A Review of Perception Technologies for Berry Fruit-Picking Robots: Advantages, Disadvantages, Challenges, and Prospects
Abstract
:1. Introduction
2. Mechanism of Berry Fruit-Picking Robots
2.1. Principle
- Approaching the object: The end-effector is open, and the robot system positions the end-effector close to the object.
- Coming into contact: The end-effector is in contact with the gripping object.
- Increasing gripping force: The end-effector outputs a certain amount of gripping force to ensure the object is gripped.
- Securing the object: When the gripping force meets the object, the gripping force should be adjusted to stop increasing once it securely grips the object.
- Lifting the object: The end-effector grips the object, moving it to the desired position, combined with the robot system moving the object to the desired position.
- Release the object: The end-effector releases its grip, separating from the gripped object.
- Monitoring the grasp: the gripping process is monitored by sensors to judge the end-effector’s contact with the gripped object and the effectiveness of the grip.
2.2. Picking and Gripping
2.3. Structures
2.3.1. Mobile Platform
2.3.2. Manipulator
2.3.3. End-Effector
3. An Overview of Perception Technologies for Berry Fruit-Picking Robots
- Visual Perception
- Tactile Perception
- Distance Measurement
- Switching Sensors
4. Methods and Analysis
4.1. Visual Perception
4.1.1. Methods
4.1.2. Analysis
4.2. Tactile Perception
4.2.1. Methods
4.2.2. Analysis
4.3. Distance Measurement
4.3.1. Methods
4.3.2. Analysis
4.4. Switching Sensors
4.4.1. Methods
4.4.2. Analysis
5. Discussion and Prospect
5.1. The Technical Characteristics of the Perception Technology
- Visual perception: the visual sensors exhibit high resolution and can accurately identify the fruit’s location, size, and ripeness. They facilitate real-time monitoring of fruit location and status, enabling the robot to adjust its actions accordingly. Additionally, visual perception can automatically distinguish between different types of fruits, ensuring the accuracy of the robot’s picking operations. Moreover, one of the primary challenges in practical visual perception is the accuracy of perception in different scenes and conditions [180]. It is, therefore, necessary to adapt visual perception to different light and background conditions in different environments to improve the reliability and accuracy of fruit-picking tasks.
- Tactile perception: The fruit’s shape, hardness, and surface properties enable the robot to grasp precisely. The tactile sensor delivers real-time data feedback, assisting the robot in adjusting its picking actions to enhance efficiency and quality. Additionally, the tactile sensor detects the pressure exerted by the robot upon contact with the fruit, preventing excessive force that could potentially damage it. Furthermore, the tactile sensor adapts to the varying characteristics of different fruit types and sizes, ensuring precise and reliable harvesting.
- Distance measurement: the distance measurement sensor accurately gauges the distance between the robot and the object fruit, ensuring the fruit can be grasped without damage. Additionally, the sensor monitors real-time changes in distance, enabling prompt adjustments to the robot’s movements for a stable picking process. The sensor also detects distances to obstacles, allowing the robot to avoid collisions and ensuring the safety of the robot and its environment. Furthermore, it aids the robot in accurately locating fruit, thereby enhancing the efficiency and precision of the picking process.
- Switching sensors: Firstly, they enable control of the robot’s actions based on detected state information, such as initiating or halting the movement of the manipulator and directing the robot’s movement. Secondly, they provide precise control over the robot’s motions. The switch sensors monitor the status of various robot components, including the opening and closing of the robotic manipulator and overall movement. They can also detect contact between the robot and obstacles, immediately stopping movement upon collision to prevent damage to both the robot and its surroundings. Furthermore, the switch sensors assess the operational status of various components, facilitating timely fault detection, diagnosis, and repair.
