Chinese livestock and poultry farming sector consistently ranks among the global leaders in production volume. It not only has a comprehensive range of farming types, but also has a complete industrial chain with associated supporting infrastructure, and occupies a pivotal position in the agricultural industry. In recent years, due to the rapid development of large-scale farming, increased prevalence of animal diseases, persistent labor shortages and other factor shave intensified the demands for intelligent and automated farming equipment. Farming robots, as an important carrier for realizing intelligent agriculture, can effectively replace or assist humans in various tasks, and have great significance and broad prospects for enhancing animal welfare, improving production efficiency, and reducing pollution and waste. This article reviews the application of intelligent technologies such as image recognition, deep learning, reinforcement learning, and autonomous navigation, as well as key components such as thermal infrared cameras, depth cameras, and LiDAR in farming robots, and introduces its achievable functions such as animal physiological information detection, behavior posture recognition, environmental monitoring and control, and feed and milk management. In addition, considering that China’s automated commenced development more recently, there is still a developmental gap compared to some developed countries. Therefore, this article examines the current industrial status and direction of intelligent farming robots both domestically (in China) and internationally, which includes an exploration of its development history, market prospects and policy support. This article also looks forward to the future development of the smart farming industry, points out the challenges of current technologies in terms of economy, stability, animal acceptance, etc., and proposes possible future directions and improvement goals, aiming to provide a reference for relevant researchers to further promote technological innovation and industrial applications.

Contemporary fruit harvesting operations are confronted with several challenges, including labor shortages, low picking efficiency, and complex operational environments. These factors underscore the imperative for developing intelligent harvesting equipment endowed with high-precision perception and autonomous operation capabilities to comprehensively enhance the efficiency and quality of fruit picking. Sensor technology plays a critical role in key tasks of fruit-picking robots, such as path planning, fruit recognition, localization, and grasping control. In unstructured orchard environments, the collaborative utilization of vision, tactile, and laser sensors facilitates target identification, positional awareness, and obstacle avoidance, significantly improving the robot's adaptability and operational accuracy in complex settings. However, existing sensors still have technical limitations. For example, visual sensors are susceptible to deleterious effects from ambient sunlight interference, occlusion from branches and leaves, and the dense clustering of fruits, all of which impede robust target detection. Tactile sensors often display sensitive to fluctuations in temperature and humidity, which poses challenges for the accurate quantification of complex mechanical feedback, thereby hindering precise control of gripping force. In addition, path optimization in unstructured environments remains challenge, and the high procurement cost of laser sensors limits their large-scale application. Single-sensor systems suffer from limitations such as single-dimensional perception, poor adaptability to environmental variations, and insufficient recognition of fruit characteristics, making them suboptimal for deployment in unstructured orchard conditions. Therefore, this paper explores the future applications of sensor technologies in fruit-picking robots, focusing on the challenges of multi-sensor fusion, including data heterogeneity, temporal synchronization, and computational complexity. It highlights that the integration of multi-spectral imaging technologies such as infrared and ultraviolet, high dynamic range (HDR) imaging, and flexible electronic skin combined with biomimetic structural designs in multi-sensor fusion systems holds great promise for widespread application.

To enhance the accuracy of the discrete element method (DEM) simulation parameters for smart shellfish seeding equipment, this study systematically calibrated the contact parameters between the clam seedling of the shifting beach quadrangular clam and the contact materials (EVA, stainless steel plate) and the clam seedling for the first time. This calibration process integrated physical property tests with DEM simulation. Reverse engineering techniques employing a 3D scanner were utilized to obtain the clam seedling contours. Subsequently, particulate model of the clam seedlings were developed based on the Hertz-Mindlin no-slip contact model with DEM framework. Free-fall collision method, inclined plane sliding method, and fixed inclined plane rolling method were used to calibrate the contact parameters between the clam seedlings and contact materials (EVA, stainless steel plate), respectively, and the results showed that: the coefficient of recovery of collision, static friction factor, and rolling friction factor between the clam seedlings and EVA were 0.339, 0.538, and 0.205, respectively. For clam seedling-stainless steel plate interactions, these parameters were 0.128, 0.323, and 0.177, respectively. The inter-seedling contact parameters were determined by the suspension collision method with MXZ-1 type friction coefficient meter. Taking the inter-seedling contact parameters as test factors, taking the relative error between the experimentally measured and DEM-simulated angle of repose served as indexes, a three-factor three-level orthogonal test was carried out, and taking the minimum relative error as the optimization objective, the optimization analysis of the test data yielded the following results: the coefficients of recovery of the collision between the clam seedlings and the shellfish seedlings, the static friction factor, and the rolling friction factor were 0.170, 0.620, and 0.313, respectively. The validation test of the calibration results was carried out through the lifting cylinder method, and the results showed that the relative error between the simulated and experimentally measured angles of repose was 3.6%, which proved that the calibration results were true and reliable. This study filled the gap of discrete meta-parameter calibration of shifting beach quadrangular clam shellfish seedling, and provides theoretical basis for the optimization of key components, such as silo inclination angle, drop port position and impeller disc, for the intelligent seedling sowing equipment.

