Traditional flat-bed rice nurseries in cold regions require large areas and suffer from inefficient tray movement and instability. A compact vertical rice seedling frame is developed in this work, which integrates a triangular-stabilized tray support, a four-point conveying mechanism, and anti-tilt/anti-torsion features, coupled with a dynamic lighting control strategy based on daily light integral (DLI). The mechanical design was validated by finite-element analysis under design loads, and collision-free operation was confirmed by digital mock-up simulation and prototype tests. In greenhouse trials comparing the vertical frame to conventional flat cultivation, the frame increased space-use efficiency to Delta P = 119.29% (i.e., approximate to 19.29% higher than the flat bench), while maintaining seedling coverage at 98%+/- 1% (ns) and stem diameter (ns), and producing significantly shorter (more compact) seedlings (p<0.05; a/b). These results demonstrate a practical pathway for high-density automated seedling production in cold climates using intelligent vertical systems.
Accurate prediction of pig growth is essential for feed planning and market decisions in precision pig farming, but farm data are often small and fragmented. To address this challenge, a biologically constrained machine learning framework is proposed to predict the time required for grower-finisher pigs to reach the target weight of 100 kg under small sample conditions. By applying biologically constrained modifications to machine learning models (SC-GAM and Monotone XGBoost), the constrained models outperformed unconstrained baselines, which achieved higher accuracy (RMSE <= 4.73 d, ACC +/- 7d >= 92%), greater robustness, and improved biological realism. An evaluation system was developed that combines traditional accuracy indicators with biologically grounded metrics. Practical applicability was examined via an on-farm shadow test on an independent batch. The models delivered reliable predictions that support finishing scheduling and feed-related management decisions. These findings highlight the potential of biologically constrained models to improve operational efficiency and reduce resource wastage in commercial pig farming.
In this study, to address the challenges of root-stubble crushed insufficiently and excessive disturbance of the soil on ridges, moving and fixed cutters imitating a cat's claw were designed for crushing stubble efficiently through reverse engineering technology. The multi-curve cutter was designed based on the distribution characteristics of roots in different soil layers, enabling efficient crushing of roots with minimal soil disruption. The EDEM was employed to build a root-stubble-soil discrete element model and simulate the crushing process of root-stubble. The accuracy of the result of the numerical simulation was validated through the field test. The qualified rate of root-stubble length, the rate of soil disturbance, and the power consumption of the device (PCD) would be increased with the increase of the rotational speed of cutter shaft (RSCS). The stubble, driven by the moving cutters, rotated around its root until a cutting support was established between the moving and fixed cutters, enabling efficient fragmentation. At a lower RSCS, the disturbed soil in the surface layer would fall back to the ridge. The soil in the middle and deep layers would also be collided and compressed by the soil in the surface layer, and a low rate of soil disturbance would occur. When the RSCS was 380 r/min, the rate of soil disturbance was the lowest, at 18.57%, and the qualified rate of root-stubble length could reach 91.07%, which could meet the requirements of agricultural technology.
Unmanned aerial vehicle (UAV) wind field airflow is the main factor affecting spraying width and operational effect. Under the soybean and maize compound planting mode, the width of the spray droplets of UAV aerial spraying is easily sprayed on other crops around the target crop by mistake due to the influence of the wind field. In order to solve this problem, a narrow-width spraying UAV equipped with an airflow guidance device was developed in this study, which can achieve precise spraying on target crops within a narrow operation width and reduce the impact of droplet drift on surrounding non-target crops. The wind field of the flight platform was simulated via simulation software, and the wind field distribution characteristics corresponding to three sizes of the guidance device were analyzed. It was verified that the guidance device has a segmentation effect on the wind field, and the optimal size of the guidance device was determined as 0.92 m accordingly. Meanwhile, the installation position and quantity of nozzles were determined to be set at the position with the minimum airflow disturbance. The wind speed at measuring points with different angles on four diameters at five heights under the UAV hovering state was tested through bench tests, to further verify the simulation results and appropriately adjust the nozzle position according to the measured wind speed. Outdoor flight spraying tests were carried out, and the results showed that when the 0.92 m guidance device was applied at a flight altitude of 1.00 m, the effective spray width was only 1.46 m according to the droplet deposition density on the coated paper of three test collection belts. Under the maize-soybean composite planting pattern, the field spraying test yielded results of 18.0%-30.0% deposition rate in the maize area and 0.1%-1.6% in the soybean area with maize as the operational target. A predictive effect of wind field simulation on the installation position of nozzles where droplets suffer the least wind field disturbance under aerial spraying conditions was confirmed. The UAV wind field can be effectively segmented by the airflow guidance device, and the diameter of the droplet-laden airflow column can be reduced, thus realizing narrow-width spraying.