5.2. The Advanced Technical of Berry Fruit Picking
5.3. Challenges and Future Opportunities
- Accuracy: The accuracy of a robot in berry fruit picking hinges on its ability to precisely identify and locate fruits, assess their ripeness, and execute picking actions with minimal damage or errors. Reliable perception technology is essential for correctly identifying different types of fruits. However, current technology often fails to achieve this desired level of accuracy. Challenges such as visual perception errors caused by lighting conditions, occlusion, wind effects, and background interference contribute to omissions and misdetections in optical perception systems. As a result, ensuring consistent accuracy in the berry fruit-picking process remains uncertain.
- Adaptability: Adaptability in robotics refers to the ability of a robot to adjust its operations and techniques in response to varying conditions and situations. This includes accommodating different types of berries, adapting to changes in weather conditions, navigating different soil types, and handling various crop layouts. The goal is to optimize efficiency and effectiveness in harvesting. During berry picking, environmental factors like light, humidity, and temperature can affect sensor performance, impacting perception capabilities. It is essential for perception technology to be adaptable in natural environments, enabling robots to perform tasks such as obstacle avoidance, object recognition, and precise object separation. These tasks can be challenging due to the unpredictable characteristics of natural environments.
- Durability: Durability in robotics refers to a robot’s ability to withstand wear, corrosion, and physical damage in its operational environment. This includes resisting high temperatures, humidity, dust, and the vibrations and shocks encountered during berry picking. Ensuring sensors remain stable and functional over extended periods is crucial for reliable performance in challenging conditions.
- Data processing: Data processing in robotics involves the collection, analysis, and utilization of sensor data. Sensors gather extensive information during operations, such as the location, ripeness, and size of berries. Real-time processing is essential to adjust the robot’s movements promptly. The application of numerical techniques for the acquisition of sensory data may be susceptible to the influence of internal bias [184]. Additionally, data may contain noise or errors, requiring cleaning and correction. Integration of data from various sensors is vital for comprehensive analysis. Thus, data processing poses a major challenge in perceptual technology.
- Low cost: Low cost is defined as a cost-effectiveness that reduces research and development expenses, thereby making the technology affordable and accessible for farms of all sizes. The sensor is a crucial component of the perception system. However, high-quality sensors with solid performance are still expensive, and they may become faulty or damaged during use, requiring maintenance or replacement. Therefore, it is necessary to reduce the cost of sensors while ensuring their quality and performance.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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---|---|---|---|---|---|---|---|
Piezoresistive | Patterned graphene/PDMS | 5 Pa | 1.2 kPa−1 | 0~25 kPa | — | 1000 | [74] |
3D graphene | 1 Pa | 0.152 kPa−1 | 0~27 kPa | 96 ms | 9000 | [75] | |
Polyaniline/polyvinylidene | — | 53 kPa−1 | 5.2~98.7 kPa | 38 ms | 50,000 | [76] | |
Capacitive | Silver/PDMS | 10 Pa | 0.18 kPa−1 | 0~400 kPa | 100 ms | 10,000 | [77] |
Silicon/polystyrene | 0.14 Pa | 44.5 kPa−1 | 0~100 Pa | 9 ms | 5000 | [78] | |
adhesive/graphene | 1 mg | 3.19 kPa−1 | 0~4 kPa | 30 ms | 500 | [79] | |
Piezoelectric | PDMS/silver paste/pvdf | — | 7.7 mVkPa−1 | — | 10 ms | 80,000 | [80] |
Flexible GaN/ZnO NWs | — | — | — | 180 ms | 4000 | [81] | |
ZnS:Mn particles | — | 2.2 cps kPa−1 | 0.6~50 Mpa | 10 ms | 10,000 | [82] | |
Triboelectric | PDMS/Ag nanofibers | — | — | — | 70 ms | 2800 | [83] |
PET/PDMS/Ag electrodes | — | 0.06 kPa−1 | 1 kPa | 70 ms | 10,000 | [84] | |
Ecoflex and PVA/PEI | — | 0.063 VkPa−1 | 5~50 kpa | — | 2250 | [85] |
Methods | Object | Advantage | Disadvantage | Ref. |
---|---|---|---|---|
Object Detection | Strawberry | This method detects mature and immature strawberries in greenhouse images, yielding highly accurate test results. | The ripe category struggles with obscured strawberries, while the immature category faces confusion issues. | [104] |