To improve the accuracy of identifying the maturity of tobacco leaves at different harvesting stages, this study systematically collected images of under-ripe, ripe, and over-ripe tobacco leaves from the upper, middle, and lower parts of the plant. We proposed an improved VGG-16-based deep learning method for tobacco leaf maturity recognition. This method leverages a pre-trained VGG-16 model as its foundation, incorporating transfer learning, fine-tuning, and convolutional layer feature fusion to enhance the model's performance in tobacco leaf maturity recognition tasks. Freezing the convolutional and subsequent layers, adding BN layers, and using the Adam optimizer further improved training efficiency, avoided over fitting, and enhanced the model's robustness and accuracy. Experimental results showed that the improved VGG-16 model had a high accuracy advantage in tobacco leaf maturity recognition tasks, with a test set accuracy of 99.7%, surpassing classical machine learning methods such as BP neural networks, support vector machines, original VGG-16 and VGG-19, AlexNet, and ResNet50. The model comprised 14 721 353 parameters, a model size of 58.9 M, and a single image recognition time of 0.024 9 seconds, demonstrating the advantages of low computational and storage resource requirements and rapid recognition speed. Further visual analysis of the recognition results of tobacco leaf images with different maturities using the Score-CAM algorithm revealed that the central region of the main vein of tobacco leaves served as a primary differentiating feature for tobacco leaves with different maturities across different stalk positions, providing key information for the recognition model, there by clarifying the chemical substance transformation patterns occurring during the maturity process of tobacco leaves from distinct plant sections. The improved VGG-16 deep learning model proposed in this study exhibits high accuracy and efficiency in identifying the maturity of tobacco leaves at different harvesting stages. It is poised to provide precise and effective decision support for tobacco harvesting and production. Future research will focus on exploring alternative feature fusion strategies and network structures to further improve the generalization ability and robustness of tobacco leaf maturity recognition across different production areas and years.

To address challenges associated with inconsistent lodging degree and the irregular corm size during the harvest of tumorous stem mustard, which is difficult to determine the optimal harvesting forces, a specialized combine harvester was designed based on the special growth traits of this crop. An innovative harvesting methodology, involving initial pulling followed by root cutting, was adopted, and the dynamic simulation analysis of the key pulling device was carried out. Based on the analysis of the harvest process of mustard tuber and its stress state, the interaction force between the V-shaped cone roller of the extraction device and the mustard tuber was studied in detail. Static friction, elastic restoring force, and dynamic friction force at different stages of the plucking process were mathematically derived, so as to establish the dynamic model for corm extraction. To comprehensively analyze the interaction between the extraction device and the mustard tuber, and further optimize the key parameters of the device, a three-dimensional model of the mechanism was established using SolidWorks, subsequently simplified and imported into ADAMS software for dynamic simulation. Considering the application points and magnitudes of forces experienced by the mustard corm during extraction, a bushing force element was employed in simulate to represent the flexible connection between the tuber (modeled as a non-rigid body) and its root. This approach enabled the construction of a rigid-flexible coupling model representing the interaction between the mustard and the combine harvester's pulling device. Taking the pull-out force as the primary performance metric, the performance simulation tests of the pull-out process were carried out by selecting the opening spacing of the pull-out roll, the rotation speed of the pull-out roll and the driving speed as the test factors. The influence of different operating parameters on the pull-out force exerted by the pull-out device was elucidated. Analysis of the simulation results indicated no obvious linear relationship between the opening spacing of the pulling roller, the rotation speed and the extraction force. Furthermore, the stability of the extraction force was observed to decreases with the increases in both the opening spacing and the driving speed of the machine. The working efficiency of the extraction device decreases with an increase in opening spacing, and was found to be largely independent of the pulling rollers' rotational speed. The range analysis of the test results identified the optimal working parameter combination of the extraction device as: the opening spacing of the extraction roller 45 mm, the rotation speed of the extraction roller 120 r/min, and the driving speed 1.0 km/h. Under these conditions, the extraction force was the largest, the maximum value 487.2 N. The research results provide theoretical support for the design and optimization of the mustard combine harvester.

