Search Results (83)

The Campos Rupestres, ancient and nutrient-poor mountaintop ecosystems in Brazil, harbor exceptional biodiversity and endemism but face severe threats from mining and urban expansion. Native grasses (Poaceae), represented by nearly 300 documented species—many of them poorly studied—are fundamental elements of these ecosystems. They provide critical ecological services, including soil stabilization, enhancing carbon storage and nutrient cycling, regulating water availability, and resilience to disturbances. This review synthesizes current knowledge on the diversity, functions, and propagation of Campos Rupestres grasses, with emphasis on their potential in ecological restoration. Despite their ecological importance, large-scale use of native grasses remains incipient, constrained by limited knowledge of reproductive biology, low seed viability, and scarce commercial seed availability. Advances in propagation include seedling and plug production, vegetative propagation, and rescue/reintroduction strategies, which have shown promising results in post-mining restoration. However, reliance on seed collection from natural populations risks depleting already limited genetic resources, highlighting the need for ex situ production systems. Expanding research on taxonomy, ecology, and cost-effective propagation methods, alongside supportive policy and market development, is crucial for integrating native grasses as cornerstone species in restoration programs. Bridging these gaps will enhance biodiversity conservation and restoration in one of the world’s most threatened megadiverse systems. Full article

To accommodate the downward-pressing seedling-picking method, this study designed a combination-type plug tray composed of a bottomless plug tray paired with a seedling base plate. The effects of peat ratio, substrate filling ratio, and nutrient solution concentration on pepper plug-seedling performance were evaluated through cultivation experiments and physical–mechanical tests, using seedling vigor index, root biomass, compressive yield strength, and substrate fragmentation rate as assessment indicators. In addition, soil-block detachment force tests were conducted to examine the influence of substrate moisture content on seedling-picking resistance. The results showed that seedling vigor index, root biomass, and compressive yield strength first increased and then declined as the levels of the three factors increased. Higher peat ratios and greater nutrient solution concentrations significantly reduced substrate fragmentation rates. The detachment force of the soil block was highly sensitive to moisture content, with an average value of 6.8 N when the substrate moisture content ranged from 65% to 75%. Based on a comprehensive evaluation approach, the optimal cultivation parameters were determined to be a substrate ratio of 3:1:1, a compaction coefficient of 1.2, and a nutrient solution concentration of 4.0‰. These results provide technical support for optimizing combined plug-seedling cultivation and for the design and parameter determination of seedling-picking mechanisms. Full article

Leaf area estimation using multispectral imaging in grafted cucumber seedlings is often underestimated due to leaf overlap at later growth stages. This study investigates the use of multispectral imaging technology to estimate leaf area in grafted cucumber seedlings and proposes a method to improve estimation accuracy by introducing “days after grafting” (DAG) as a correction variable. For the experiments, the scion varieties ‘Goodmorning Backdadagi’, ‘NakwonSeongCheongJang’, and ‘Sinsedae’ were grafted onto the same rootstock ‘Heukjong’ (Cucurbita ficifolia), and images were acquired at 7, 14, and 21 days after grafting. The results show that including DAG as a correction variable significantly enhances the accuracy of image-based leaf area estimation, particularly in plug tray units, where R² increased from 0.89 to 0.96 for ‘Goodmorning Backdadagi’, by effectively reducing errors caused by leaf overlap. Across all three varieties and both seasons (spring and summer), models incorporating DAG consistently showed higher accuracy in leaf area estimation than models without DAG. These results suggest that the method’s broad applicability is validated through comparisons across different seasons and varieties. Overall, this study provides a practical and accurate method for correcting leaf area estimation, with strong potential for application, particularly in seedling production and cultivation management. Full article

To improve the mechanized transplanting efficiency for large-scale broccoli production in Ningxia, this study aims to identify key morphological traits of seedlings suitable for mechanized transplanting. A Box–Behnken design was used to set three experimental factors, broccoli variety, seedling age, and plug tray specification, to evaluate their effects on seedling plant type (plant height, stem diameter, canopy diameter, stem inclination angle, and plant type cone angle) and root system characteristics (substrate loss rate). The results showed that plug tray specification was the primary factor affecting substrate loss rate, followed by variety and seedling age. Seedling age was the dominant factor affecting plant height, stem diameter, and canopy diameter, while plug tray specification primarily influenced stem inclination angle. Optimization via response surface methodology (RSM) indicated that the best transplanting performance was achieved with the “Hannai Youxiu” variety (excellent cold tolerance), 30-day-old seedlings, and 72-cell or 98-cell plug trays. Field validation confirmed that under these optimal parameters the mechanized transplanting feeding rate reached 100%, the seedling missing rate was 2.5%, and the transplanting qualification rate was 97.5%, with all RMSE values being less than 7.5%. These findings provide a scientific basis for the mechanized transplanting of broccoli in Ningxia, recommending the “Hannai Youxiu” variety and 98-cell plug trays with 30-day-old seedlings to enhance transplanting quality and production efficiency. Full article