Despite their transformative potential, large language models (LLMs) remain underutilized in agriculture due to domain-specific data scarcity and computational constraints. This study presents CAMAGRI-GPT, a parameter-efficient agricultural consultation system that addresses these critical challenges through innovative domain adaptation. A corpus of 2.3 million annotated entries was constructed from raw documents (kappa=0.82 agreement, 18 categories) and employed LoRA (r=8) and P-tuning v2 to reduce trainable parameters to 0.2% while maintaining 95.8% performance. The RAG framework with HNSW indexing achieves (87 +/- 12) ms retrieval latency, enabling real-time consultation. CAMAGRI-GPT demonstrated over 90.0% accuracy across three representative agricultural tasks (crop management, pest and disease diagnosis, and agricultural Q&A), consistently outperforming GPT-3 and BERT-Agri baselines, p<0.001. Median response latency remained below 2 s across all query categories, meeting field deployment requirements. These results demonstrate that domain-adapted LLMs can effectively deliver expert-level agricultural knowledge to resource-constrained farming communities, offering scalable and sustainable solutions to complement declining traditional extension services.
Investigating the interplay between biotic stress from downy mildew and abiotic stress from nitrogen deficiency is crucial for improving crop management measures and enhancing cucumber production. A greenhouse experiment was conducted using two pathogen treatments-non-infected (B1) and infected with Pseudoperonospora cubensis (B0)-across three nitrogen levels: deficiency (N1: 50%), optimal (N2: 100%), and excess (N3: 150%). The resulting six treatment combinations (B1N1, B1N2/Control, B1N3, B0N1, B0N2, and B0N3) revealed that downy mildew infection and nitrogen application rates significantly influenced key physiological and biochemical parameters (p<0.05). These included sucrose, soluble sugar, hydrogen peroxide (H2O2), catalase (CAT), superoxide dismutase (SOD), polyphenol oxidase (PPO), and malondialdehyde (MDA), as well as the fresh and dry weights of the leaves, stems, and roots. Among all groups, the combination of infection and nitrogen deficiency (B0N1) had the most significant impact on biomass accumulation and hormone metabolism. Compared to the B1N2 control, B0N1 led to substantial reductions in sucrose (52.83%), soluble sugar (68.67%), leaf fresh weight (56.67%), leaf dry weight (55.51%), stem fresh weight (52.82%), stem dry weight (57.28%), root fresh weight (32.46%), and root dry weight (54.07%). This study clarifies the interactive physiological responses of cucumbers to combined biotic and abiotic stress. It is of great significance for facilitating the control of downy mildew and the improvement of cucumber yield in sustainable agriculture.
In the agricultural domain, agricultural machinery trajectory operation mode identification is essential for spatiotemporal trajectory processing. Its goal is to classify machinery trajectories into road travel or field operations by extracting latent spatiotemporal features. However, conventional models lack effective feature enhancement and ignore the varying importance of trajectory points, thereby weakening feature representation and reducing identification accuracy. To overcome these challenges, a self-supervised learning-based GAT is proposed for identifying agricultural machinery trajectory operation modes. First, to enhance trajectory feature representation, a multi-dimensional feature enhancement module is introduced based on statistical methods. To mitigate the impact of redundant features on model performance, a bidirectional feature fusion module is subsequently proposed that captures both interpoint and intrapoint dependencies, thereby enhancing spatiotemporal representations and suppressing irrelevant information. Next, to capture the importance of trajectory points, a graph attention network (GAT) with a masked attention mechanism is introduced. Finally, to reduce the dependence on labeled data and improve the model's feature learning capability, self-supervised learning is used as a pretraining step for the GAT. To evaluate SSL-GAT, experiments are conducted on two real-world paddy and wheat harvester datasets. For the paddy dataset, SSL-GAT reaches 95.92% accuracy and a 92.42% F1-score, exceeding those of GAN-BiLSTM by 4.67% and 5.24%, respectively. On the wheat dataset, it achieves 93.92% accuracy and a 90.72% F1-score, with gains of 5.58% and 4.49% over GAN-BiLSTM. These results collectively demonstrate that our SSL-GAT model achieves superior performance, establishing it as a new state-of-the-art model in agricultural machinery trajectory operation mode identification.