Cherry tomato | This method uses an enhanced YOLOv3 algorithm for cherry tomato detection, achieving a precision of 94.29%. | Identifying heavily shaded fruits was difficult, and the integration of ripeness in detecting fruits at various growth stages was not performed. | [105] | |
Cherry | This cherry detection method is based on an improved YOLOv4 model. It accurately detects ripe, semi-ripe, unripe, and ripe cherry fruits. | The speed of object detection is relatively slow for cherries. | [106] | |
Blueberry | A YOLOv5 network model that can detect blueberries improved accuracy by 2.4% over the original YOLOv5 network. | The disadvantage is that the method has more network parameters, and further research is needed to improve the detection ability. | [107] | |
Strawberry | YOLOv5 combined with dark channel enhancement improves strawberry fruit-picking accuracy and robustness in complex environments. | This method does not use different image enhancement methods for various periods of lighting conditions. | [108] | |
Strawberry | A strawberry growth detection algorithm improves the precision and accuracy of fruit growth state monitoring in complex environments. | Deep learning-based strawberry growth state monitoring is server-dependent and still has limitations. | [109] | |
Tomato | The YOLO-Tomato models effectively detect tomatoes in complex environmental conditions, and their performance is excellent. | The method is designed to enumerate fruit, and the YOLOv3 model performs poorly in detecting small fruits. | [110] | |
Tomato | The YOLO-Tomato model effectively detects tomatoes in complex environments, outperforming state-of-the-art methods. | Models are poorly adapted to different conditions and may lose semantic information under heavily occluded conditions. | [111] | |
Strawberry | The R-YOLO model improves the localization precision, increasing strawberry harvesting robots’ harvest rate and real-time performance. | It does not discuss the detection accuracy under different environmental conditions. There is a lack of comparative analysis of existing methods. | [112] | |
Kiwifruit | The improved DY3TNet model accurately detects kiwifruits in orchards with minimal data weight. | The method mentions the effect of flash on kiwifruit image detection, but no statistical tests were performed. | [113] | |
Semantic Segmentation | Grape | This CNN accurately detects grape berries for yield estimation and prediction in viticulture. | It shows a notably worse accuracy for the class edge, with 41.7%. | [114] |
Tomato | This method accurately segments tomatoes, with intersection and pixel accuracies of 82.5% and 89.79%, respectively. | This method may be some burrs in the process of extracting tomato stems and calyxes that may affect the identification of the final picking points. | [115] | |
Strawberry | This method accurately assesses strawberry maturity in challenging fields with 93.7% accuracy. | Many strawberries in the transition maturity stage may cause classification confusion. | [116] | |
Grape | DeepLabv3+ combined with transfer learning is more suitable for accurately segmenting grape clusters, with better performance. | The method mentions increased accuracy with HE image enhancement methods but lacks details on statistical tests. | [117] | |
Strawberry | The Hierarchical adaptive feature fusion method significantly improves real-time strawberry segmentation compared to existing methods. | This methodology does not provide detailed comparative results or discussion with other related methods. | [118] | |
Grape | This method proposes a conditional random field-based approach to identify grapes with adaptivity and multi-feature fusion. | The present study conducted experiments with artificially selected reference berries, which may introduce a subjective factor for identification. | [119] | |
Grape | The method was developed to efficiently estimate canopy volume, detect clusters, and count grapevines in the field. | The present method is a more reliable option for sensitivity to light conditions, and the RGB-D sensor’s data quality is better. | [120] | |
Tomato | A transformer-based R-CN model effectively accurately identifies tomato varieties and ripening stages. | The method does not explore tomato detection in various occluded regions, raising practical applicability concerns. | [121] | |
Bayberry | A CNN-based model for prune segmentation in complex environments that resists the limitations of light variations and occlusion. | Higher false and missed segmentation rates and poorer segmentation of small objects in fruit clusters. | [122] | |
