Key Technologies of Plug Tray Seedling Transplanters in Protected Agriculture: A Review
Abstract
:1. Introduction
2. Overview of Transplanting Operation
2.1. Types of Transplanting Operation
2.2. Technical Requirements for Transplanting Operations
3. Key Technologies of Transplanting Machines
3.1. Physical Properties of Substrate
3.1.1. Force Measuring Platform
3.1.2. Simulation Technology
3.1.3. Image Technology
3.2. End Effector
Structural Style | Drive | Gripper Shape | Finger/Shovel Quantity | Principle Characteristics | Reference |
---|---|---|---|---|---|
Plug-in clamping type | Single cylinder | Aciculiform | 4 | Insert and clamp the substrate continuously within one push stroke of the cylinder. | [69] |
Single cylinder | Aciculiform | 4 | The cylinder piston rod retracts during the process of gripper insertion into the substrate to achieve the grasping action. | [38] | |
Single cylinder | Aciculiform | 4 | The crank slider mechanism is used to drive the gripper, and the end effector can realize two working modes of oblique insertion and clamping. | [70] | |
Double cylinder and air sac | Aciculiform | 4 | Two cylinders with a certain angle are used to drive gripper insertion into the substrate obliquely. The airbag between the two cylinders and the tightening spring are used to realize the clamping and opening of the gripper. | [68] | |
Oblique plug-in type | Single cylinder | Spade shape | 4 | The finger shovel is driven by the air cylinder and inserted along the four walls of the hole to reduce the damage to the seedling mound. By adding blocks, the substrate on the shovel is removed during the shovel recovery. | [57] |
Single cylinder | Aciculiform | 4 | Using a single cylinder and the guide groove mechanism to realize oblique insertion action of the gripper. | [54] | |
Linear effector | Aciculiform | 4 | Using electric push rod and the connecting rod mechanism to drive gripper insertion into the substrate. | [53] | |
Single cylinder | Spade shape | 4 | Using one cylinder completes the extension and retraction action of four groups of seedling spades. | [72] | |
Deformation sliding needle type | Double cylinder | Aciculiform | 4 | The large cylinder drives the small cylinder and the needle fixing plate to move downward together. Then, the small cylinder pushes the needle fixing plate to move downward to realize the clamping action. | [73] |
Single cylinder | Aciculiform | 4 | The single cylinder pushes four flexible needles through four oblique guide tubes to grasp the seedling substrate. | [77] | |
Servo motor | Aciculiform | 4 | When picking up seedlings, the four sliding needles extend out of the guide tube and are inserted obliquely into the hole to hold the seedling substrate. | [76] | |
Single cylinder | Aciculiform | 4 | With the pushing force of the seedling claw control cylinder, the gripping action is achieved by the deformation of the seedling needles. | [75] |
3.3. Integration for Multiple End Effectors
3.4. Transplanting Manipulator
3.4.1. Series Industrial Manipulators
3.4.2. SCARA Manipulator
3.4.3. Parallel Manipulator
3.4.4. Cartesian Coordinate Manipulator
3.5. Growth Status Identification for Seedlings
Reference | Method | Camera Model | Resolution | Light Source | Collected Information | Algorithm | Performance |
---|---|---|---|---|---|---|---|
[94] | Monocular CCD camera, line laser | -- | -- | 650 nm wavelength red light source line laser | Leaf area and plant height | Thresholding and linear structured light 3D location algorithm | Plant height accuracy is 5 mm. |
[92] | Monocular CCD camera | PU LNIX TMC-7DSP | 640 × 480 | 6 fluorescent lamps (380–780 nm), 40 W | Leaf area | Improved watershed algorithm and threshold segmentation | The recognition accuracy is 98%, and the average recognition time of a disc of seedlings is 4.396 s. |
[95] | Monocular CCD camera, line laser | PULNiX TM-7CN | 768 × 484 | 770–790 nm laser, 2 quartz halogen light sources, 300 W | Leaf area and plant height | Template matching and line structured light 3D positioning algorithm | The accuracy of hole identification is 95%. |
