In precision agriculture, after vegetable transplanters plant the seedlings, field management during the seedling stage is necessary to optimize the vegetable yield. Accurately identifying and extracting the centerlines of crop rows during the seedling stage is crucial for achieving the autonomous navigation of robots. However, the transplanted ridges often experience missing seedling rows. Additionally, due to the limited computational resources of field agricultural robots, a more lightweight navigation line fitting algorithm is required. To address these issues, this study focuses on mid-to-high ridges planted with double-row vegetables and develops a seedling band-based navigation line extraction model, a Seedling Navigation Convolutional Neural Network (SN-CNN). Firstly, we proposed the C2f_UIB module, which effectively reduces redundant computations by integrating Network Architecture Search (NAS) technologies, thus improving the model’s efficiency. Additionally, the model incorporates the Simplified Attention Mechanism (SimAM) in the neck section, enhancing the focus on hard-to-recognize samples. The experimental results demonstrate that the proposed SN-CNN model outperforms YOLOv5s, YOLOv7-tiny, YOLOv8n, and YOLOv8s in terms of the model parameters and accuracy. The SN-CNN model has a parameter count of only 2.37 M and achieves an mAP@0.5 of 94.6%. Compared to the baseline model, the parameter count is reduced by 28.4%, and the accuracy is improved by 2%. Finally, for practical deployment, the SN-CNN algorithm was implemented on the NVIDIA Jetson AGX Xavier, an embedded computing platform, to evaluate its real-time performance in navigation line fitting. We compared two fitting methods: Random Sample Consensus (RANSAC) and least squares (LS), using 100 images (50 test images and 50 field-collected images) to assess the accuracy and processing speed. The RANSAC method achieved a root mean square error (RMSE) of 5.7 pixels and a processing time of 25 milliseconds per image, demonstrating a superior fitting accuracy, while meeting the real-time requirements for navigation line detection. This performance highlights the potential of the SN-CNN model as an effective solution for autonomous navigation in field cross-ridge walking robots.

The rapid and accurate detection of broccoli seedling planting quality is crucial for the implementation of robotic intelligent field management. However, existing algorithms often face issues of false detections and missed detections when identifying the categories of broccoli planting quality. For instance, the similarity between the features of broccoli root balls and soil, along with the potential for being obscured by leaves, leads to false detections of “exposed seedlings”. Additionally, features left by the end effector resemble the background, making the detection of the “missed hills” category challenging. Moreover, existing algorithms require substantial computational resources and memory. To address these challenges, we developed Seedling-YOLO, a deep-learning model dedicated to the visual detection of broccoli planting quality. Initially, we designed a new module, the Efficient Layer Aggregation Networks-Pconv (ELAN_P), utilizing partial convolution (Pconv). This module serves as the backbone feature extraction network, effectively reducing redundant calculations. Furthermore, the model incorporates the Content-aware ReAssembly of Features (CARAFE) and Coordinate Attention (CA), enhancing its focus on the long-range spatial information of challenging-to-detect samples. Experimental results demonstrate that our Seedling-YOLO model outperforms YOLOv4-tiny, YOLOv5s, YOLOv7-tiny, and YOLOv7 in terms of speed and precision, particularly in detecting ‘exposed seedlings’ and ‘missed hills’-key categories impacting yield, with Average Precision (AP) values of 94.2% and 92.2%, respectively. The model achieved a mean Average Precision of 0.5 (mAP@0.5) of 94.3% and a frame rate of 29.7 frames per second (FPS). In field tests conducted with double-row vegetable ridges at a plant spacing of 0.4 m and robot speed of 0.6 m/s, Seedling-YOLO exhibited optimal efficiency and precision. It achieved an actual detection precision of 93% and a detection efficiency of 180 plants/min, meeting the requirements for real-time and precise detection. This model can be deployed on seedling replenishment robots, providing a visual solution for robots, thereby enhancing vegetable yield.

