile and easy to damage， the requirements for picking robots that come into directly contacted with the fruits are stricter， ensuring stable grasping without damaging the fruits. In order to achieve efficient and precise operations of picking robots， lightweight and flexible end execution agencies that interact with biological friendly interaction are key theoretical technologies that urgently need to be broken through. This article first elaborates on the characteristics and development overview of cable-driven flexible manipulators with commercial application prospects. Then， the research progress of the theory such as the design， modeling and control of cable-driven flexible robotics at home and abroad is reviewed in detail. Further the typical application development of cable-driven flexible robots in the field of physical rehabilitation and dexterous grabbing is outlined. A detailed analysis is conducted on the significance of the application of cable-driven flexible manipulators in the field of agricultural picking， and the current research status of cable-driven flexible manipulators in tomato picking， apple picking， strawberry picking， and blackberry picking. Aiming at the problems of low picking accuracy， poor versatility， high cost， damage and low picking efficiency of cable-driven flexible manipulator in agricultural picking at the present stage， it is pointed out that in the future， the commercialization of cable-driven flexible manipulator needs to make innovations in agricultural machinery and agronomy collaboration， modular and reconfigurable design， improving interaction safety， multi-sensor integration， picking sequence planning and strong robust control in this study and it provides new ideas and methods for the design of mechanized non-destructive picking mechanisms for fruits and vegetables.

To effectively identify tea buds in complex environments and improve the precision of intelligent harvesting while minimizing damage to tea trees， this study addresses the issues of low detection accuracy and poor robustness exhibited by traditional target detection algorithms in tea gardens， and proposes YOLOv7-tea model for tea bud identification and detection based on an improved YOLOv7， so as to achieve rapid recognition and detection of tea buds.First， tea bud images were collected and annotated， and data augmentation was performed to construct a tea bud dataset. Next， the CBAM attention mechanism module was introduced into three feature extraction layers of the YOLOv7 backbone network to enhance the model's feature extraction capability； the SPD-Conv module was used to replace the SConv module in the neck network's downsampling module to reduce the loss of small object features； and the EIoU loss function was employed to optimize box regression， thereby improving the accuracy of the predicted boxes. Finally， a comparative experiment was conducted between other target detection models and the YOLOv7-tea model using the tea bud image dataset as a sample， and the recognition effect of tea buds shot at different distances and angles was tested.The experimental results show that the YOLOv7-tea network model outperforms the YOLOv7 model in terms of precision （P）， recall （R）， and mean average precision （mAP） by 2.87， 6.91， and 8.69 percentage points， respectively. Additionally， the model has a faster detection speed and exhibits higher confidence scores in the recognition and detection of tea buds in complex backgrounds.The YOLOv7-tea model constructed in this study demonstrates better recognition performance for small-sized tea leaf buds， reducing instances of missed detection and false alarms. It exhibits good robustness and real-time performance， offering valuable insights for estimating tea yield and implementing intelligent harvesting.