Grape | This method can detect and mask single berry objects with a semantic segmentation network using a class edge to separate single objects. | The training of this method is time and computationally intensive and has limited adaptability to different training systems. | [123] | |
Instance Segmentation | Blueberry | The ResNet50 backbone with Mask R-CNN accurately quantifies wild blueberries using high-definition images. | Many experiments are required to select the best hyper-parameters in order, and this method has a slower recognition speed. | [124] |
Blueberry | This method effectively detects and segments blueberry fruits, extracting traits related to yield and monitoring fruit development. | This method has a slower recognition speed. | [125] | |
Grape | The enhanced model achieved high detection accuracy and robust generalization across diverse varieties in complex growth environments. | Detection accuracy is limited under varying light conditions and occlusion, while the model’s detection speed requires further improvement. | [126] | |
Waxberry | The waxberry identification network achieved high precision and demonstrated strong robustness to occlusion. | It did not increase the immature waxberries for fruit counting and yield estimation. It does not solve the problems caused by fruit stacking. | [127] | |
Grape | This method is versatile for grape berry counting and size detection, enabling precise discernment of berry features. | Errors in segmentation may result in the misclassification of non-berry contours as a group. | [128] | |
Strawberry | The improved DeepLabV3+ model accurately segments strawberries with different maturities, reducing environmental factors. | This method does not conduct experiments on strawberries of different locations, and the recognition speed is slow. | [129] | |
Cherry tomato | Using bimodal eigenmaps and a balanced multitask loss model, this model enhances stem segmentation accuracy in cherry tomato picking. | The present model has challenges regarding color and shape similarity, loss of stem features, and category differences. | [130] | |
Strawberry | A method for strawberry segmentation demonstrated efficiency in a natural system and created a database with images and entries. | New methods emerge, making rigorous performance comparison impossible due to long computational times. | [131] | |
Strawberry | The method identified and localized strawberries and provided location information. | The model has some drawbacks regarding error detection and speed in the recognition process. | [132] | |
Strawberry | A Mask R-CNN model detects strawberries and other diseases, providing plant disease detection. | The model’s accuracy is low, and the recognition time is extended. | [133] |
Methods | Object | Advantage | Disadvantage | Ref. |
---|---|---|---|---|
Piezoresistive | Tomato | Servo motors control the bending angle and speed of the soft gripper, with tactile sensors for tomato gripping. | The sensor is non-stretchable, which influences the flexibility of the gripper. | [136] |
Grape | An anthropomorphic end-effector that combines the adhesion principle with a multi-contact design with piezoresistive tactile sensors. | This method has some drawbacks in maximizing the contact area and lacks a direct measurement and adjustment process. | [137] | |
Strawberry | A soft, sensitized gripper is introduced with a robotic system for picking small fruits like strawberries. | The present gripper jaws’ piezoresistive tactile sensors are unsuitable for handling fragile berries with a normal force range. | [138] | |
Blackberry | A tendon-driven gripper for automated blackberry harvesting, incorporating a flexible resistive force sensor for providing force feedback. | Complex calibration process and poor signal immunity. | [59] | |
Kiwifruit | This methodology enables detecting and classifying kiwifruit according to fruit hardness, utilizing data from force and bending sensors. | The gripper may damage the fruit during gripping, and the perception function may be limited due to the limited number of sensors. | [139] | |
Sweet pepper | The end-effector can successfully pick the pepper, and the vacuum pressure sensor provides feedback that the suction cup has grabbed it. | Vacuum pressure sensors are less sensitive at low pressures and are highly influenced by temperature. | [140] | |
Tomato | The method enables the grasping of tomatoes while combining tactile perception information and algorithms to classify the size and ripeness. | This method lacks practical scenarios and performance evaluations and raises concerns about reliability and robustness. | [141] | |