Conventional feeders can achieve timed and quantitative feeding, but they cannot optimize feeding strategies on the basis of actual aquaculture conditions. This study evaluated the feeding intensity of largemouth bass and developed an intelligent feeder to achieve efficient and precise feeding. A mobile feeding system was built by designing and simulating the structure of the data acquisition, control, feeding power, storage, and mobile modules of the feeder. The surface water pressure signals during largemouth bass feeding were collected through pressure sensors and analyzed, and the feeding intensity was classified into three levels: strong, weak, and none. Signal features were extracted to construct a dataset and input into five machine learning models for optimal parameter tuning. The precision, recall, F1 score, and average accuracy of the random forest model were 96.2%, 95.5%, 95.6%, and 93.4%, respectively. The YOLOv5 model was adopted to detect remaining feed on the water surface. The feeding system was designed to enable the feeder to automatically track and provide feed into the tank. Experiments were conducted on the intelligent feeding system, with the feed residue rate as the indicator of the practicality of the feeding system. Verification experiments were also performed on eight tanks, and the average feed residue rate was less than 3%, proving that the feeding system has good practicality in actual aquaculture environments.
The military application of artificial intelligence (AI) is advancing far faster than global governance, with AI-assisted targeting, reconnaissance, and autonomous systems already deployed in active conflicts such as those in Ukraine and the Middle East. It not only restructures the tempo and scale of warfare but also brings profound ethical and operational risks, including algorithmic opacity and the excessive dilution of meaningful human control over lethal military decisions. Existing international humanitarian law and voluntary corporate commitments are insufficient to address these challenges. Drawing on lessons from historical arms control regimes, this article calls for an urgent global binding convention to regulate AI warfare, with scientists and international mechanisms like the Convention on Certain Conventional Weapons (CCW) playing key roles in promoting rule-making.
The conventional tiger nuts harvesting method suffers from several drawbacks, including low harvesting efficiency, high levels of impurities, significant loss rates, and excessive labor intensity. To address these issues, a novel operational approach of sand removal first and then separatingwas introduced. A tractor-mounted tiger nuts combine harvester was designed to perform the tasks of excavation, sand removal, and rhizome separation of tiger nut tubers in a single operation. The machine's overall structure and operational principles are described. It then details the design of critical components, such as the combined vibration digging device, the conveying and sand removal device, the cylinder screening device, and the windscreen cleaning device. The structural and dynamic parameters of these key components were established through an analysis of the mechanism's motion and the material flow during the combined vibration digging, conveying sand removal, cylinder screening, and wind-screen cleaning processes. Field trials demonstrated the harvester achieved an average loss rate of 4.88%, the average impurity rate (excluding gravel content) of 7.52%, and an average digging depth of >= 12.5 cm. Throughout the testing phase, the machine operated reliably, and the results fulfilled the operational requirements for the tiger nut harvester.
Multi-picking of spoon-chain type seed-metering devices in potato planters causes multi-seeding, which leads to yield loss and plant diseases. This study developed a dedicated test bench for multi-picking detection and air-blowing excess seed removal, which integrated a detector based on spatial capacitive sensor, an air-blowing excess seed remover, and a controller. The feasibility of multi-picking identification via maximum net capacitance change was verified through theoretical analysis and Maxwell simulation. A two-point air-blowing strategy was adopted for air-blowing excess seed removal. Taking the success rate of air-blowing excess seed removal as the evaluation index, bench tests and parameter optimization were conducted with airflow speed for air-blowing excess seed removal, potato spoon depth, and seed-metering chain speed as variable factors. The optimal parameters were determined as an airflow speed of 31.34 m/s, a potato spoon depth of 19.67 mm, and a seed-metering chain speed of 0.29 m/s. Under this optimal parameter combination, the actual success rate of air-blowing excess seed removal reached 85.5%. This test bench provides a practical technical solution for solving multi-seeding in seed potato precision seeding, and lays a solid foundation for the optimization of seeding processes and the popularization of seed potato precision seeding technology.
