Search

Utilizing machine vision technology based on YOLOv5, a six-axis robot can quickly identify and classify targets. However, when the YOLOv5 model is used for the recognition and grasping of small workpieces, issues such as low precision and missed detections frequently occur. This paper proposes an enhanced object recognition algorithm, integrating a CBAM attention module and an improved loss function into YOLOv5 to control the hand–eye coordination of the six-axis robot during grasping. The CBAM attention module is incorporated into the backbone network of YOLOv5 to enhance its feature extraction capabilities, while the original loss function is modified to accelerate convergence and improve regression accuracy. An experimental platform for six-axis robot hand–eye coordination grasping was built, and grasping experiments were conducted. The proposed method significantly improves the robot’s grasping accuracy, with a 99.59% mAP0.5 and a 90.83% successful grasping rate, effectively addressing the challenges of low accuracy and missed detections in traditional systems.

Compared to traditional manual methods for assessing the cotton verticillium wilt (CVW) hazard level, utilizing deep learning models for foliage segmentation can significantly improve the evaluation accuracy. However, instance segmentation methods for images with complex backgrounds often suffer from low accuracy and delayed segmentation. To address this issue, an improved model, YOLO-VW, with high accuracy, high efficiency, and a light weight, was proposed for CVW hazard level assessment based on the YOLOv10n model. (1) It replaced conventional convolutions with the lightweight GhostConv, reducing the computational time. (2) The STC module based on the Swin Transformer enhanced the expression of foliage and disease spot boundary features, further reducing the model size. (3) It integrated a squeeze-and-excitation (SE) attention mechanism to suppress irrelevant background information. (4) It employed the stochastic gradient descent (SGD) optimizer to enhance the performance and shorten the detection time. The improved CVW severity assessment model was then deployed on a server, and a real-time detection application (APP) for CVW severity assessment was developed based on this model. The results indicated the following. (1) The YOLO-VW model achieved a mean average precision (mAP) of 89.2% and a frame per second (FPS) rate of 157.98 f/s in assessing CVW, representing improvements of 2.4% and 21.37 f/s over the original model, respectively. (2) The YOLO-VW model’s parameters and floating point operations per second (FLOPs) were 1.59 M and 7.8 G, respectively, compressed by 44% and 33.9% compared to the original YOLOv10n model. (3) After deploying the YOLO-VW model on a smartphone, the processing time for each image was 2.42 s, and the evaluation accuracy under various environmental conditions reached 85.5%, representing a 15% improvement compared to the original YOLOv10n model. Based on these findings, YOLO-VW meets the requirements for real-time detection, offering greater robustness, efficiency, and portability in practical applications. This model provides technical support for controlling CVW and developing cotton varieties resistant to verticillium wilt.

Vegetables are an essential part of people’s daily diet, and weeds can cause serious losses in vegetable yield and quality. Intelligent weeding technology for vegetables will be one of the mainstream technologies in modern agricultural development. This article reviews the current research status of intelligent weeding technology for vegetables, including vegetable and weed detection technology, weeding actuators, and weeding robots. Firstly, the vegetable and weed detection technology was introduced in detail from three aspects: global weed detection, crop-rows detection, and vegetable/weed precise recognition technology. The research results of some researchers were summarised, and the vegetable/weed precise recognition technology, including machine learning and proximal sensor technology, was introduced. Secondly, the weeding actuators and robots were introduced, including intelligent chemical weeding, mechanical weeding, physical weeding, and integrated weed management methods. Some weeding actuators and robots developed by researchers and agricultural companies were showcased. Finally, the challenges and future development directions of intelligent weeding technology were discussed and analysed. Intelligent weeding technology for vegetables is still mainly limited by natural conditions and a lack of technology. In the future, it will be possible to develop in the direction of multi-algorithm and multi-sensor fusion technologies. It is necessary to improve the applicability of intelligent weeding equipment for various environments, crops, and weeds. This article can provide a reference for future research in the field of intelligent weeding for vegetables.

