Research Status and Development Trend of Key Technologies for Pineapple Harvesting Equipment: A Review

: Pineapple harvesting is a key step in pineapple ﬁ eld production. At present, pineapple fruits are usually picked manually. With decreasing labor resources and increasing production costs, machines have been used instead of manual picking approaches in the modern pineapple industry. This paper brie ﬂ y describes the basic situation of pineapple planting worldwide. Based on the degree of automation of mechanized pineapple harvesting equipment, the main structural forms, core technologies, and operation modes of semi-automatic, automatic, and intelligent pine-apple harvesting equipment are summarized. The research status and existing problems of key pine-apple fruit picking robots, such as fruit recognition, maturity classi ﬁ cation, positioning, and separation of pineapple fruits, are analyzed. Considering the problems of pineapple harvesting equipment, such as di ﬃ culty entering the ground, low harvesting e ﬃ ciency, low picking success rate, and fruit damage, innovative future research directions for mechanized pineapple harvesting technology are proposed, such as combining agricultural machinery and agronomical principles


Introduction
Modern agricultural equipment is important for ensuring a stable and safe supply of food and other important agricultural products.Currently, Chinese agricultural production methods have entered a new development stage dominated by mechanization.However, agricultural machinery and equipment in China remain limited.Therefore, accurately identifying agricultural production needs and addressing the shortcomings and weaknesses of mechanized agricultural equipment are important to comprehensively promote the revitalization of the countryside, accelerate the modernization of agriculture approaches and rural areas, and build a strong agricultural country.
Pineapple is a distinctive tropical fruit in China, with a planting area of approximately 67,000 hectares and a total annual output of more than 1.8 million tons [1].Mechanized agricultural equipment for pineapple field production is lacking, especially for fruit picking and transport in the pineapple harvesting process, which all require workers.Pineapple harvesting is a seasonal, labor-intensive process.Due to increasing labor and production costs, young and strong laborers in rural areas are lacking, and the demand for mechanized pineapple harvesting equipment in China is becoming increasingly urgent.Pineapple production areas are mainly found in developing countries, and the mechanization level of pineapple harvesting equipment is relatively low.At present, semi-automated pineapple harvesting equipment is used in commercial applications in other countries.The harvesting methods include manual picking of fruits, the use of mechanical conveyors to collect fruits, and the transportation of fruits to processing plants or marketing distribution points through transfer equipment.Research on mechanized pineapple harvesting technology started later in China, and the use of semi-automated harvesting methods remains in the experimental exploration stage.In addition, existing pineapple seedling breeding methods mainly involve the propagation of mother plants.The structural composition of the pineapple plant is shown in Figure 1 [2].The protection requirements of plants and sprouts present great challenges to the development of automatic pineapple fruit harvesting methods, limiting the rapid development of automatic pineapple fruit harvesting technology and equipment.Many studies on automated and intelligent pineapple harvesting technology have been carried out, but no automatic pineapple harvesting equipment has been commercially applied.Thus, automatic and intelligent harvesting technology for pineapple fruits must be further researched.
The purpose of this paper is to present the current research status of mechanized pineapple harvesting equipment, summarize the key technologies involved in automatic and intelligent harvesting of pineapple fruits, identify the problems and shortcomings of current intelligent pineapple harvesting technology, and propose future research directions.By summarizing and analyzing the advances of recent studies, this study aims to provide a reference for pineapple harvesting equipment research, which is highly important for addressing the limitations of pineapple field production agricultural machinery and equipment.

General Overview of the Pineapple Farming Industry
Pineapple is the world's largest tropical fruit export, except for bananas [3].In 2022, the total harvested area of pineapple was 1,059,203 hectares, and 29,361,138.34tons of fruit were produced, according to Food and Agriculture Organization (FAO) statistics (Food and Agriculture Organization of the United Nations, Rome, Italy) [4].According to regional statistics, the largest harvested area and total production were in Asia, where the pineapple harvested area was 422,959 hectares and the total production was 13,429,160.42 tons.The second-largest area was Africa, with a pineapple harvest area of 377,981 hectares and a total production of 5,458,600.38tons.The third region was the Americas, with a pineapple harvest area of 253,687 hectares and a total production of 10,360,469.11tons.In Oceania, the pineapple harvest area was 4,577 hectares, with a total production of 112,908.42 tons.In 2022, pineapple cultivation data were available for 85 countries, and the top ten countries in terms of pineapple harvesting area were Nigeria (18.12%),India (10.12%),China (7.19%), Thailand (6.70%), the Philippines (6.35%), Brazil (6.01%), Costa Rica (3.75%), Vietnam (3.72%), Angola (3.46%), and Mexico (2.40%).These 10 countries, which account for 61.96% of the world's pineapple harvesting area, are mainly underdeveloped and developing countries.Due to science and technology limitations in these areas, the mechanization level of pineapple production technology in these countries is low.
According to the China Rural Statistical Yearbook 2023 (2023 China Statistics Press Co., Ltd., Beijing, China) [5], the main provinces in which pineapples were planted were Guangdong, Hainan, Guangxi, Yunnan, Sichuan, and Fujian.The pineapple production in Guangdong and Hainan accounted for approximately 90.41% of the total national production in 2022.According to the statistics of FAO, which are shown in Figure 2a, the pineapple harvest area was between 60,000 and 90,000 hectares from 2012 to 2022.The pineapple production per unit area in China has continuously increased, exceeding the average production per unit area of all countries worldwide in 2015.A pineapple planting company (Hainan Nonglong Agricultural Development Ltd., Haikou City, Hainan Province, China) in Hainan Province with a pineapple planting area of 400 hectares was investigated to evaluate the material and labor costs of pineapple field production processes such as ploughing, preparing the land, planting, field management, and harvesting.Figure 2b shows that the labor costs of pineapple harvesting, which accounted for 42.71% of the production process, were the highest among the different tasks.The reason is that fruit picking and handling during harvesting are performed manually.The pineapple harvesting period is short, and the labor demand is high.Therefore, the high labor cost of pineapple production has become an important factor influencing pineapple production but not pineapple harvesting in China.