[93] | Monocular camera | Pulnix TM-745 | -- | 2 tungsten filament lamps, 120 W | Leaf area | Otus adaptive threshold segmentation | -- |
[19] | Monocular CCD camera | CCD | -- | -- | Leaf area | Threshold segmentation | -- |
[105,106] | Monocular camera | JoinHope Image OK-AC1300 | -- | Four F40BX/480 fluorescent lamps, 36 W | Side view of seedlings at 0 and 90 positions (upright degree and plant height) | Threshold segmentation | Each image processing algorithm takes 0.35 s on average. |
[102] | 3D stereo positioning with binocular vision | DaHeng Image DH-GV400UC | 752 × 480 | -- | Top view of hole seedling | SIFT feature matching algorithm | The three-dimensional reconstruction image of the original acupoint plate is obtained. |
[107] | Monocular camera | Epson Inc. GT800 | 512 × 512 | -- | Seedling hole area and total area | Three-layer neural network algorithm | -- |
[108] | Monocular camera | JHSM300E | 3 megapixel | -- | Top view of hole seedling | Threshold segmentation | The accuracy of hole identification is 100%. |
[97] | Depth camera | Intel RealSense SR300 depth sensor | 640 × 480 | -- | Seedling depth image | Point cloud clustering algorithm | The identification accuracy is 96.59% for 105-hole disc of seedlings with 10 days growth time. |
[109] | Monocular CCD camera | PU LNIX TMC-7DSP | 640 × 480 | Six F40BX/480 fluorescent lamps, 36 W | Leaf area and blade circumference | Improved watershed algorithm and threshold segmentation | The identification accuracy of inferior seedlings is above 98%. |
[96] | Depth camera | Realsense D415 | 1280 × 720 | 30 mm × 90 mm kM-BRD7530 LED | Seedlings’ height and edge points | ExG algorithm | Image processing average time is 0.753 s. |
[110] | Monocular CCD camera | DaHeng MER-131-75GC | 1280 × 1024 | Lemons OPT-L133037-W | Plug seedlings, plug bodies, and stem leaves | Threshold segmentation, morphological processing | -- |
3.6. Path Planning Methods
4. Discussion
4.1. Summary of Current Problems
- Limited standardization and compatibility. In protected agriculture, variations exist in the dimensions of seedbeds, planting densities, and tray specifications utilized by different regions and growers. Furthermore, there is a wide range of seedling substrate compositions and proportions employed. Manual seeding predominates, leading to inconsistencies in seedling position. Additionally, the timing of transplanting operations is not standardized. All these differences in the production modes of vegetables have led to the poor practicability of transplanters. Therefore, it is necessary to standardize the production mode by unifying the supporting planting equipment and planting agronomy, combining agronomy with equipment, so as to reduce the complexity of transplanting equipment development.
- Insufficient intelligence and automation. The existing transplanting machines have made some progress in terms of intelligence, but there is still room for improvement. Some aspects include sensing and perception, decision making and control, adaptability, learning, and human–machine interaction. With the development of artificial intelligence technology, we can develop a plant growth status recognition system based on deep learning and three-dimensional reconstruction technology, conduct research on motion control algorithms of multiple grippers to improve the transplanting efficiency, and use the internet of things (IoT) technology to develop remote monitoring functions for transplanters.
- Limited adaptability to diverse agricultural environments. Transplanting machines often face challenges in adapting to the wide range of agricultural environments found in different regions and countries. Variations in climate, soil conditions, crop varieties, and planting practices necessitate flexible and adaptable machines. Currently, there is a lack of transplanting machines, which can easily accommodate these variations, resulting in suboptimal performance and reduced efficiency. Future research should focus on developing transplanting machines, which can be easily customized and configured to suit different agricultural environments, ensuring optimal transplanting outcomes.