Chlamydomonas reinhardtii polysaccharides (CRPs) are bioactive compounds derived from C. reinhardtii, yet their potential in cancer therapy remains largely unexplored. This study optimized the ultrasound-assisted extraction conditions using response surface methodology and proceeded with the isolation and purification of these polysaccharides. The optimal extraction conditions were identified as a sodium hydroxide concentration of 1.5%, ultrasonic power of 200 W, a solid-to-liquid ratio of 1:25 g/mL, an ultrasonic treatment time of 10 min, and a water bath duration of 2.5 h, yielding an actual extraction rate of 5.71 ± 0.001%, which closely aligns with the predicted value of 5.639%. Infrared analysis revealed that CRP-1 and CRP-2 are α-pyranose structures containing furoic acid, while CRP-3 and CRP-4 are β-pyranose structures containing furoic acid. Experimental results demonstrated that all four purified polysaccharides inhibited the proliferation of cervical (HeLa) hepatoma (HepG-2) and colon (HCT-116) cancer cells, with CRP-4 showing the most significant inhibitory effect on colon cancer and cervical cancer, achieving inhibition rates of 60.58 ± 0.88% and 40.44 ± 1.44%, respectively, and significantly reducing the migration of HeLa cells. DAPI staining confirmed that the four purified polysaccharides inhibit cell proliferation and migration by inducing apoptosis in HeLa cells. CRP-1 has the most significant inhibitory effect on the proliferation of liver cancer cells. This study not only elucidates the potential application of C. reinhardtii polysaccharides in cancer therapy but also provides a scientific basis for their further development and utilization.

The air-suction precision seeder for small seeds is a planting machine, characterized by precision, high efficiency, and ease of operation, that uses air suction technology to sow small grain seeds at set intervals and depths into the soil. However, the forced vibration, enhanced by the increase in the operating speed, affects the seeding accuracy of the seeder and limits the seeding efficiency. To study the influence of vibration conditions on the seed suction performance of the air-suction precision seeder, we developed a computational fluid dynamics–discrete element coupling method to construct a bidirectional fluid–solid coupling numerical simulation model of the seed suction process under vibration conditions. Within the range of operating speeds from 0.6 km/h to 8 km/h, we quantitatively studied the population movement under different vibration frequencies, vibration amplitudes, negative pressure values, and seeding disc speeds and verified the simulation model and its analysis results through bench tests. The numerical results show that the interaction between the vibration frequency, vibration amplitude, and negative pressure value has the most significant impact on the single-seed rate. In addition, via variance analysis and response surface analysis, the optimal range of negative pressure values for achieving high single-seed rates under different vibration frequencies (4~10 Hz), vibration amplitudes (3~7.5 mm), and seeding disc speeds (4~50 rpm) was determined. The results indicate that, rather than the higher the negative pressure value, the higher the seed suction rate, the optimal negative pressure value for achieving a high seed suction rate varies with the specific vibration frequencies and amplitudes.