Research Progress on Pineapple Harvesting Equipment
With respect to the degree of automation, the current mechanized pineapple harvesting equipment can be divided into semi-automatic and automated harvesting equipment.The semi-automatic harvesting equipment includes manual-assisted picking devices and fruit transfer equipment.Manual-assisted picking devices rely on manual control of the picking device, and the pineapple fruit is separated by breaking or cutting the fruit stalk.Fruit transport equipment, which can realize mechanized collection and transportation of fruit, has been widely used in foreign countries.This equipment relies on manual picking of fruits, using conveyor belts or other conveying mechanisms to transport fruits to the collection platform, and the fruits are transported away from the pineapple field by this transport equipment.Automatic pineapple harvesting equipment uses a picking mechanism to automatically pick the fruit and can perform loading and transportation functions.At present, most automatic pineapple harvesting technology and equipment are in the laboratory research or experimental stage, and automatic fruit picking technology needs to be investigated further.With the rapid development of intelligent technology, machine vision, and sensing technology, scholars have carried out research on pineapple picking robots, aiming to use robot technology to automatically pick pineapple fruits.

Manual-Assisted Picking Device
The manual-assisted picking device is an ergonomic intervention with a mechanical structure that can reduce or eliminate occupational musculoskeletal diseases such as back pain and lower limb injuries caused by bending and other bad postures during the picking process [6,7].This device can alleviate the intensity of labor required by picking workers and reduce the damage to workers caused by pineapple plants with thorny leaves.Force arm gravity compensation is a method that can alleviate the muscle fatigue experienced by pineapple picking workers.The workers who used a gravity compensation mechanism experienced no obvious discomfort when working for 20 min under normal working conditions.However, when workers did not use the gravity compensation mechanism, the pain became more severe after approximately 5 min of work [8].
Scholars have designed artificial auxiliary picking devices by using methods such as clamping fruit in combination with separation methods such as cutting [9][10][11][12][13], sawing [14], and breaking [15][16][17] fruit.These devices can significantly reduce the intensity of labor performed by workers and improve picking efficiency.Artificial auxiliary picking devices for cutting or sawing involve a fruit clamping mechanism combined with a cutting or sawing mechanism.The cutting or sawing mechanism directly cuts the fruit stalk.These types of devices require workers to accurately determine the cutting position on the fruit stalk; otherwise, fruit damage can easily occur.The auxiliary pineapple picking device with a shear mode and multilink transmission mechanism has an overall height of 493 mm, a mass of 0.8 kg, a theoretical picking operation force of 29.17 N, and a single picking time of 3.4 s.Compared with the efficiency of manual picking, the efficiency increases by 26% by applying this approach, but the applicable fruit stalk diameter range decreases from 60 mm to 30 mm [18].The picking efficiency of the artificial auxiliary picking device (Figure 3), which is suitable for fruit stalks with diameters less than 30 mm, increased by only 16% [19].Fruit stalk slipping during the cutting process is an important factor affecting picking efficiency that also increases the risk of damage to pineapple plants.The curved mechanical claw clamp method increases the clamping force of the picking device; the success rate of fruit clamping was more than 98%, the success rate of cutting was more than 95%, the average single-fruit picking time was approximately 6 s, and the pineapple fruits and plants were well preserved during the picking process [20].The auxiliary picking device, based on the sawing method, uses a motor to drive the sawing mechanism to destroy the fruit stalk.This approach is suitable for fruit stalks of any diameter and requires only a small operating force, but the electrical components increase the overall weight of the device.For example, the total weight of one handheld pineapple picking device is 3.5 kg [21].A single-chip microcomputer can be used to automatically control the clamping mechanism and the disc saw cutting mechanism (Figure 4), which could meet the picking requirements of pineapple fruits of different sizes and shapes and reduce the operation time for each part of the picking process [22].The auxiliary picking device based on the breaking method involves fixing the fruit via a clamping mechanism and applying an external load to break the fruit stalk to separate the fruit.This kind of picking device has a simple structure, effectively overcomes the knowledge of the cutting position along the fruit handle required by the cutting and sawing methods, and reduces the picking difficulty for workers.However, the device requires accurate control of the clamping force on the fruit.If the clamping force is too small, slip phenomena may occur, leading to picking failure, while an excessive clamping force may damage the fruit.The clamping method also affects the picking success rate and picking efficiency of pineapple fruits.Moreover, a pineapple picking manipulator based on the double-claw side clamping method (Figure 5) had a theoretical bending/breaking force of 23.4 N, a picking success of approximately 80%, and an average single-fruit picking time of 13.5 s [15].A picking device based on the four-claw three-dimensional clamping method had a picking success rate of 95%, but the fruit damage rate was approximately 5%, and the single-fruit picking time was 14 s [16,17].A comparison of the above three fruit separation methods reveals that the auxiliary picking device based on the cutting method has the highest picking efficiency, and the overall picking efficiency increases by more than 15% compared to that of the other approaches.The auxiliary picking device based on the sawing method has the lowest picking efficiency.The success rate and efficiency of the auxiliary picking devices and the fruit damage rate are mainly determined by the proficiency of the operator.