Xisha watermelon, one variety of selenium-rich economic crops, has been widely cultivated in the semi-arid regions of northwest China. It is critical to estimate its yield for the decision making on the harvesting and market. This paper presents a yield estimation model for Xisha watermelon using drone remote sensing in digital agriculture. Firstly, the watermelon weight was estimated in three steps: object detection with YOLOv8n, contour fitting with the functions from the OpenCV library, and weight estimation of individual watermelon through a volume-to-weight model. Then, two yield estimation strategies were developed. 1) Sampling: the total yield of watermelons over the entire plot was calculated using the average yield within sampled units and the plot area. 2) Global scanning: an overall yield distribution of watermelons was obtained to scan the entire plot using orthoimagery. Finally, a series of field tests was carried out to verify the estimation in plantations. The results reveal that the average accuracy of the detection model was 0.986 using YOLOv8n. Once the number of watermelons exceeded 45, the relative error between the total estimated and the measured weight was less than 1.00%. The speed of sampling was 27.37 m2/s for a 9000 m2 field size of Xisha watermelon, approximately 50 times higher than that of global scanning. Compared with global scanning, the sampling-based estimation underestimated the count by 1.77% and the total weight by 5.10%, both of which fall within an acceptable range. Each estimation can be suitable for the specific scenarios of application. The sampling can be expected to provide the higher efficiency for the total field yield. While the global scanning can effectively represent the overall yield distribution of Xisha watermelons in the field. This study provides a new research approach and direction for fruit and vegetable yield estimation in precision agriculture based on UAV remote sensing technology.
Aiming at the problems of unclear potato movement trajectory and serious force impact, this study took the potato bagging device with buffer roller as the object of research. Through single-factor experiments, it analyzed how the roll diameter, conveyor speed, feed amount, and size of the potato collection bag affected the force of potatoes. Based on the EDEM-RecurDyn coupled simulation, it applied the Box-Behnken experimental method to conduct a three-factor orthogonal experiment on the operating parameters of the device, which took the maximum compressive force of the potato and the maximum kinetic energy as the experimental indices, and took the roller diameter, conveying speed, and feeding amount as the experimental factors. Establishing a quadratic polynomial regression model by using Design Expert software, the regression model was optimized to obtain the best combination of parameters as follows: roll diameter was 212 mm, conveying speed was 0.9 m/s, and feeding amount was 30 t/h. The verification experiment was conducted by using a potato impact recording device, and the results showed that under the condition of the optimal parameter combination, the maximum compressive force of the electronic potato was 238.854 N when bagging, which was close to the theoretical value after parameter optimization, with an error of 5.40%. The rate of damage to potatoes and skin-breaking rate were 0.98% and 1.86%, respectively. The research results can provide support for the study of the motion trajectory, impact force characteristics, and loss reduction of potatoes during subsequent bagging operations.
The daylily (Hemerocallis citrina Baroni) is an herbaceous perennial whose flowers are rich in nutritional and functional components. It is typically cultivated in rows but harvested manually, a labor-intensive process that this study aims to automate by developing a robotic harvesting system. A critical component of such a system is autonomous navigation, which poses significant challenges in unstructured field environments due to changing natural light, randomly distributed weeds, and varying inter-row density. To address these challenges, this study adopted vision-based navigation technology and proposed a guidance directrix detection algorithm. The proposed approach begins with converting the color model from RGB to HSV to decouple the brightness, thereby mitigating the impact of natural light variations. Morphological dilation is applied to suppress noise from weeds in the inter-row regions. Furthermore, an innovative coarse segmentation strategy based on hue and saturation is introduced to handle the problem of different sparsity in the inter-row. Finally, the inter-row axis is accurately extracted by employing concepts from physics, namely, the center of mass and moment of inertia. Experimental results demonstrate an accuracy of 98.25%, a recall of 100%, an average navigation processing time of 8.7521 ms, and a compact model size of only 18 KB. These findings empirically confirm that the proposed approach achieves high precision and real-time performance under unstructured, uncertain, and dynamically changing field conditions. Additionally, the algorithm operates with high computational efficiency and requires neither expensive hardware nor large-scale training datasets.