- Cost effectiveness and affordability. The high cost of transplanting machines is a significant barrier to their widespread adoption, particularly in developing countries and small-scale farming operations. The extensive equipment sets and specialized components required for transplanting machines contribute to their high procurement and maintenance costs. To encourage broader adoption, research efforts should concentrate on developing cost-effective solutions, including the use of affordable materials, streamlined designs, and modular components. By reducing the overall cost of transplanting machines, their accessibility and affordability can be enhanced, facilitating their integration into diverse agricultural systems.
4.2. Research Focus and Development Trend
- Integration of advanced sensing technologies. One of the prominent trends in transplanting machine research is the integration of advanced sensing technologies. This includes the incorporation of diverse sensors, such as cameras, 3D scanners, lidar, thermal imagers, and hyperspectral sensors. These sensors enable precise and comprehensive data acquisition, facilitating a deeper understanding of both the crop and the surrounding environment. By integrating data from multiple sensors, transplanting machines can enhance their perception capabilities, enabling real-time monitoring and analysis of crucial parameters, such as soil moisture, plant health, and environmental conditions.
- Development of intelligent decision-making algorithms. Another key focus in transplanting machine research is the development of intelligent decision-making algorithms. Machine-learning techniques, such as deep learning, can be applied to analyze the collected data and extract meaningful insights. These algorithms enable transplanting machines to make informed decisions based on real-time information, optimizing transplanting strategies and enhancing overall efficiency. Additionally, the integration of artificial intelligence and machine vision technologies allows for automated detection and classification of seedlings, improving the accuracy and precision of transplanting operations.
- Advancements in robotics and automation. The advancement of robotics and automation technologies plays a significant role in the evolution of transplanting machines. Researchers are exploring the development of robotic systems with improved dexterity, allowing for more precise and efficient handling of seedlings. Automation features, such as autonomous navigation, adaptive grasping, and coordinated multi-robot systems, are being investigated to enhance the performance and productivity of transplanting machines. These advancements aim to reduce the reliance on human labor, increase operational efficiency, and minimize human errors.
- Compact and lightweight technology for transplanting machines. The development of compact and lightweight technology for transplanting machines offers a promising solution to address the challenges related to the adaptability and cost effectiveness of large-scale equipment. By adopting smaller and more flexible transplanter systems, greater versatility can be achieved, allowing for tailored configurations and programming specific to different crop types and environmental conditions. The utilization of finite element analysis techniques enables the optimization of structural design for transplanting machine components, while incorporating lightweight materials in the manufacturing process enhances overall performance. Furthermore, the downsizing of equipment also contributes to cost reduction. Integrating cost reduction into this discussion, the development of compact and lightweight transplanting machines not only enhances adaptability but also improves cost effectiveness, making them more accessible to a wider range of users.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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References | Content | ||||||||