Pineapple Fruit Transfer Equipment
Pineapple fruit transport equipment is mainly used to solve the problem of field handling after fruit picking.Replacing manual handling with machine approaches may significantly improve harvesting efficiency, reduce labor intensity, and decrease harvesting costs.Machine-based approaches are considered important transition methods, with the aim of developing fully automatic harvesting equipment, and these approaches can reduce the labor needs of pineapple harvesting by 70% [23].Due to the high planting density of pineapple, these machines are not used in foreign countries.When the equipment is working, the walking device moves along a path outside the pineapple field, and a conveying mechanism transports the fruit away from the pineapple field.This equipment (Figure 6) does not damage pineapple plants or fruits, and the harvesting efficiency is high [24][25][26].These regions and countries have superior pineapple planting conditions and fewer obstacles than other areas.A good passing ability can be achieved when a conveying arm with a large width is used.Therefore, this transport equipment is very important for pineapple harvesting.Pineapple plots in China are characterized by small areas, substantial dispersion, and many obstacles, which limit the application of large-scale pineapple harvesting and transportation equipment.According to the protection requirements of pineapple plants and the characteristics of pineapple plants taller than 80 cm during the harvesting period, Chinese researchers have developed small pineapple harvesting and transportation equipment by using high-clearance chassis structures and cross-road operation modes.These techniques do not damage the fruit or seedlings during operation.Researchers at the Guangdong Institute Modern Agricultural Equipment, the National Chiayi University in Taiwan Province of China, Kaohsiung District Agricultural Research and Extension Station, Tw-Sato Industrial Co., Ltd.(Changhua City, Taiwan, China), and other scientific research institutions have reported the application of small-horsepower high-clearance pineapple harvesting equipment.This equipment needs to be operated by at least two picking workers and has a load of more than 400 kg; moreover, the picking efficiency is more than double that of manual harvesting [27].Due to the small load of this equipment, it is impossible to transport all the fruit in one row at one time, and it is necessary for the equipment to enter the ground many times, which limits improvements in harvesting efficiency.The harvesting efficiency of the transfer equipment (Figure 7) with a load of 5 tons reached 0.133 ha/h to 0.167 ha/h, which was more than three times greater than that of manual harvesting [28].Adjustable ground clearance transfer equipment with a hub motor drive and a scissor hydraulic lifting structure has a theoretical maximum load of 1 ton [29].Configuring the picking mechanism, which is controlled by workers, on high-gap pineapple fruit transfer equipment can reduce the labor intensity of picking workers.The control system of the picking device can be manually controlled to determine its position and clamp the fruit.Moreover, the picking machine executes the cutting mechanism on the fruit stalk to separate the fruit.The picked fruit is automatically transported to the fruit collection box under the guidance of the conveyor guide groove mechanism [30].The semi-automatic screwing pineapple picking and collecting machine is manually moved to the upper end of the pineapple plant to determine the approximate position.The screw nut mechanism and the X-shaped scissor lifting mechanism are used to fine-tune the picking height of the picking mechanical claw, and the fruit is clamped.The rotating movement of the picking mechanical claw is driven by a motor, which bends and breaks the fruit stalk at the determined position, and finally, the fruit is placed on the collecting groove.The average picking time of each pineapple is 17.99 s, and the average picking success rate is 75.3% [31].
Thus, auxiliary manual picking devices and pineapple fruit transport equipment reduce the labor intensity of harvesting, improve production efficiency, and reduce production costs for fruit picking and handling tasks.However, fruit picking must be performed manually, which is the main limitation of semi-automatic pineapple harvesting equipment.Only by solving the problem of manual fruit picking can automatic pineapple harvesting be realized.

Automated Pineapple Harvesting Equipment
Automatic pineapple harvesting equipment mainly uses mechanized methods to organically perform fruit picking and handling, thereby reducing the amount of labor needed and improving harvesting efficiency.The most important function of automatic pineapple harvesting equipment is automatic fruit picking, which involves a power platform, a picking mechanism, a conveying mechanism, and a collecting box.The automatic picking mechanism is installed at the front end of the tractor, and the pineapple fruit is fed into the picking mechanism in an orderly manner by using the fruit guide groove mechanism.The fruit handle is cut by a disc cutter, and four rows are picked at a time.The picked fruit is transported to the compartment through the chain conveying mechanism.The tool lifting mechanism can be adjusted to cut pineapple fruits at different heights [32].Because pineapple stalks are very brittle, the fruit can easily fall due to vibrations before the stalk is cut.In this case, ideal harvesting outcomes cannot be achieved.
Therefore, some scholars have proposed theoretical schemes such as automatically adjusting the height of the cutting mechanism [33] and the circumferential layout of the cutting blade [34] to optimize the fruit stalk cutting method, with the aim of increasing the pineapple harvesting success rate of automatic methods.However, it is difficult to evaluate the advantages and disadvantages of this method due to the lack of experimental results.Automatic pineapple picking and collecting machines are designed by using two threedegree-of-freedom manipulators; this pineapple picking method is non-destructive and continuous [35,36].The operator remotely controls the machine to move, and the sensor detects whether the robot arm is located directly above the fruit.After the mechanical arm reaches the top of the fruit, the picking mechanism installed on the mechanical arm clamps and cuts the pineapple fruit, and the picked fruit is placed on the belt transport mechanism.The belt conveyor transports the fruit to the designated position, and then the fruit is grabbed by the handling manipulator and placed in a collection box in an orderly manner.However, when the rotation speed of the saw is less than 400 r/min, the fruit stalk cannot be cut.Only 195 pineapples could be harvested each hour to ensure a low fruit damage rate.The binocular vision system can also be used for fruit recognition and positioning.By adjusting the height of the platform and the blade through the control system, the blade can be aligned with the fruit stalk, and the fruit can be pushed towards the cutter by the push rod so that the blade can cut through the fruit stalk.The separated fruit then falls onto the conveyor belt and is sent to the collection device [37].
In addition, scholars have used manual picking methods to study automatic pineapple harvesting methods based on clamping, fracturing, and conveying.For example, an automatic fruit picking mechanism was designed by combining horn and rotary picking approaches.Through simulations of the virtual prototype, the speed of the rotary picking mechanism was determined to be 100 r/min, and the forward speed was determined to range from 1.25 m/s to 1.75 m/s [38].The V-type picking finger mechanism has also been used to cut pineapple fruit stalks, with the fruit stalks broken by the rotation of the picking finger.The key components of this small and efficient pineapple picking machine were designed to realize the simultaneous picking of two rows of pineapple fruit.The average travel speed was approximately 0.2 m/s, and 20 to 26 pineapples could be picked per minute [39].Furthermore, a rotating automatic picking device was designed using an arcshaped claw and the horizontal rotating picking method.With the help of a pressure sensor, a clamping force control system was developed for the picking device so that it could be used for fruits of different sizes while reducing the fruit damage rate.The fruit is transported to the collection box by a conveyor belt, enabling automatic picking, collection, and transportation [40].Liu studied an automatic pineapple harvesting method that did not involve clamping the fruit.In addition, they used the characteristics of the shedding region between the ripe fruit and the fruit stalk and the relative movement of the flexible harvesting mechanism and pineapple to generate an appropriate fracture torque so that the pineapple in the shedding layer fractured.They also designed a pineapple harvesting mechanism consisting of a flexible finger roller [41,42] and toggle-feeding mechanism [43] (Figure 8), which they combined with a pineapple internal damage prediction model [44].The optimal operational and structural parameters of the harvesting mechanism were clarified.The field test results showed that the harvest rate and damage rate of the flexible finger roller were 78% and 8%, respectively, and only 1 s was needed to harvest each fruit.Moreover, the harvest rate and damage rate of the toggle-feeding pineapple harvesting mechanism were 84% and 9.53%, respectively.Although the above fruit clamping method accounts for the inconsistency in the height of the fruit, considering the complex environment in which the fruit is located, it remains unclear how the picking mechanism accurately clamps the fruit.The existing automatic pineapple harvesting approach involves one-time harvesting of fruit, and mature fruit cannot be identified based on the maturity of the pineapple during selective picking.This leads to the waste of immature fruit during the picking process, and the success and efficiency of harvesting, the fruit damage rate, other performance indicators, and commercial application requirements must still be examined.