To improve the seedling-picking efficiency of the vegetable transplanter and reduce the damage rate of the seedling pot, a reciprocating seedling-picking device driven by full air pressure was designed. In this paper, the structure and working principle of the pneumatic seedling-picking device are introduced. Through the mechanical analysis between the seedling-picking claw and the seedling pot, working parameters such as the stroke and driving force of the pneumatic seedling-picking claw clamping cylinder were determined. According to the action sequence of the seedling-picking mechanism, which is horizontally dispersed and longitudinally conveyed, the pneumatic control scheme of the seedling-picking and -dropping system was formulated. The simulation model for the control loop of the longitudinal cylinder was created with AMESim simulation software, and the simulation analysis was carried out. The Box–Behnken response surface design optimization method was used to determine the best operating parameters of the cylinder. The optimized peak value of shock vibration at the end of the cylinder was optimized from −65.64 mm·s⁻² to 35.41 mm·s⁻², proving that the optimization of pneumatic working parameters has a positive effect on the success rate of seedling picking. The bench test of the seedling-picking mechanism was conducted on 72-hole plug seedlings with two picking frequencies of 120 plants·min⁻¹ and 144 plants·min⁻¹, respectively, and the average seedling leakage rate, seedling damage rate, and seedling pot damage rate at different picking frequencies were counted. The experimental results show that under the two seedling-picking frequencies, the average success rate of seedling picking and throwing after optimization is increased from 96.4% and 92.4% to 97.9% and 95.3%, respectively. This is in line with the requirements of high-speed seedling picking and confirms the rationality of the seedling-picking mechanism design.

To address the inefficiency and instability of automatic transplanting machines, a dual-row seedling pick-up device and its corresponding control system were developed. Existing seedling end-effectors are primarily mechanically controlled, and the seedling needles can easily cause damage to the interior of the bowl. In order to reduce the damage inflicted by the end-effectors to the bowl, this paper conducted a mechanical analysis of the end-effector. At the same time, a buffer optimization analysis was conducted on the operation of the end-effector, and a flexible pneumatic end-effector for seedling picking was designed. The control system combined the detection of multiple sensors to monitor the process of seedling picking and throwing. By coordinating the lifting cylinder and clamping cylinder, the system effectively reduced seedling pot damage while improving seedling picking efficiency. By setting the operating parameters of the servo motor, the goal of low-speed and high-efficiency seedling picking was achieved. To evaluate the performance of the control system, the linear displacement sensors and acceleration testing systems were used to analyze the performance of the seedling throwing. The results showed that the seedling picking efficiency could reach 180 plants min⁻¹, with no significant difference between the actual measured moving distance and the theoretical setting distance. The positioning error remained stable between 0.5 and 0.9 mm, which met the requirements for seedling picking accuracy. The buffer optimization design reduced the peak acceleration of the end-effector from −22.1 m/s² to −13.4 m/s², and the peak value was reduced by 39.4%, which proved the significant effectiveness of the buffer design. A performance test was conducted using 128-hole seed trays and 33-day-old cabbage seedlings for seedling picking and throwing. When the planting frequency reached 90 plants/row·min⁻¹, the average success rate of picking and throwing seedlings was 97.3%. This indicates that the various components of the designed seedling pick-up device work in good coordination during operation, and the control system operates stably. Technical requirements for the automatic mechanical transplanting of tray seedlings were achieved, which can provide reference for research on automatic transplanting machines.

The planting mechanism of existing transplanters cannot meet the agronomic requirements of planting densely planted vegetables with multiple rows, small plant spacing, and small row spacing. In order to solve this current problem, an eight-row duckbill planting mechanism driven by a motor and a cylinder was designed. According to the agronomic guidance and mechanism design requirements for transplanting densely planted vegetable seedlings, this paper analyzes the working principle of the planting mechanism, establishes its kinematic theoretical model, and determines the structural parameters of the driving device and opening and closing device in the planting mechanism. Aimed at the problem of large planting resistance when eight-row planting end effectors of the planting mechanism are planting at the same time, based on the existing research, three duckbill planting end effectors with double incisions, four incisions, and conical structures were selected, and the planting process was simulated using an EDEM 2022-RecurDyn 2024 coupling simulation. The single-factor analysis method and the interactive factor Box–Behnken response surface analysis method were used. It is concluded that the duckbill end effector with double incisions has the smallest planting resistance, and the rationality of the mechanism design is preliminarily verified. A planting resistance measurement platform was built based on the STM32 platform and HX711 module, and a planting resistance test of the duckbill planting end effector was carried out to verify the correctness of the planting mechanism simulation results. The planting mechanism performance test was carried out, and the test results showed that the planting qualification rate of the prototype reached 96.62%, the planting spacing variation coefficient was only 3.55%, and the planting efficiency reached about 7135 plants/h, which met the agronomic requirements of small plant spacing and small row spacing for densely planted vegetables and verified the feasibility and practicality of the planting mechanism.