The double-hole rice vacuum seed meter is critical equipment for the planting precision of rice direct seeding. The effects of shaped holes and seed disturbance on the precision of rice bunch planting were investigated to improve the precision of bunch planting with the double-hole rice vacuum seed meter. A test bench with the rice vacuum seed meter was set up to analyze the trends in the quality of feed index, miss index, and multiple index of seed meters with different shaped holes at different speeds and vacuum pressures. Based on the optimal hole structure, different seed disturbance structures were designed to investigate the influence of the seed disturbance structure on the precision of bunch planting. A multiple linear regression model was established for the relationship between the disturbance structure, vacuum pressure, rotational speed, and the precision of bunch planting. Discrete element numerical simulation experiments were carried out to analyze the effect of disturbance structures on seeds. The planting precision of the seed meter with the shaped hole was significantly higher than that of the seed meter without the shaped hole while the shaped hole B was the optimum structure. Disturbance structure affects the quality of feed index, multiple index rate, and miss index. The planting precision of the seed disturbance structure II was better than the other structures. At a speed of 60 rpm and vacuum pressures of 2.0 kPa, 2.4 kPa, and 2.8 kPa, the qualities of feed index of seed disturbance structure II were 90%, 91.11%, and 89.17%, respectively, and the miss indexes were 2.96%, 1.94%, and 1.57%, respectively. At high rotational speeds, the precision of rice bunch planting with the seed disturbance structure is better than that without the seed disturbance structure. In the simulation test, the seed velocity and total force magnitude of the meter without disturbance structures were less than those with the disturbed structure. Simulation experiments showed that the seed disturbance structure breaks up the stacked state of seeds. Research has shown that the shaped hole holds the seed in a stable suction posture, which helps to increase the seed-filling rate. Seed disturbance improves seed mobility, thereby enhancing the precision of bunch planting.

This investigation considered the effects of both internal and external excitation vibrations on the efficacy of the seed dispenser in a rice precision hole seeder. Through comprehensive field tests, we analyzed vibrational characteristics during direct seeder operations and established a vibration seeding test bed for systematic examination of these effects. Time-domain analysis of the vibration data revealed a predominantly vertical vibration direction, with notably higher levels in sandy loam soil compared to clay loam. A correlation was observed between increased engine size and rotary ploughing speeds, as well as forward speed and elevated vibration amplitudes. Frequency domain analysis pinpointed the primary vibration frequency of the machinery within the 0–170 Hz range, remaining consistent across different operating conditions. Crucially, bench test results indicated that seeding accuracy and dispersion were significantly influenced by vibration frequencies, particularly within the 70–130 Hz range, where a decrease in accuracy and increase in dispersion were noted. A regression model suggested a complex, non-linear relationship between seeding performance and vibration frequency. These insights highlight the necessity for a robust mechanism to effectively address these vibrational impacts. This study paves the way for enhancing the operational efficiency of the rice precision hole seeder, aiming to achieve the design goals of minimized vibrations in the paddy power chassis.

Weeds in paddy fields can seriously reduce rice yield. An intra-row weeding device with double-layer elastic rods was designed, considering the differences in mechanical properties between rice and weeds, which can press weeds into the soil and avoid damaging rice. The elastic force of the elastic rods can be adjusted by changing the position of the regulating mechanism to adapt to different weeding conditions. A measurement experiment was conducted to determine the variation rule of elastic force. The quadratic orthogonal rotation combination discrete element simulation experiment, which used weeding depth and weeding speed as experimental factors, and the amount of soil disturbance and the force of the inner and outer elastic rod in the horizontal and vertical directions as experimental indicators, was conducted to study the interaction between the weeding device and the soil. The optimal weeding parameters were obtained: the weeding depth was 15 mm, the weeding speed was 0.9 m/s. The field experiment, which used the various parameters of the weeding device as experimental factors and the weeding rate and damaging seedling rate as experimental indicators, was conducted to determine the weeding effect. The experimental results showed that the optimal position of the regulating mechanism was 270 mm, with a weeding rate of 80.65% and a damaging seedling rate of 3.36%. The weeding rate can be increased by at least 11.18% by adjusting the regulating mechanism to a suitable position under the same weeding conditions. This study can provide a reference for research on weeding machinery for organic rice.

To elucidate the vibrational characteristics of power chassis in paddy fields, we examined the Yanmar VPG6G rice transplanter across diverse terrains, including paddy fields, dry land, and concrete roads. Vibrational acceleration measurements, taken in longitudinal, transverse, and vertical orientations at key chassis locations, revealed noteworthy findings. The Mizuta power chassis exhibited its lowest root-mean-square (RMS) vibrational acceleration on concrete, while the highest was observed on paddy fields. The acceleration power spectra predominantly peaked between 1~14 Hz, with peak values amplifying as speed increased. Additionally, pendant orientation frequencies exceeded those of longitudinal and lateral directions. Both front and rear wheels mirrored the vibrational accelerations of the rear axle, but dynamic load coefficients for the front wheels consistently surpassed the rear, particularly at elevated speeds. This research not only enhances our understanding of terrain-induced vibrations and the intricate dynamics between terrain and tires but also lays the groundwork for designing optimized vibration-damping solutions tailored to prevalent road conditions.