Labor shortages and reliance on manual seedling transplanting constrain pepper production from meeting market demand. To address this mechanization gap, the development of new agricultural machinery is an urgent priority. This study presented kinematic modeling and field validation of an automatic link-driven hopper planting unit for a 3.4 kW walking-type pepper transplanter under development. Kinematic behavior of the hopper was analyzed through mathematical modeling and dynamic simulation and validated under actual transplanting conditions under ridge-patterned field. The optimal design (crank length: 75 mm; 60 rpm) achieved a stable elliptical trajectory that enabled synchronized seedling pickup, tray release, and soil deposition while maintaining vertical alignment. Under this setup, the hopper followed a stable elliptical trajectory (166.88 mm × 318.81 mm), with supply and deposition coordinates of approximately (321 mm, −322 mm) and (293 mm, −617 mm), and peak velocities and accelerations within 0.47 m/s and 1.68 m/s², respectively. Field results showed that the proposed mechanism enabled reliable transplanting performance, achieving a mean planting depth of 27.06 ± 8.18 mm and an uprightness angle of 80.03 ± 7.56°, which fall within agronomic requirements for early pepper establishment. The overall defect rate was low (7.17 ± 3.73%), leading to a 92.83 ± 3.73% success rate at a throughput of 24 seedlings min⁻¹. Variety-dependent responses were observed: Kaltan seedlings exhibited lower defect rates and greater stability than Shinhung seedlings, highlighting the importance of plug strength and stem rigidity in automated systems. These results demonstrate that the mechanism supports fully automated transplanting with acceptable agronomic quality and provides practical design guidance for advancing mechanized pepper production. Full article

Labor shortages in seedling nurseries, particularly in manual inspection and replanting, hinder operational efficiency despite advancements in automation. This study aims to develop a cost-effective, GPU-free machine vision system to automate the detection of deficient seedlings in plug trays, specifically for small-scale nursery operations. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV’s Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV’s Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. Under controlled laboratory conditions, the DDRP-Machine achieved high detection accuracy (96.0–98.7%) and precision rates (82.14–83.78%). Benchmarking against deep-learning models such as YOLOv5x and Mask R-CNN showed comparable performance, while requiring only one-third to one-fifth of the cost and avoiding complex infrastructure. The Batch Detection (BD) mode significantly reduced processing time compared to Continuous Detection (CD), enhancing real-time applicability. The DDRP-Machine demonstrates strong potential to improve seedling inspection efficiency and reduce labor dependency in nursery operations. Its modular design and minimal hardware requirements make it a practical and scalable solution for resource-limited environments. This study offers a viable pathway for small-scale farms to adopt intelligent automation without the financial burden of high-end AI systems. Future enhancements, adaptive lighting and self-learning capabilities, will further improve field robustness and including broaden its applicability across diverse nursery conditions. Full article

To address the challenge of improving the accuracy and efficiency of automatic transplanting operations in pepper plug seedling transplanters, this study innovatively designed a follow-up seedling picking and depositing mechanism. The core innovation lies in the synchronization of the seedling picking claws with the moving seedling cups, which was achieved by coordinating the motion speeds of the seedling picking and depositing mechanism with the seedling conveying mechanism. This synchronization ensured relative spatial stillness during seedling deposition, significantly enhancing seedling depositing accuracy. To meet the design requirements of this follow-up mechanism, this study presents a comprehensive design of the transplanter, including a three-dimensional model. Key mechanisms, namely the seedling picking and depositing mechanism and the seedling conveying mechanism, were thoroughly analyzed, with detailed explanations of their working principles. The transmission system was designed for reliability and stability, being towed by a tractor with the ground wheel driving the motion of the seedling conveying and distributing mechanisms. The motion mode of the seedling picking and depositing mechanism combined a crank–rocker mechanism and a crank–slider mechanism, utilizing a gear transmission rod for seedling picking and carrying actions, and rail guidance for follow-up seedling depositing. Experimental results validated the effectiveness of this design. In bench tests, the success rates of the seedling picking and depositing mechanism at operating speeds of 100 seedlings/min, 150 seedlings/min, and 200 seedlings/min were 97.4%, 98.44%, and 95.03%, respectively. In field tests, at operating speeds of 90 seedlings/min, 120 seedlings/min, and 150 seedlings/min, the planting success rates were 99.65%, 94.95%, and 89.18%, respectively. These results demonstrated that the follow-up seedling picking and depositing mechanism met the demands of automatic transplanting operations, offering an effective solution to enhance both the operating speed and quality of the transplanter. Full article