Intelligent Pineapple Harvesting Equipment
To realize intelligent and accurate picking of pineapple fruits, scholars have carried out research on intelligent pineapple harvesting equipment.Intelligent pineapple harvesting equipment mainly includes pineapple fruit picking robots and harvesting equipment integrating intelligent systems on the basis of transfer equipment or automatic harvesting equipment.Based on the automatic pineapple harvesting equipment, Liu [45] used the improved rapidly exploring random tree star algorithm to study the global operation path planning of pineapple harvesting equipment and develop unmanned pineapple harvesting equipment.The plan time of the navigation system was 14.78 ms under the navigation test in the field.Under the conditions of 0.2 m/s, 0.4 m/s, and 0.6 m/s driving speed, the average position deviation was 5.75 cm, 7.27 cm, and 8.96 cm, respectively, and the average heading deviation was 7.78°, 10.25°, and 12.57°, respectively.These robots mainly consist of a visual system, a mechanical arm, an end effector, a walking system, and a control system.However, pineapple fruit picking robots have different visual systems, mechanical arm structures, and end effector working methods, leading to different operating effects.For example, a pineapple fruit picking robot was designed with a three-degree-offreedom robotic arm, an electric double-claw clamping structure to clamp the end effector, two CCD cameras to form a visual system, an image segmentation target recognition algorithm based on threshold segmentation, and a rotating picking separation method; this robot achieved a picking success rate and single-fruit picking time of 87% and 36.3 s, respectively, in a greenhouse setting [46].However, in an experiment in which a pineapple fruit picking robot was designed with a double-finger gripper and shearing mechanism, the recognition success rate and picking success rate were affected by the intensity of the light source.The recognition success rate and picking success rate were 82.1% and 80.9%, respectively, in the daytime and 77.3% and 74.9%, respectively, at night [47].In addition, a picking robot system (Figure 9) was designed by using dual manipulators, industrial cameras to form a vision system, and the YOLOv3 learning algorithm to detect targets; this robot achieved a recognition accuracy of 90.82% [48].The picking success rate reached 95.56% by optimizing the power and motion control of the manipulator and the end effector [49].Furthermore, a picking robot was designed by using a seven-degree-of-freedom manipulator, a binocular stereo camera, and an end effector composed of an airbag clamping method and a shearing mechanism.Although the successfully picked fruit had no obvious bruising or surface damage and the total single-fruit picking time was 8.8 s, the picking success rate was 82.47% with human-computer interaction-based control and 64.06% with unmanned interactive control [50].Moreover, a picking robot was designed using the YOLO v5 algorithm, a shear type end effector, a depth camera, and a multimanipulator; this robot obtained better picking results than previous systems.The laboratory test results showed that the average positioning error was 23.9 mm, the picking success rate was 90%, and the average successful picking time was 5.4 s.In field identification tests, the detection accuracy of pineapple fruit was 97.0%, and the recall rate was 95.75% [51].However, research on pineapple fruit picking robot technology started only recently, and research on technology-integrated systems is limited.At present, this technology remains in the laboratory stage, and problems such as poor recognition and positioning accuracy, a low picking success rate, and low picking efficiency need to be addressed.Fruit recognition and positioning methods need to be further studied, and the structural design of the end effector and the performance of fruit separation methods need to be further optimized.

Research on the Key Technology of Pineapple Picking Robots
The structure and working principles of pineapple-picking robots are basically the same as those of other types of fruit-picking robots [52].These robots use visual systems to obtain fruit information.The control system processes information and makes control decisions, which are performed by the robotic arm and the end effector to nondestructively separate the fruit from the plant.At present, research on the key technology of pineapple picking robots has focused mainly on fruit recognition, maturity classification, positioning, and separation.

Pineapple Fruit Identification Technology
Target recognition involves identifying the target of interest based on images and providing feedback on the position and category of the target.At present, commonly used target recognition algorithms include traditional image recognition methods based on manual feature construction and deep learning-based image recognition methods based on feature extractors [53,54].

Traditional Image Recognition Methods
Traditional image recognition methods mainly include three steps: region selection, feature extraction, and classification.In complex natural scenes, such as changes in lighting conditions, complex weather conditions, complex background effects, etc., the problem makes the process of manually designing features more complicated [55].Traditional image recognition is a complex process that requires comprehensive consideration of various steps and technologies to obtain ideal target recognition results.
Li studied image recognition methods for pineapple fruits from different perspectives: image acquisition (monocular camera, binocular camera), image segmentation (threshold segmentation, region segmentation), and feature extraction (roundness shape descriptor, SURF feature extraction).Binocular vision systems were shown to obtain better recognition accuracy than monocular vision systems.Moreover, factors such as occlusions and light intensity influenced the accuracy of fruit recognition.The recognition success rate of the developed pineapple fruit recognition system was 90% under sunny conditions but only 60% on cloudy days.The recognition time was 0.017 s [56][57][58].Furthermore, the processing of grayscale images results in the loss of considerable color information, which affects image segmentation and recognition accuracy.Wu [46] proposed a method for threshold segmentation of images in the HSV space.The connected region labeling method is used to solve the problem of image noise.The shape contexts algorithm, which has good robustness in nonrigid object matching, was used for target recognition.On this basis, He [37] used the hue H in the HSV color space and the Otsu threshold segmentation method to segment images of pineapple fruits according to the large difference between the colors of pineapple fruit bags and leaves.The fruit centroid position is determined according to the upper, lower, left, and right pixels of the fruit target, improving the calculation speed.Chaivivatrakul [59] studied a pineapple detection method based on texture analysis.Through interest point feature extraction and descriptor calculations, interest point classification, candidate fruit point mapping, morphological closure, and fruit region extraction based on a support vector machine, the single-image detection accuracy of pineapple reached 85%.
Traditional pineapple fruit image recognition methods use simple features and models.Target recognition systems have low performance requirements and certain advantages in terms of system development costs.However, manually designing features such as color and texture takes a long time.Considering factors such as the unstructured nature of pineapple harvesting areas, light changes, occlusions, and other environmental conditions, these methods lack self-learning ability and have poor adaptability, resulting in poor performance.