The efficient identification of rice pests and diseases is crucial for preventing crop damage. To address the limitations of traditional manual detection methods and machine learning-based approaches, a new rice pest and disease recognition model based on an improved YOLOv7 algorithm has been developed. The model utilizes the lightweight network MobileNetV3 for feature extraction, reducing parameterization, and incorporates the coordinate attention mechanism (CA) and the SIoU loss function for enhanced accuracy. The model has been tested on a dataset of 3773 rice pest and disease images, achieving an accuracy of 92.3% and an mAP@.5 of 93.7%. The proposed MobileNet-CA-YOLO model is a high-performance and lightweight solution for rice pest and disease detection, providing accurate and timely results for farmers and researchers.

In order to improve the single-grain seeding rate of the pneumatic single seed metering device, an airflow seed cleaning device was designed in combination with positive pressure airflow. The influence of the position of the seed cleaning mechanism on the seed cleaning effect is theoretically analyzed and a flow field simulation test analysis of different nozzle structures was carried out by using Fluent software (ANSYS, Inc., Canonsburg, PA, USA). The results of this test show that a nozzle with a Witoszynski curve has good airflow concentration and uniform air pressure distribution. In order to verify the performance of the seed cleaning mechanism, a 0.7 times coated seed (hybrid rice Wuyou 1179) was used as the test material and a quadratic regression test with three levels was carried out with the rotation speed of the seed plate, the negative pressure of the suction chamber, and the positive pressure of the seed cleaning as the test factors. The results showed that when the speed of sucking plate was 30 r/min, the negative pressure of the suction chamber was 1.8 kPa and the positive pressure of the seed cleaning was 0.2 kPa; the seeding effect was at its best and the qualified rate of the seed metering device was the highest at 86.43%, the minimum leakage rate was 3.81%, and the multiple rate was 9.76%. The proposed seed cleaning mechanism effectively improves the accuracy of seeding and provides a certain theoretical basis for the single-grain sowing of hybrid rice.

Droplet characteristics are identified as essential factors in agricultural spray application. The aims of this study were to analyse the influence of spray parameters on droplet characteristics and to determine possible candidate sprays that would produce the same droplet size categorizations as the American Society of Agricultural and Biological Engineers (ASABE) standard S-572.1 for air induction nozzles (AINs). Six different orifice sizes of the Billericay Farm Services (BFS) air induction (AI) flat fan hydraulic nozzles (the air bubblejet) were examined at different spray pressures (200 kPa, 300 kPa, 400 kPa, 500 kPa, 600 kPa and 700 kPa) and concurrent air velocities (2 m/s, 3 m/s, 4 m/s and 5 m/s). The influences of spray parameters on the droplet characteristics were analysed using analysis of covariance (ANCOVA) and analysis of variance (ANOVA). Results showed that: (1) The values of droplet characteristics and the results of ANOVA were significantly different before and after eliminate the influence of dynamic surface tension (DST) on droplet characteristics by ANCOVA; (2) (a) the reduction rates of the droplet diameter sizes decreased with increasing spray pressure; (b) air velocities of 2 m/s and 5 m/s resulted in smaller droplets reports, and air velocities of 3 m/s and 4 m/s are more suitable for agricultural spray applications; (c) a larger nozzle orifice size not always result in a larger droplet size and (3) Fine, Medium, Coarse, Very Coarse and Extremely Coarse droplet classification categories as the ASABE S-572.1 standard categorizations were determined to classify AINs.