Existing force-measuring devices lack versatility in studying the dynamic coupling process between the seedling-picking device and the plug seedling pot during automatic transplanting. This research developed a universal force-measuring device featuring a centrally symmetrical clamping needle layout and a simultaneous insertion and clamping mechanism. The force-measuring device enables the flexible adjustment of the number of clamping needles (2/3/4 needles) via a modular structure. It can also modify the insertion depth and angle of the clamping needles to accommodate three specifications of plug seedlings, namely 50-hole, 72-hole, and 128-hole plug seedlings. A real-time monitoring system with dual pull-pressure sensors is integrated to precisely acquire the dynamic response curves of the clamping force (F_J) and the disengaging force (F_N) of the plug seedling pot during the seedling-picking process. Taking water spinach plug seedlings as the research object and combining with EDEM-RecurDyn coupling simulation, the interaction mechanism between the clamping needle and the plug seedling pot was elucidated. The performance of the force-measuring device was verified through systematic force-measuring experiments. The main research findings are as follows: The force-measuring device designed in this study can successfully obtain the mechanical characteristic curve of the relevant seedling plug pot throughout the automatic seedling-picking process. The simulation results show high consistency with the experimental results, indicating that the force-measuring device can effectively reveal the dynamic coupling process between the seedling-picking device and the plug seedling pot. The verification experiment demonstrates that the force-measuring device can effectively quantify the mechanical properties of the of plug seedling pots under different plug seedlings specifications and different clamping needles configurations. Reducing the hole size and increasing the number of clamping needles can effectively decrease the peak value of the disengaging force (F_Nmax). The peak clamping force (F_Jmax) is approximately inversely proportional to the needle number, with the four-needle layout providing the most uniform force distribution. The force-measuring device developed in this study is functional, applicable, and versatile, offering a general force-measuring tool and a theoretical foundation for optimal seedling-picking device design. Full article

With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article

To address the issues of bulky structure and complex transmission systems in current transplanters, a compact, electric-driven automatic transplanter was designed. Using pepper plug seedlings as the test subject, this study investigated plug tray dimensions and planting patterns. According to the design requirement that the width of the single-row transplanter must be less than 62.5 cm, a three-dimensional transplanter model was constructed. The transplanter comprises a coaxially installed dual-layer seedling conveying device and a sector-expanding automatic seedling picking and depositing device. The structural dimensions, drive configurations, and driving forces of the transplanter were also determined. Finally, the circuit and pneumatic system were designed, and the transplanter was assembled. Both bench and field tests were conducted to select the optimal working parameters. The test results demonstrated that the seedling picking and depositing mechanism met the required operational efficiency. In static seedling picking and depositing tests, at three transplanting speeds of 120 plants/min, 160 plants/min, and 200 plants/min, the success rates of seedling picking and depositing were 100%, 100%, and 97.5%, respectively. In the field test, at three transplanting speeds of 80 plants/min, 100 plants/min, and 120 plants/min, the transplanting success rates were 94.17%, 90.83%, and 88.33%, respectively. These results illustrate that the compact, electric-driven seedling conveying and picking and depositing devices meet the operational demands of automatic transplanting, providing a reference for the miniaturization and electrification of transplanters. Full article

As a core component of a fully automatic pepper transplanter, the performance of the seedling-picking mechanism is of particular significance. However, existing seedling-picking mechanisms have problems such as being prone to damaging the seedling roots and substrate, as well as having poor stability. To develop a highly efficient, stable, and minimally damaging seedling-picking mechanism, this study proposed a design scheme for a stem-clamping-and-pulling-out-type seedling-picking end actuator driven by a non-circular gear system. The specific methods and objectives include the following: (1) designing a differential non-circular gear system to replicate a manual picking trajectory accurately; (2) establishing a kinematic model and developing optimization software to determine the optimal parameter combination; (3) experimentally validating the mechanism’s performance through virtual simulations and bench tests. The bench tests showed that the mechanism could complete two seedling-picking operations per rotation, extracting an entire row (eight plants) in a single rotation at a speed of 30 r/min. The measured angles of the end effector at four key postures were highly consistent with simulation and high-speed camera data, with all key posture errors less than 1°. These results demonstrate the mechanism’s high accuracy, efficiency, and reliability. Full article