Deep Learning-Based Image Recognition Methods
Deep learning-based image recognition methods use deep neural networks to automatically learn image features without manually designing features.Deep learning models automatically extract useful information from image databases.These methods can easily learn the target features, and scholars at home and abroad have begun to apply such approaches for fruit recognition in complex environments, verifying that the technology has good robustness to target occlusion and illumination changes, high accuracy, and realtime advantages [55,60].At present, many deep learning-based image recognition methods for pineapple fruit recognition have been developed based on convolutional neural networks such as you only look once-level (YOLO), single-shot detectors (SSDs), and faster region-based convolutional neural networks (R-CNNs).
The YOLO algorithm has excellent target detection speed, is suitable for real-time applications, and performs well in recognizing difficult targets.The YOLO v3 model combined with transfer learning was used to develop a pineapple fruit image recognition method.An offline test in an indoor simulation scene showed that the average recognition accuracy was 90.82% [48].However, in the case of complex field backgrounds, the average recognition accuracy of the YOLO v3 model was 64%, and the recall rate was 74.1% [61].In addition, a densely connected convolutional network (DenseNet) was added to the Darknet-53 backbone network, and a spatial pyramid pooling network (SPP-net) was integrated into the detection module to improve the performance of the YOLO v3 model, enhancing the information representation ability of the feature map.The average accuracy of the improved YOLO v3 model was 94.9%, which is 9.65%, 5.49%, and 4.08% higher than that of YOLO v3, Fast-R-CNN, and Mobile net-SSD, respectively, and the robustness in fruit recognition tasks is better than that of the other three models [62].The YOLO v4 model is more accurate than the YOLO v3 model.By improving the loss function of the model, the detection accuracy of the YOLO v4 model reached 98.8%.Compared with the original YOLO v4 model, the recognition accuracy was improved by 1.1%, and the accuracy was improved by more than 1.3% compared with that of the YOLO v3 model [63].The YOLO v5 model has the advantages of fast training speed, batch reasoning capability, and low memory requirements.The model memory is only 27 MB, and 1300 iterations of training take only 14.46 min.The detection accuracy of this model for mature pineapple fruit reached 95% [64].By introducing a collaborative attention mechanism to improve the performance of the YOLO v5 model, the pineapple fruit recognition accuracy was increased to 99.2%, and the recognition time of each image was 40 ms [65].Furthermore, the simple attention mechanism (SimAM) attention mechanism was added to the YOLO v7 network to improve the maximum pooling convolution structure, and the semantic orthogonal learning framework non-maximum suppression (Solf-NMS) algorithm was used to replace the non-maximum suppression algorithm to improve the performance of the YOLO v7 model.This approach improves the feature extraction ability of the YOLO v7 model, reduces the feature loss in the sampling process, and improves the detection and recognition performance for pineapple fruits in the field in scenarios with occlusions or overlap.The average accuracy of the improved model was 95.82%, and the detection time was 23.81 ms [66].By replacing the complete intersection over union (CIoU) loss function in the initial model with the shape-aware intersection over union (SIoU) loss function, the convolutional block attention module (CBAM) module was merged into the backbone network, which minimized the overall degree-of-freedom of the YOLO v7 model, accelerated model convergence, and enhanced the ability of the model to emphasize the key features of pineapple fruits, thereby improving its generalizability and overall robustness.The average recognition accuracy of the improved YOLO v7-tiny model reached 96.9%, which was 1.6% higher than that of the original model [67].
In addition, the MobileNet network was used to replace the visual geometry group (VGG) 16 basic network in an SSD for preliminary extraction of fruit features.Combined with the back-end multiscale feature detection network, the features of the front-end network were extracted under different scale conditions to predict the location and category of fruits.The non-maximum suppression module is used to filter repeated prediction targets.The first step in this method is to set a threshold and clear the bounding boxes below the threshold.Then, the remaining bounding boxes are selected as output values.Next, the bounding boxes that have been selected with larger intersection over union (IOU) values are identified and cleaned up.This is repeated 2 to 3 times until all bounding boxes have been traversed.Finally, the purpose of clearing the redundant bounding boxes is to realize the model lightweighting and improve the computational speed.With this approach, the weight of the SSD model was decreased, and the model size was only 24.2 MB.The average detection time for each image with the lightweight Mobile Net-SSD model was 0.128 s.The recognition accuracy under dense fruit conditions was 50.5%, the recognition accuracy under weed-covered fruit conditions was 99%, and the recognition accuracy under overlapping fruit conditions was 90% [68].Chen [69] studied the pineapple fruit recognition performance of the RetinaNet model in different complex scenarios and improved the performance of RetinaNet by embedding the efficient channel attention (ECA) attention mechanism into the classification subnet of RetinaNet.In the case of mild complexity, the detection accuracy, recall rate, and harmonic mean of the improved Reti-naNet model were 94.8%, 95.41%, and 94.75%, respectively.Compared with those of the original RetinaNet model, the detection accuracy and harmonic mean were improved, but the recall rate was reduced by 1.97%.For very complex situations, the detection accuracy, recall rate, and harmonic mean of the improved RetinaNet model were 91.6%, 88.84%, and 90%, respectively, and the performance was better than that of the original RetinaNet model.In these two complex cases, the recognition speed did not differ considerably.The overall detection accuracy was better than that of the Faster R-CNN, CenterNet, YOLO v3, YOLO v4, and SSD models.Moreover, the recognition speed of the improved Reti-naNet model was not much different from that of YOLO v3 and YOLO v4.The recognition speed of this model was 16 frames/s slower than that of Faster R-CNN and 25 frames/s and 36 frames/s faster than those of CenterNet and the SSD, respectively.Additionally, the Swin Transformer was merged with the R-CNN model to establish the spatio-temporal convolutional neural networks (STCNNs) model, which effectively extracts global and local information and overcomes the limitations of the convolution and Transformer models.The detection accuracy of the STCNN model for pineapple was 92.54%, the memory was 28 MB, and the detection speed was 0.163 s [70].Furthermore, Zhang proposed using Baidu's customized easy deep learning (Easy DL) training platform to develop a deep learning-based method for images of bagged pineapple fruits.After model training and online testing, the average accuracy was 97.9%, the recall rate was 90%, and the recommended threshold was 0.6.In offline tests, the average precision was 92%, the recall rate was 90%, and the suggested threshold was 0.3 [71].
According to the comparison results of pineapple fruit recognition algorithms in Table 1, scholars preferred to use the YOLO series of algorithms to develop pineapple fruit recognition methods.This is because these algorithms perform better in terms of evaluation indices such as recognition accuracy, recognition speed, and reconciliation average, especially the improved YOLO v7 algorithm, which can quickly recognize fruits with higher recognition accuracy under different illumination, occlusion, and environmental conditions, with an overall recognition accuracy greater than 95%.The recognition speed is approximately 20 ms, which is sufficient for real-time decision making and operation; however, the reconciliation average is only approximately 90%, and the comprehensiveness performance of the model can still be optimized and improved.The SSD, RetinaNet, Faster R-CNN, and CenterNet algorithms have difficulty obtaining ideal results that balance recognition accuracy and recognition speed.To obtain high recognition accuracy, a long recognition time is needed, which is not suitable for real-time applications of picking robots.Furthermore, to obtain a fast recognition speed, the recognition accuracy is reduced, which cannot meet the high-precision requirements of picking robots.