In order to ensure national food security and promote sustainable agricultural development, it is crucial to accurately predict regional rice yield by formulating effective food strategies. While advancemnets have been achieved by integrating remote sensing data with machine learning technology for yield prediction, a more in-depth analysis of the machine learning model mechanism and the application of high spatio-temporal resolution data remains necessary. This study utilized Sentinel and MODIS Normalized Difference Vegetation Index (NDVI) data along with county-level yield statistics from 2023 to implement a Random Forest (RF) model for predicting yield from county level to pixel level with case study of Sheyang unmanned farm in Jiangsu Province, and investigated the impact of different features on the model's learning mechanism. The results demonstrated that there was a significant correlation between NDVI data and rice yield during the period of August to October 2023, which is a critical period in the crop phenological cycle. The RF model effectively predicted rice yield at the county level, with a Root Mean Square Error (RMSE) value of 339.5 kg /hm². Furthermore, spatial distribution mapping of the predicted yields within Sheyang County for 2023 revealed significant heterogeneity, indicating lower yields in marginal regions and higher yields in central areas ranging from 9 000 to 9 300 kg/hm². This study not only enhances understanding of machine learning application and vegetation data in yield prediction but also provides theoretical support for improving model accuracy and formulating scientific food production strategies.

With the deepening advancement of sustainable development concepts in global animal husbandry, welfare-oriented broiler farming has emerged as a critical direction for industrial transformation. This study systematically analyzes the current status, core challenges, and future trends of global broiler welfare farming. It employs a multidimensional perspectives including policy, technology, and market dynamics, while comparing the practical approaches adopted in developed regions (e.g., the EU and the U.S.) and those in China. Research demonstrates that the EU has established a relatively comprehensive legislative framework for welfare farming through regulations such as *Council Directive 2007/43/EC*, which mandates controlled stocking density (33-42 kg/m²), optimized lighting and ventilation conditions, and the gradual adoption of slow-growing breeds (daily weight gain ≤50 g/d, slaughter age 49-81 days). These measures have significantly improved welfare indicators compared to conventional practices. In contrast, while the U. S. lacks unified federal legislation, certain states (e. g., California) have enacted laws like the Prevention of Farm Animal Cruelty Act to promote improvements in space allocation and health management for farm animals. Furthermore, leading industry stakeholders in the U. S. have also initiated piloting slow-growing breeds (50 g/d, slaughter age ≥56 days) and floor-rearing systems. China, though having released the Animal Welfare Requirements for Broilers (T/CAS 267-2017) group standard in 2017, still implements welfare farming practices predominantly confined to export-oriented enterprises. The white-feathered broiler sector remains dominated by high-density cage systems (≤33 kg/m²), while yellow-feathered broilers exhibit partial welfare enhancements through measures such as the provision of perches and litter. However, widespread adoption of comprehensive welfare practices in China is hindered by policy gaps, cost constraints, and limited consumer awareness. Technologically, intelligent farming equipment has become a key driver for welfare enhancement. Future trends indicate that an increasing consumer willingness to pay premiums for welfare-assured products will foster market segmentation. Concurrently, the integration of advanced technologies, including 5G connectivity, Artificial Intelligence (AI), and robotics, is expected to further reduce the costs of welfare farming practices. From a policy perspective, the EU plans to revise regulations to strengthen enforcement mechanisms, whereas China faces the imperative of developing and implementing localized welfare standards. This study provides theoretical and practical insights relevant to the sustainable and welfare-conscious transition of the global broiler industry. It recommends that countries adopt differentiated pathways based on their resource endowments and technological capabilities—developed nations are advised to reinforce policy constraints and technological innovation, while developing countries should aim to strike a balance between production efficiency and animal welfare through gradual standardization and market-oriented approaches.