Strawberry | A piezoresistive tactile sensor manufactured by a 3D printer with no additional modifications can be used with a soft robot to grasp strawberries. | The print material’s increased resistance lowers sensor sensitivity. Material deformation causes readings to drift and oscillate. | [142] | |
Capacitive | Tomato | Electrohydraulic bending actuators with tactile sensors for real-time gripping detection and fruit size estimation. | The manual fabrication process and multi-component design represent drawbacks. | [143] |
Strawberry | Capacitive sensors integrated into robotic gripper jaws enable gentle picking of strawberries, providing susceptible data output. | Increasing pressure causes a rise in compressive stiffness, impairs sensor performance, and limits the linear measurement range. | [144] | |
Piezoelectric | Tomato | Piezoelectric sensors can measure the end-effector’s initial contact with the tomato, signal oscillations, and vibrations. | This approach does not solve the problems of poor surface contact and adhesion that affect pressure sensors. | [145] |
Tomato | The bionic manipulator features adaptive gripping capabilities, using piezoelectric and strain sensors to measure the softness of tomatoes. | Without a sensor module, the robot cannot adjust the contact force and is less accurate when using a single piezo to recognize softness. | [146] | |
Triboelectric | Tomato | A three-finger actuator and a triboelectric tactile sensor have been designed to monitor and clamp tomatoes precisely. | Other factors strongly influence sensor performance and are poorly adapted to changes in the shape of the gripped object. | [147] |
Kiwifruit | A hybrid sensor integrates a triboelectric perception unit and an inductive sensor. When combined with vision, it accurately detects kiwifruit. | The method has some limitations when dealing with complex input signals, including the inability to recognize other features of the object. | [148] | |
Tomato | The end-effector fingers are equipped with TENG sensors, featuring a simple structure, high sensitivity, and durability for harvesting tomatoes. | Drawbacks include non-linearity, creep issues, dependency on external light sources, and magnetic fields. | [149] |
Methods | Object | Advantage | Disadvantage | Ref. |
---|---|---|---|---|
Infrared | Strawberry | The manipulator uses three internal infrared sensors to optimally identify and position the object. | The infrared sensor may cause incorrect positioning due to occlusion, and the sensor may not be detected due to unsuccessful cutting. | [32] |
Tomato | The camera consists of a pair of stereo infrared sensors. The 3D coordinates of the object tomato in the camera coordinate system were measured. | Environmental, background, lighting, occlusion, and overlap issues can make detecting and locating object tomatoes difficult. | [156] | |
Grape | The infrared sensor detects and synchronizes the position of the grapes for harvesting. | Infrared sensors have limited detection range and narrow transmission and reception angles. | [157] | |
Strawberry | RGB-D cameras can estimate the position of strawberries in three-dimensional space by combining infrared with depth information. | When calculating the strawberry’s position, there is an error due to the offset of the image center from the camera’s mounting position. | [158] | |
Cherry tomato | RGB-D cameras provide high precision and accuracy in locating cherry tomatoes by acquiring multimodal images. | Positioning becomes more complicated when the camera is in front of the fruit. | [159] | |
LiDAR | Strawberry | The finger is equipped with a pair of LiDAR sensors; when the fruit stem blocks the laser beam, the control module operates to cut off the stem. | LiDAR sensors have errors, and the sensor’s small detection distance makes it unable to adapt to narrow and low-channel environments. | [160] |
Strawberry | Fusion of laser sensors with monocular cameras for accurate navigation and improved system fault tolerance. | LiDAR sensors do not provide semantic information for scene recovery in complex environments and limit accuracy and light changes. | [161] | |
Tomato | Using LIDAR sensors, accurate representation and positioning of crops such as tomatoes can be achieved in occlusion. | Tracking systems may suffer from errors, switching between objects, or missed and false detections. | [162] | |
Tomato | Using LIDAR, the mobile platform is provided with environmental awareness and navigation capabilities for autonomous movement. | LIDAR data processing requires conversion and fusion with other data, and LIDAR has difficulties in dealing with background interference. | [163] | |