Pineapple Fruit Maturity Classification Technology
Maturity is an important characteristic for determining the pineapple fruit picking period.Because pineapple is a non-climacteric fruit, fruits with different maturity levels have varied and inconsistent quality.The harvest time is one of the most important criteria for evaluating the quality of pineapple.Harvesting the pineapple too early leads to poor fruit quality, and harvesting the pineapple too late leads to fruit decay.According to the technical regulations for pineapple production [72], the maturity of pineapples should be selected according to the market use of the fruit.Pineapple fruits for processing or longdistance transport for export should be picked during the green ripening period.Fresh food and fruit sold at local markets should be harvested at the yellow-ripening stage.Some studies have proposed the use of growth regulators to artificially induce pineapple flowering and improve the uniformity of flowering and the flowering rate [73].Additionally, ripening agents can be used to ripen the fruit so that the fruit ripening rate is consistent among different fruits.However, the outcomes of forced pineapple flowering methods are affected by type, application concentration, time, plant age, number and size of green leaves, and environmental conditions [74][75][76][77][78].In actual production, commonly used forced pineapple flowering methods include traditional manual spraying of growth regulators.This approach has some shortcomings, such as poor spraying uniformity and low liquid irrigation accuracy [79].It is difficult to achieve consistent and uniform flowering and fruit maturity.Furthermore, scholars have found that after the ripening of pineapple fruit by a ripening agent, ethylene accelerates the deterioration and decay of harvested fruit, potentially leading to severe occurrences such as black heart disease.Reducing or even completely eliminating the use of ripening agents and improving the quality of pineapple fruits are recommended [80,81].Therefore, accurately determining the optimal maturity index for selective fruit harvesting can ensure fruit quality and maximize postharvest storage and shelf life.
The existing maturity classification methods are mainly based on the yellow area of the fruit epidermis [82].The yellow area of the epidermis is an important region for pineapple fruit maturity detection and classification methods.Mohammad [83] classified fresh pineapple into seven maturity levels according to the mode of transportation and market use, as shown in Table 2.In natural orchard scenes, the classification of pineapple maturity is affected by factors such as leaf occlusion, light changes, excessive imaging light, and background colors similar to those of the fruit epidermis.Ahmed [84] used the hue value to remove the ground and sky from images and combined the resulting image with an adaptive red-blue image to study the performance of an image segmentation method for pineapple fruits.In addition, they used the elliptic Hough transform to enhance the segmentation image to completely remove misclassified regions and the image background, improving the accuracy of the image segmentation algorithm.To further optimize the algorithm, a convolutional neural network and cascaded object detection method were used to detect the location of the pineapple in the captured image, and the pineapple maturation was classified according to the environment around the pineapple.The template matching method was used to enhance the image to obtain the best classification results [85].This method was highly accurate and efficient and successfully detected immature and mature MD-2 pineapple fruits in images with complex backgrounds.Anh [48] detected green and ripe pineapple fruits using a YOLO v3 model, achieving an average accuracy of 88.39% for green fruits and 93.26% for ripe fruits.The naive Bayes, linear discriminant analysis, K-nearest neighbor, support vector machine, artificial neural network, logistic regression, decision tree, random forest, gradient boosting, and adaptive boosting methods have also been used to classify immature and mature pineapple fruits.The re-sults showed that the artificial neural network had the highest accuracy of 80% by considering peel color, demonstrating that color values can be combined with machine learning methods to evaluate pineapple maturity [86].
Table 2. Characteristics of pineapple fruit skin color at different maturity levels in different scenarios [83].

Index
Fruit Epidermis Color Characteristics Color Characteristic Traits and Applicable Scenarios Immature All the epidermises are dark green and the flesh is firm, indicating that the fruit is not yet ready for picking.
Early mature The epidermises are mostly dark green with a slight yellowish tinge between the stalk and the body of the fruit; these pineapples are suitable to be picked for export purposes via waterborne transportation.