The classification and recognition of tea buds represents a crucial aspect of renowned tea production. In view of the problems of large model size, large computational complexity and inability to distinguish the picking morphology of the current tea bud recognition algorithm, this study proposes an enhanced fresh tea leaf recognition model (YOLOv5s-SPCS) based on YOLOv5s as the foundational model. Firstly, images of fresh tea leaves were collected in both laboratory and natural environments to create a dataset of fresh tea leaves. This was done through offline and online collection of images in multiple scenarios, with the resulting images divided into a training set and a test set. Secondly, the Shuffle Block module was constructed based on the ShuffleNetV2 idea for replacing the convolution module in YOLOv5s backbone network, which reduced the number of model parameters and the amount of computation while increasing the speed of feature extraction. Subsequently, the Partial Convolution structure, PConv and SimAM were incorporated into the neck network to construct the C3-PCS module, replacing the original C3 structure which further reduced the model computational redundancy and memory access, while improving the recognition accuracy with a minimal increase in the number of parameters. Finally, the SIoU bounding box loss function was employed to enhance the convergence velocity and precision of the prediction frame. In addition to accelerating the convergence of the model prediction frame regression, the use of this loss function also generates more accurately positioned prediction frames. The experimental results demonstrate that the enhanced YOLOv5s-SPCS model exhibits 14%, 14% and 16% of the YOLOv5s model in terms of the number of parameters, computational volume and weight file. The size of the model is, respectively, with an accuracy of 81.8% and a mean average precision (mAP) of 82.4% for the fresh tea image recognition, which is 2.7% more accurate than the original model. The accuracy was enhanced by 2.7 percentage points, while the mean average precision of mAP remained unaltered. Furthermore, the overall performance of the enhanced YOLOv5s-SPCS model is superior to that of the prevailing target detection models, including Faster R-CNN, SSD, YOLOv3, and YOLOv4. This study offers a valuable technical foundation for fresh tea leaves recognition classification and subsequent mobile deployment.

The garlic combine harvester has essentially realized the mechanized harvesting of garlic. However, its intelligence level is relatively low. During the operation, problems such as yaw, overflow and blockage are prone to occur, which affect the operation quality and harvesting efficiency of the whole machine. To address these issues, this paper presents an STM32-Based real-time monitoring system for garlic combine harvesters, which can monitor the yawing, overflow, and blockage failure problems in real-time with high accuracy, and can also provide voice broadcast reminders. The system comprised a row assist modules, a yield monitoring module, a fault diagnosis module and an STM32 microcontroller. The row assist modules utilized an MPU6050 gyroscope to ascertain the direction of the harvester, which was then compared with the set direction angle warning and alarm threshold to determine whether the harvester had deviated from the normal operational route. The yield monitoring module multiplied the weight of a single garlic by the total number of garlic counts to calculate the total weight of the harvested garlic, and compared it with the set threshold of the full bin to ascertain whether the collection bin was indeed full. Fault diagnosis was performed by the Hall sensor, which collected the rotational speed of the follower wheel of the clamping and conveying mechanism and compared it with the set threshold to judge whether a blockage occurrs. The maximum error value of the row assist module, as determined through indoor commissioning, was 1.3°. The minimum error value was 0.2°. The yield detection module monitoring count accuracy was 96.7%. The fault diagnosis module identified the average error value of rotational speed as 0.8 r/min, with an error rate of 0.4%. This enabled the module to accurately monitor the follower wheel rotational speed changes when the clamping device was blocked. The results of the field test demonstrate that the designed monitoring system is feasible, accurate and stable. The findings of this study indicate that the proposed monitoring system can operate reliably over an extended period, issuing timely alerts when the garlic harvester is exhibiting signs of yawing, overflowing, and blocking issues. This could markedly enhance the operational quality and efficiency of traditional garlic harvesters.

With the advancement of agriculture, the economic losses and food safety threats caused by major plant diseases and pests to agriculture and forestry are becoming increasingly severe. In 2024, China's main food crops face challenges from 22 types of diseases and pests, with an expected affected area of 155 411 khm² and a yield loss exceeding 1.5×10¹¹ t. This article provides an overview of the current status of crop disease and pest warning and emergency control technologies, where disease and pest warning technologies include the Internet of Things, remote sensing technology, and computer vision recognition technology; emergency control technologies cover the understanding of chemical communication mechanisms for the development of new pesticides and equipment, innovation in physical extermination and intelligent control products, utilization and product innovation of biological control resources, research on pest damage-type variation mechanisms and sustainable use of plant resistance varieties, research and development of pesticide resistance mechanisms and control technologies, as well as research and application technologies for the ecological regulation of diseases and pests. The aim is to achieve effective control of diseases and pests through interdisciplinary cooperation, while reducing environmental impact and promoting sustainable agricultural development. The article also discusses the application of disease and pest warning and emergency control technologies in the agricultural field, analyzes their important role in the development of agricultural science and technology, and summarizes the challenges faced in each field, such as the insufficient accuracy of unmanned aerial vehicle remote sensing monitoring technology, difficulties in sample collection for disease and pest image automatic recognition technology, the failure of chemical control to achieve precise application, the lagging production processes and market promotion of green formulations, and the incomplete construction of disease and pest warning systems. In the future, the application of Internet of Things technology to disease and pest warning should be accelerated, the construction of smart agriculture and disease and pest warning systems should be promoted, and research should be conducted on unmanned aerial vehicle remote sensing monitoring technology, disease and pest image automatic recognition technology, chemical control technology, and biological control technology.