---|---|---|---|---|---|---|---|---|---|
Transplanting Types | Research Status | Substrate Physical Properties | End Effector | Multiple End Effector Integration | Overall Structure | Vision System | Path Planning | Research Direction | |
[47] | √ | √ | √ | √ | √ | ||||
[48] | √ | √ | |||||||
[36] | √ | √ | √ | √ | √ | √ | √ | ||
[49] | √ | √ | |||||||
[50] | √ | √ | √ | ||||||
ours | √ | √ | √ | √ | √ | √ | √ | √ | √ |
Types | Change Spacing for End Effectors | Independent Control for End Effectors | Visual System |
---|---|---|---|
Widening-spacing transplanting | Yes | Needed or not | No |
Replacement transplanting | Needed or not | Yes | Yes |
Grading transplanting | Needed or not | Yes | Yes |
Tray-to-pot transplanting | Yes | Needed or not | No |
Methods | Instruments/Tools | Purpose | Characteristics | Reference |
---|---|---|---|---|
Force measuring platform | Dynamometer | Measure the pulling force, penetration resistance, and clamping force of the gripper. | Cheap measurement platform needs to be designed, and the measurement data need to be recorded manually. | [52,53,54] |
Universal testing machine | Measure the tensile and compressive strength of the substrate, the pulling force, penetration resistance. | Simple operation, automatic recording of test data, moderate cost. | [55,56,57] | |
Texture analyzer | Measure the matrix compression and creep test, the pulling force, penetration resistance. | Simple operation, high accuracy, automatic recording of test data, high cost. | [58,59,60,61] | |
Direct shear apparatus | Measure the shear strength and shear stress of the substrate. | The test operation is complex, the test data are automatically recorded, high cost. | [62] | |
Simulation technology | FEM | Analyze the stress of the substrate and the gripper, simulate the damage of the substrate under different physical characteristic parameters. | Suitable for analyzing a substrate, which is easy to lose. | [63] |
DEM | Analyze the scattering of the substrate during the grasping process under different physical characteristic parameters. | Suitable for loose substrate. | [14,57] | |
Image technology | CT | Study the relationship between root density distribution, root micro-displacement, and the substrate crack expansion. | The equipment parameters need to be adjusted to obtain the three-dimensional image of root distribution. | [38,64,65] |
SEM | Study the substrate damage mechanism at the microscopic level of the internal composition and structure of the substrate. | Obtaining the internal section image of the substrate. | [66] |
Transplanting Manipulator | Structural Complexity | Bearable Load | DOF | Efficiency | Cost | Dimensions | Scalability |
---|---|---|---|---|---|---|---|
Serial Robot | Simple | High Load | 5–6 | High | Expensive | Middle | Best |
Four-Axis SCARA Robot | Simple | Middle Load | 4 | Middle | Middle | Small | Good |
Parallel Robot | Simple | Low Load | 2–3 | Low | Low | Small | Good |
Cartesian Coordinate Manipulator | Complicated | High Load | 2–3 | High | High | Big | Bad |
Country /Manufacturer | Model | Number of Grippers | Wired/Wireless | Independent Drive | Independent Clamp | Efficiency (Plants/Hour) | Applicable Plug Tray | Vision System | Air Consumption (L/min) | Power (kw) | Weight (kg) |
---|---|---|---|---|---|---|---|---|---|---|---|
The Netherlands/Viscon | Pic-O-Mat Blueline | 4–8 | Wired | Y | Y | 10,000 | Max size 600 × 400 | N | -- | -- | -- |