The pick-up gripper, as a core component of automatic transplanting systems, presents challenges in reliably grasping seedlings. In this study, a large-opening flexible seedling pick-up gripper was designed based on standard trays and seedling characteristics. Structural design and force analysis of the grasping mechanism were conducted to develop a functional prototype. As this represented the first prototype of this new gripper, multi-factor orthogonal tests and performance tests under local conditions were performed to evaluate its grasping effectiveness. It was found that the end diameter of the pick-up pin and the extraction speed for lifting plug seedlings vertically had the most significant effects, followed by the penetration depth and grasping force. The optimum grasping effectiveness was achieved when the end diameter of the pick-up pin was 1.2 mm, the penetration depth in the top straight line of the pick-up pin was 40 mm, the grasping force for squeezing root lumps was 0.4 MPa, and the extraction speed for lifting plug seedlings in a vertical direction was 900 mm/s. For typical vegetable seedlings, the average success rate in transplanting was up to 95%. Under the combined actions of penetrating, squeezing, and extracting operations, plug seedlings could be efficiently picked out for efficient transplanting. Full article

As an emerging vegetable cultivation technology, plug seedling cultivation significantly improves seedling production efficiency and reduces costs through standardization. Grading and transplanting, as the final step before the sale of plug seedlings, categorizes seedlings into different grades to ensure consistent quality. However, most current grading methods can only detect seedling emergence but cannot classify the emerged seedlings. Therefore, this study proposes an intelligent grading method for pepper plug seedlings based on RGB and point cloud images, enabling precise grading using both RGB and 3D point cloud data. The proposed method involves the following steps: First, RGB and point cloud images of the seedlings are acquired using 2D and 3D cameras. The point cloud data is then converted into a 2D representation and aligned with the RGB images. Next, a deep learning-based object detection algorithm identifies the positions of individual seedlings in the RGB images. Using these positions, the seedlings are segmented from both the RGB and 2D point cloud images. Subsequently, a deep learning-based leaf recognition algorithm processes the segmented RGB images to determine leaf count, while another deep learning-based algorithm segments the leaves in the 2D point cloud images to extract their spatial information. Their surface area is measured using 3D reconstruction method to calculate leaf area. Additionally, plant height is derived from the point cloud’s height data. Finally, a classification model is trained using these extracted features to establish a grading system. Experimental results demonstrate that this automated grading method achieves a success rate of 97%, and compared with manual methods, this method has higher production efficiency. Meanwhile, it can grade different tray seedlings by training different models and provide reliable technical support for the quality evaluation of seedlings in industrialized transplanting production. Full article

In the process of plug seedling transplantation, the cracking and dropping of seedling substrate or the damage of seedling stems and leaves will affect the survival rate of seedlings after transplantation. Currently, most research focuses on the reduction of substrate loss, while ignoring damage to the hole tray seedling itself. Targeting the problem of high damage rate during transplantation of plug seedlings, we have proposed an adaptive grasp method based on machine vision and deep learning, and designed a lightweight real-time grasp detection network (LRGN). The lightweight network Mobilenet is used as the feature extraction network to reduce the number of parameters of the network. Meanwhile, a dilated refinement module (DRM) is designed to increase the receptive field effectively and capture more contextual information. Further, a pixel-attention-guided fusion module (PAG) and a depth-guided fusion module (DGFM) are proposed to effectively fuse deep and shallow features to extract multi-scale information. Lastly, a mixed attention module (MAM) is proposed to enhance the network’s attention to important grasp features. The experimental results show that the proposed network can reach 98.96% and 98.30% accuracy of grasp detection for the image splitting and object splitting subsets of the Cornell dataset, respectively. The accuracy of grasp detection for the plug seedling grasp dataset is up to 98.83%, and the speed of image detection is up to 113 images/sec, with the number of parameters only 12.67 M. Compared with the comparison network, the proposed network not only has a smaller computational volume and number of parameters, but also significantly improves the accuracy and speed of grasp detection, and the generated grasp results can effectively avoid seedlings, reduce the damage rate in the grasp phase of the plug seedlings, and realize a low-damage grasp, which provides the theoretical basis and method for low-damage transplantation mechanical equipment. Full article

This study examines the spatial root development patterns of bareroot, containerized, and plug plus (plug+) saplings in hemiboreal forests of Latvia, focusing on the effects of two common soil preparation methods: mounding and disc trenching. In northern Europe, forest regeneration after clearcutting often involves planting, with soil preparation aimed at enhancing sapling survival and productivity. This study included four tree species: Pinus sylvestris, Picea abies, Betula pendula, and Alnus glutinosa. The results reveal that saplings planted in mounded sites developed more radially symmetrical root systems, while roots in trenched sites predominantly grew parallel to the furrow. This spatial root distribution was consistent across all forest types and did not show significant variation between stock types (containerized, bareroot, or plug+) or treatments (control or fertilized). Additionally, the number of main roots did not differ significantly between the soil preparation methods. These findings align with previous research and raise important questions regarding the impact of early root architecture on stand resilience at a mature stage, particularly in relation to windthrow, heavy snowfall, drought, and flooding resistance. The study underscores the need to consider root system development as a key factor in forest management practices aimed at ensuring long-term forest stability. Full article