In order to construct a discrete element bonding model for columnar granular organic fertilizer with different moisture contents, this study calibrated the model parameters based on the Bonding model in the EDEM software. The maximum load-displacement data of the organic fertilizer were determined through uniaxial compression tests, establishing the relationship between moisture content and maximum load. Significant parameters were screened using the Plackett-Burman design, and parameter combinations were optimized through steepest ascent and Box-Behnken experiments. A mathematical model was established relating the maximum load to the Bonding parameters (normal stiffness, tangential stiffness, critical normal stress, critical tangential stress, and bonding radius) based on uniaxial compression simulation tests. Furthermore, a model was developed to predict the Bonding parameters based on the moisture content of the organic fertilizer, and the model's accuracy was verified through experimental validation. The results show that the average error of the maximum load predicted by the model is 1.98%, which allows for the accurate construction of the bonding model for slurry granular organic fertilizer with different moisture contents, laying a foundation for the simulation study of the interaction between organic fertilizer and working components.
Fruits are easily damaged by collision during mechanical harvesting of blueberries, affecting quality. In this study, theoretical analysis, numerical simulation, and experimental verification are comprehensively employed to investigate the mechanical behavior and damage mechanism of blueberry fruit collisions. A nonlinear collision dynamics model for fruits was constructed based on Hertzian theory; the collision force is found to be related to the mass, the coefficient of restitution, the material properties of the collided object, and the collision velocity. A discrete element simulation model was created, as was a finite element simulation model. The fruit collision type was determined, and the analysis showed that when the collision velocity of the fruit-rigid plate collision model was increased from 1 m/s to 3 m/s, the peak contact stress increased from 64 000 Pa to 110 000 Pa, the peak collision force increased from 1.85 N to 6.75 N, and the damage volume increased from 152.72 mm3 to 459.42 mm3, which is more likely to trigger compared to the fruit-fruit collision model plastic deformation. Through the collision test it was determined that blueberry fruit can withstand a maximum collision force in the range of 2.003.00 N. More than 300 mm of drop height will cause the damage surface ratio to increase dramatically. The results of this study identified the key factors and thresholds affecting damage, providing a theoretical basis for reducing the damage rate of blueberry fruit and also providing a reference for the mechanical harvesting of other berry fruits.
To analyze gene function in plants lacking a stable genetic transformation system, virus-induced gene silencing (VIGS) is essential. Sunflower (Helianthus annuus), the world's fourth most important oil crop and a significant cash crop in China, was selected as the experimental material. Three specific fragments of the H. annuus phytoene desaturase gene (HaPDS) were amplified and cloned into the pTRV vector to construct the recombinant vector pTRV-HaPDS for VIGS. Confectionery sunflower cultivars Huiyuan119, LD5009, and 3936, as well as oilseed sunflower cultivars R5 and GK2002, were used to optimize silencing conditions. Agrobacterium tumefaciens-mediated infiltration was performed, and HaPDS gene silencing was evaluated by considering factors such as the position of the silencing fragment within the gene, vacuum treatment, A. tumefaciens co-cultivation time, A. tumefaciens concentration (OD600), and plant cultivation temperature. Silencing efficiency was statistically analyzed, and HaPDS expression levels were quantified by qPCR. After various trials, it was determined that seed vacuum infiltration followed by 6 h of co-cultivation produced the most effective VIGS results. The optimal conditions involved silencing the HaPDS3 fragment near the 3 ' end, an OD600 value of 1.0, and a plant ambient temperature of 22 degrees C, resulting in relatively high silencing efficiency in Huiyuan119 and GK 2002. This study aims to develop an efficient and stable gene silencing system for sunflower, establishing a foundation for the subsequent validation of gene functions in this species.