Ultrasonic | Blueberry | Ultrasonic sensors can detect weeds and determine a plant’s height by measuring the distance between it and the sensor. | Ultrasonic sensors may have some errors in measuring crop height, and the input voltage and the characteristics of the sensor itself limit its output. | [164] |
Multimodal | Tomato | Infrared sensors measure plant position, LiDAR acquires 3D information about the plants, and RGB-D cameras validate LiDAR results. | Occlusion limits the infrared sensor’s field of view and light signal, and lighting and incident light affect camera image quality. | [165] |
Tomato | Using LIDAR and infrared cameras, it is possible to navigate, build maps, detect tomatoes, and locate the center point of a tomato. | LIDAR and depth camera tomato recognition outdoors is affected by light. | [166] |
Methods | Object | Advantage | Disadvantage | Ref. |
---|---|---|---|---|
Photoelectric | Kiwifruit | The position data from the switch sensor is transmitted to the stepper motor, which drives the gripping mechanism to clamp the fruit. | There may be a discrepancy between the finger and the kiwi due to the light restriction caused by the switch mounting position. | [169] |
Grape | The photoelectric switching sensor, integrated into the gripper, provides feedback that enables precise strawberry gripping. | Errors in the photoelectric switching sensor may result from occlusion and stacking problems associated with the strawberry fruit. | [170] | |
Proximity | Kiwifruit | The robot equipped with a hall sensor can discern alterations in the magnetic field and thereby determine the position of the kiwifruit. | It is possible that the hall sensor detection is not sufficiently sensitive due to the kiwifruit’s non-magnetic shape. | [171] |
Kiwifruit | Hall sensors discern alterations in magnetic fields and regulate the direction and number of steps stepper motors undertake. | When catching fruit, kiwifruit-picking robots tend to inhale surrounding debris, such as branches and leaves. | [172] | |
Tomato | Magnetic proximity switches are used for end-effector control and are used to achieve control of the tomato-picking position of the gripper. | Proximity switches are difficult to apply in cluttered, messy, and fragile plant environments. | [173] | |
Pressure | Grape | Fingers can be switched to clamp the grapes flexibly using the information obtained from the contact force with the grapes to the actuator. | Due to the hydraulic brake’s low control accuracy, the fruit may be damaged during the clamping process. | [174] |
Tomato | The gripper, driven by a self-service motor, measures and precisely controls the end-effector gripping tomato using a resistance strain gauge. | Temperature variations and electromagnetic interference can affect resistance strain gauges, leading to errors in the results. | [175] | |
Tomato | The gripper has a membrane pressure switch that stops the finger from closing when the pressure reaches a minimum destructive value. | The pressure switch’s lack of flexibility can easily damage the tomato rind. | [176] | |
Cherry tomato | The pressure switch controls the force of the end-effector to pick the tomato after measuring the applied force on a human finger. | The pressure switch is not mounted on the end-effector, and due to individual tomato differences, tomatoes may be destroyed. | [177] | |
Sweet pepper | Vacuum switching sensors detect the results of sweet pepper fruit gripping, and harvesting operations continue after successful fruit gripping. | Vacuum switch sensors are more costly and have longer response times. | [178] |
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Wang, C.; Pan, W.; Zou, T.; Li, C.; Han, Q.; Wang, H.; Yang, J.; Zou, X. A Review of Perception Technologies for Berry Fruit-Picking Robots: Advantages, Disadvantages, Challenges, and Prospects. Agriculture 2024, 14, 1346. https://doi.org/10.3390/agriculture14081346
Wang C, Pan W, Zou T, Li C, Han Q, Wang H, Yang J, Zou X. A Review of Perception Technologies for Berry Fruit-Picking Robots: Advantages, Disadvantages, Challenges, and Prospects. Agriculture. 2024; 14(8):1346. https://doi.org/10.3390/agriculture14081346
Chicago/Turabian StyleWang, Chenglin, Weiyu Pan, Tianlong Zou, Chunjiang Li, Qiyu Han, Haoming Wang, Jing Yang, and Xiangjun Zou. 2024. "A Review of Perception Technologies for Berry Fruit-Picking Robots: Advantages, Disadvantages, Challenges, and Prospects" Agriculture 14, no. 8: 1346. https://doi.org/10.3390/agriculture14081346