Mature
Approximately 1-2 eyes of the fruit have pale yellow epidermises; these pineapples are suitable to be picked for export purposes via air transportation.
Under ripe Approximately 25% of the epidermises are pale yellow; these pineapples are suitable to be picked for domestic markets.

Early ripe
Approximately 50% of the epidermises are pale yellow; these pineapples are suitable to be picked only for domestic markets, and this is the optimum level for fresh consumption.

Ripe
Approximately 75% of the epidermises are pale yellow; these pineapples are suitable to be picked for domestic markets only.

Overripe
Approximately 100% of the epidermises are yellowish; these pineapples are not suitable for any market.
In the above studies, only unripe and ripe pineapple fruits were detected and classified.However, the actual maturity classification needed to be more refined, especially for classifying semi-ripe fruits.The HSV color space was used to segment the yellow and green pixels of pineapple fruits; the maturity of the fruit was determined by a threshold; and a support vector machine was used for detection and classification.The indoor environment test results showed that the detection accuracy of immature and fully mature fruits was 100%, and the detection accuracy of semi-mature fruits was 86% [87].In addition, a fuzzy logic classifier was used for classification, and the detection accuracy of semimature fruit was improved to 90% [88].In a simulation experiment, it was found that the convolutional neural network algorithm achieved a classification accuracy of 100% for immature and fully mature pineapples, while the classification accuracy for semi-mature pineapples reached only 82% [89].However, in a training test of a convolutional neural network for pineapple maturity classification, the recognition accuracies for fully ripe, semi-ripe, and immature pineapple fruits were 100%, 82%, and 79%, respectively [90].According to the results of a YOLO v2 model for maturity classification of Tainong 17 pineapple fruits, the classification accuracy of semi-mature, fully mature, and immature fruits was 95.24%, 96.70%, and 77.34%, respectively [91].Furthermore, based on the improved YOLO v4 model, a support vector machine classifier was used to classify the maturity of pineapple fruits.The experimental results showed that the detection accuracies of fully mature, semi-mature, and immature fruits were 98.6%, 95%, and 97%, respectively [63].Additionally, an improved YOLO v7 model for detecting and classifying immature, semimature, and fully mature pineapple fruits was developed, with average accuracy rates of 94.4%, 95.5%, and 97.5%, respectively.The classification and detection performance of this model were better than those of YOLO v4-tiny, YOLO v5s, and YOLO v7 [66].In addition, Punnarai [92] proposed a single-image multiobject sampling technique using image enhancement approaches and developed a pineapple maturity classification method based on Mask R-CNN.The images were generated from small datasets captured under controlled conditions to enhance the training's robustness for small datasets.With this method, the training scale and the optimal threshold selection were increased.The detection accuracies for immature, semi-mature, and fully mature fruits were 86.70%, 87.81%, and 91.13%, respectively.An improved RetinaNet algorithm (ECA-RetinaNet) method was also used to classify and detect pineapple fruits at four maturity stages: growth period, immature, semi-mature, and fully mature.The detection accuracies during the growth, immature, semi-mature, and fully mature periods were 87.52%, 94.43%, 95.75%, and 95.08%, respectively, and the overall average accuracy was 93.2% [69].
The methods used in the abovementioned studies mainly detect the yellow area of the pineapple fruit epidermis for classification.The epidermis of immature fruit is green, and the epidermis of semi-mature fruit gradually becomes pale yellow from the fruit base to the crown bud.The yellow area accounts for 25% to 75% of the fruit epidermis area, and the yellow area of the epidermis reaches more than 75% of the total area for fully mature fruit.Maturity classification depends on the successful recognition of pineapple fruits.The yellow coloration of the fruit epidermis is helpful for improving the recognition accuracy of pineapple fruit.However, in complex natural environments, the classification results of semi-mature pineapple fruits are easily affected by light, occlusions, and other factors, resulting in lower accuracy for semi-mature pineapple fruits than for immature and fully mature pineapple fruits.

Pineapple Fruit Positioning Technology
After the fruit is identified, the position information of the fruit must be obtained to provide a reference for the motion planning systems of the manipulator and the end effector.Accurate location information for pineapple fruits can greatly improve the success rate of robot-assisted fruit picking.Pineapple fruit position information is mainly acquired by the visual system, and the positioning accuracy mainly depends on the performance of the visual system.The vision system includes monocular vision, binocular vision, and depth cameras.The monocular vision method obtains only two-dimensional information and cannot obtain the distance between the camera and the pineapple fruit; thus, the position of the fruit centroid is mainly used by these systems.Li [56] established the cluster point set of the center point coordinates of each fruit eye (the surface of the pineapple fruit appears as a separate piece of fruit body known as the fruit eye) by extracting the center point information of the pineapple fruit eyes.In addition, they used the roundness discrimination method to construct the center coordinates of the maximum cluster point set to obtain the two-dimensional position information of the pineapple fruit centroid (2D centroid position of the whole fruit in the image).This method is very effective for resolving centroid positioning defects in pineapple fruits with severe occlusions.
To obtain the distance between the camera and the pineapple fruit, He [37] and Li [93] used an ordinary low-cost camera to construct a binocular vision platform, used the triangulation principle to calculate visual differences, and obtained the three-dimensional information of the fruit through camera calibration, stereo calibration, and image correction.However, ordinary cameras are sensitive to light.When the depth distance was 100 cm, the depth error ranged from 2 cm to 3 cm under the conditions of the field experiment.Then, a binocular stereo vision system with a 3D industrial camera was used to determine the spatial positioning results in the depth range of 1700 mm to 2700 mm.The maximum absolute error was 42.91 mm, the average absolute error was 24.414 mm, the maximum relative error was 1.94%, and the average relative error was 1.17% [61].The formulas for average absolute error are shown in Equations ( 1) and (2).The formulas for average relative error are shown in Equations ( 3) and ( 4).