This study sought to construct and empirically validate a discrete element method (DEM) particle model representing post-tillage soil blocks. This model was developed to facilitate a detailed examination of granular movement and contact mechanics during the shaping process of planting chambers for rape plants. The research specifically targeted the sticky, cohesive soil prevalent in rice paddy fields of the middle and lower Yangtze River region. Simulations were conducted using EDEM software to improve the accuracy with which soil-tool interactions are predicted for the design and optimization of mechanical transplanters. The physical and bonding parameters of the sticky soil were calibrated using the Hertz-Mindlin with Johnson-Kendall-Roberts (JKR) contact model and Hertz-Mindlin with Bonding contact model. A particle replacement method was adopted to create a discrete element model of cohesive soil aggregates with different shapes and sizes after rotary tillage. The accumulation angle of soil aggregates was used as the evaluation index in both the simulation and physical experiments. Design-Expert software was used to design a four-factor, three-level simulation experiment to identify the optimal parameter combinations for the physical and mechanical properties of the sticky soil and the JKR contact model, which comprised a soil-soil static friction coefficient of 0.32, soil-soil rolling friction coefficient of 0.10, soil-steel static friction coefficient of 0.51, and surface energy of soil for the JKR model of 5.50 J/m2. Next, the steepest climbing test and BoxBehnken orthogonal combination test were then used to narrow down the range of values for the significant factors and identify the optimal parameter combinations for the bonding contact model parameters, which included a bonding bond normal contact stiffness of 2.1 & times;106 N/m, a bonding bond tangential contact stiffness of 2.2 & times;106 N/m, a normal ultimate stress of 0.55 MPa, a tangential ultimate stress of 0.55 MPa, and a bonding radius of 12 mm. Field experiments were conducted using a flat box device to measure the soil evenness and firmness after ridge formation and compaction by a rotary tiller. The results of these experiments were compared with the discrete element simulation optimization results. The relative errors between the field test results and the simulation test results for soil flatness and compaction were 10.7% and 9.8%, respectively, which indicated good accuracy of the parameters calibrated and optimized by EDEM discrete element simulation software. Overall, this research can provide a reference for understanding the working mechanism and optimizing the parameters of soil touching components in rape transplanting equipment.
This study introduces high hydrostatic pressure (HHP) as an innovative non-thermal technique to simultaneously enhance preservation and extract phenylethanol glycosides, the primary bioactive compounds in fresh-cut Cistanche deserticola. Treatments ranging from 100 to 300 MPa significantly prolonged microbial shelf-life, improved tissue integrity, and increased phenylethanol glycoside yields by up to 34.17%, with optimal accumulation occurring between 9 and 12 d of storage. Microstructural analysis indicated that HHP induced controlled permeability of cell walls, facilitating the release of bioactive compounds without compromising cellular viability. Additionally, HHP stimulated crucial physiological responses such as the activation of phenylalanine ammonia-lyase and enhanced energy metabolism, thereby promoting the biosynthesis and accumulation of the target glycosides. These results illustrate that moderate HHP treatment effectively balances microbial safety, structural quality, and bioactive enhancement, providing a sustainable bioprocessing approach to enhance the value of medicinal plant-based products.
To solve the problem that there are many human factors, great difficulty, and low efficiency in distinguishing quinoa seeds from weed seeds and distinguishing the quality of quinoa seeds by appearance, a method of quinoa seed detection and classification based on computer vision is proposed. In this study, convolutional neural network and Vision Transformer (ViT) were used to quickly and nondestructively classify different quinoa seeds and weed seeds. The dataset used in this experiment was 1440 sample images containing quinoa seeds and weed seeds, which were divided into training set, test set, and validation set at a ratio of 8:1:1. The training set was 1152 pieces, test set was 144, and the validation set was 144 pieces. The convolutional neural network model and ViT model based on deep learning were established. The results show that the average classification accuracies of MobileNet, VGG16, ResNet50, and ViT models used in the experiment are 93.75%, 90.97%, 93.75%, and 98.61% respectively. The accuracy of ViT classification is much higher than that of convolutional neural networks, establishing a benchmark for quinoa seed classification. This study provides a reproducible dataset construction method and a dual-imaging strategy, and demonstrates practical deployment value for automated seed grading and purity testing.