Pic-O-Mat Greenline | 4–14 | Wired | Y | Y | 21,000 | Max size 600 × 400 | N | -- | -- | -- | |
Pic-O-Mat Redline | 8–24 | Wired | Y | Y | 35,000 | Max size 600 × 400 | N | -- | -- | -- | |
Pic-O-Mat PC11 | 2–4 | Wired | Y | Y | 6000 | 4 plants per pot | N | -- | -- | -- | |
Pic-O-Mat PFS-8 | 8 | Wired | Y | Y | 10,000 | 4 plants per pot | N | -- | -- | -- | |
Pic-O-Mat VMP | 6–12 | Wired | Y | Y | 12,000 | 4 plants per pot | N | -- | -- | -- | |
Fix-O-Mat TIFS-IV | 12 | Wired | Y | Y | 12,000 | -- | Y | -- | -- | -- | |
Select-O-Mat Phoenix | 12 | Wired | Y | Y | 8000 | -- | Y | -- | -- | -- | |
The Netherlands/TTA | FlexPlanter | Multiple | Wired | Y | Y | 3000–30,000 | Plug size 9–30 mm | Y | 60 | 5 | 2000 |
FlexPlanter XF | Multiple | Wired | Y | Y | 10,000–30,000 | Plug size 9–30 mm | Y | 15 | -- | 4500 | |
PackPlanter wireless | Multiple | Wireless | Y | N | 10,000–60,000 | Plug size 9–30 mm | N | 20 | 2.5 | 800 | |
PackPlanter | Multiple | Wireless | N | N | 10,000–50,000 | Plug size 9–30 mm | N | 20 | 2 | 550 | |
PackPlanter S | Multiple | Wireless | Y | N | 10,000–20,000 | Plug size 9–30 mm | N | 20 | 2 | 450 | |
MidiFlat | Multiple | Wireless | Y | N | 4000–40,000 | Plug size 9–60 mm | N | 17 | 3 | 700 | |
MidiVision | Multiple | Wireless | Y | Y | 5000–40,000 | Plug size 9–60 mm | Y | 17 | 3.5 | 800 | |
FlexSorter | Multiple | Wired | Y | Y | 3000–12,000 | Plug size 9–60 mm | Y | 60 | 5 | 2000 | |
FlexSorter XF | Multiple | Wired | Y | Y | 10,000–30,000 | -- | Y | 16 | -- | 4500 | |
MaxSorter | Multiple | Wired | Y | Y | 6000–12,000 | -- | Y | 35 | -- | 3800 | |
Combifix II | Multiple | Wired | N | Y | 12,000–20,000 | Plug size 9–30 mm | Y | 535 | 5 | 2250 | |
The Netherlands/Flier Systems | SPH-Transplanter | Multiple | Wired | Y | Y | -- | -- | Y | -- | -- | -- |
Young Plant Sorter | Multiple | Wired | N | Y | 8000 | -- | Y | -- | -- | -- | |
Plug Fixer | 5 | Wired | N | Y | 11,000 | -- | Y | -- | -- | -- | |
The Netherlands/ISO Group | Vision Planter | Multiple | Wired | N | Y | -- | -- | Y | -- | -- | -- |
Plug Planting Machine | Multiple | Wired | N | Y | -- | -- | N | -- | -- | -- | |
Great Britain/TEA | TEA 600N | 12 | Wired | Y | Y | 18,000 | Plug size 30–50 mm | N | 70 | 1 | 600 |
TEA 1500N | 12 | Wired | Y | Y | 12,000–14,400 | Max size 600 × 400 | N | 80 | 1 | 700 | |
TEA 2000N | 16 | Wired | Y | Y | 16,000 | Max size 600 × 400 | N | 80 | 1 | 750 | |
TEA 1500J | 2–12 | Wired | Y | Y | 12,000–14,400 | Max size 600 × 400 | N | 80 | 1 | 650 | |
TEA 2000J | 16 | Wired | Y | Y | 16,000 | Max size 600 × 400 | N | 80 | 1 | 700 | |
Italy/Urbinati | RW32 | 40 | Wireless | Y | N | 40,000 | -- | -- | 80 | 4 | 960 |
RW64 | 80 | Wireless | Y | N | 56,000 | -- | -- | 80 | 5 | 1300 | |
USA/Bouldin & Lawson | PlugPlanter S Model | 16–32 | Wired | Y | N | 16,400–32,500 | -- | -- | -- | -- | -- |
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Liu, W.; Tian, S.; Wang, Q.; Jiang, H. Key Technologies of Plug Tray Seedling Transplanters in Protected Agriculture: A Review. Agriculture 2023, 13, 1488. https://doi.org/10.3390/agriculture13081488
Liu W, Tian S, Wang Q, Jiang H. Key Technologies of Plug Tray Seedling Transplanters in Protected Agriculture: A Review. Agriculture. 2023; 13(8):1488. https://doi.org/10.3390/agriculture13081488
Chicago/Turabian StyleLiu, Wei, Shijie Tian, Qingyu Wang, and Huanyu Jiang. 2023. "Key Technologies of Plug Tray Seedling Transplanters in Protected Agriculture: A Review" Agriculture 13, no. 8: 1488. https://doi.org/10.3390/agriculture13081488
APA StyleLiu, W., Tian, S., Wang, Q., & Jiang, H. (2023). Key Technologies of Plug Tray Seedling Transplanters in Protected Agriculture: A Review. Agriculture, 13(8), 1488. https://doi.org/10.3390/agriculture13081488