|∆Z| = Z − Z
(1) where |∆Z| is absolute error, mm; ZW is the average distance between those three points and the camera optical center, measured by a laser rangefinder, mm; ZA is the calculated distance by the current algorithm, mm; |∆E | is the average absolute error, mm; n is the number of measurements; ER is relative errors, mm; ∆E is the average relative error.
The localization stability and accuracy of ordinary binocular cameras are affected by their structure, and their images are not as realistic as those of binocular depth cameras.Zhang [68] and Chang [91] tested the positioning results of pineapple fruits obtained by a real-sense D435i binocular depth camera in an indoor environment.The maximum error was 6 mm at a depth distance of 300 mm, and the depth distance error was less than 2%.In an outdoor environment, the depth-distance error was 1.53%.The goal of fruit positioning is to provide a basis for planning picking and grasping actions.However, due to the influence of the growth environment, pineapple fruits have different growth angles, and the fruit posture is uneven.Therefore, the pose of the pineapple fruits must be estimated and predicted to achieve accurate grasping.In one study, a Kinect depth camera was used to obtain point cloud data of pineapple fruits, and point cloud registration and geometric algorithms were used to estimate the 6D pose information of the fruit through coarse and fine registration to obtain high-precision positioning information.However, the calculation time was long.In pineapple fruit positioning experiments, the coarse and fine registration tasks took 34.254 s and 0.469 s, respectively [94].
In other words, the positioning accuracy of pineapple fruits is related to the resolution, noise level, dynamic range, and other qualities of the visual sensor; the environmental conditions, such as the illumination level, shadows, reflection, and occlusions; and the accuracy and robustness of the feature extraction and matching algorithms.

Pineapple Fruit Separation Technology
The fruit separation action performed by the end effector is similar to that performed by the artificial auxiliary picking device.In the separation process, the fruit is first clamped and grabbed, and then the fruit is separated via twisting (Figure 10a) and cutting (Figure 10b) methods.In the study of twisting and cutting separation methods, Wang [95,96] designed an end effector with a double V-shaped finger structure to grab the fruit from the side and separate fruits with rotating and twisting actions, as shown in Figure 11a.The picking success rate was highest when the rotation angle was 180°, and the average singlefruit picking time was 23 s.Because the pineapple fruit is surrounded by hard leaves, the end effector cannot easily clamp the fruit from the side.Wu [46] proposed a method to clamp pineapple fruit from the top of the crown bud, and the fruit was separated by rotating and twisting the end effector, but the picking action took considerable time to complete.Wang [97] studied the pineapple fruit picking mechanism based on the shutter principle and realized fruit clamping, rotation, twisting, and picking through the opening and closing of the shutter mechanism, as shown in Figure 11b.The grasping direction of the picking mechanism is from the crown bud to the middle of the fruit, but this kind of grasping method has poor adaptability to the clamping of nonerect pineapple fruit, and the overall structure is large, which can damage pineapple plants.Xia [98] designed an end effector for pineapple fruit picking by using a double Vshaped finger structure and cutting the fruit stalk.The V-shaped groove angle of the finger was 120°, the distance between the cutter and the center of the V-shaped finger was 120 mm, the rotation angle of the cutter was 200°, and the clamping error was less than 5 mm.Although the transverse diameter of pineapple at this stage of maturity does not significantly affect the ultimate compressive strength, with increasing maturity, the ultimate compressive strength decreases, and pineapple is more likely to experience damage [99].Therefore, the end effector is suitable only for clamping and picking fruit of the same maturity.Du [100] adopted a grasping method of moving from the crown bud to the center of the fruit, clamping the fruit with a double-finger step-by-step motion clamping mechanism, and the fruit was cut with a disc mechanism to pick and separate the pineapple fruit, as shown in Figure 11c.The clamping mechanism was equipped with a pressure sensor to control the clamping force.During the picking test, the average picking time was 14.76 s, the fruit damage rate was 5%, the plant damage rate was 0%, and the fruit drop rate was 1.7%.Since the opening space of the clamping mechanism is fixed, the picking efficiency is mainly affected by the size of the fruit.Guo [35] and Anh [48] designed a spoon-shaped clamping mechanism based on the shape of pineapple fruit, which clamped the fruit from the side and cut the fruit with a disc saw to separate pineapple fruits, as shown in Figure 11d.The package clamping space of the spoon-shaped clamping mechanism is larger than that of the fruit, which can prevent clamping-induced damage to the fruit, and the overall grasping action is more stable.Francis [101] studied a three-finger clamping mechanism by using an anthropomorphic finger clamping method for pineapple fruit.Each finger had three degrees of freedom.Through multipoint contact with the fruit, the fruit grasping performance improved, and pineapple fruit was separated with a disc saw cutting mechanism.The calyx and separation layer of pineapple are shown in Figures 12 and 13, respectively.A picking success rate of 86% was obtained by clamping pineapple fruits and cutting fruit stalks [49].After the fruit enters the sleeve, when the infrared sensor detects the fruit, the air compressor inflates the air bag, and the DC motor drives the clamp to cut the fruit handle.A pressure sensor was installed on the outer layer of the air bag to obtain a stable clamping force for fruits of different sizes to ensure that the fruits were not damaged.The main reason for picking failure is that the shape of the fruit changes greatly, and the clamping force is not sufficient when smaller fruits are clamped.In addition, picking failure may occur because the shear position is on the calyx outside the separation layer, and fruit stalks at the calyx position are more difficult to cut than those in the separation layer.According to the above results, the clamping action of pineapple fruit can be divided into lateral radial clamping and axial clamping, as shown in Figure 14.The end effector structure for radial clamping mainly performs finger clamping, while the end effector structure for axial clamping has either a spoon or airbag structure.The structure of the end effector used in the twist-breaking separation method is relatively simple, and adaptive control of the clamping force is crucial for successful picking of pineapple fruit and preventing damage.The end effector used in the cutting and separation method needs a cutting mechanism, and the structure is relatively complex.Before cutting or shearing, the cutting position of the fruit stalk must be accurately identified; otherwise, it is easy for the cutting mechanism to directly damage the fruit or the plant due to a low cutting position.

Current Problems
In summary, due to the relatively small overall planting area of pineapple, the planting areas are mainly located in underdeveloped areas with large labor forces and low labor costs, and semi-automatic pineapple harvesting equipment is popular in these regions.At present, the main production application of pineapple mechanized harvesting is the use of semi-automatic harvesting equipment, especially pineapple harvesting and transportation equipment.The picking technology of pineapple fully automated harvesting equipment and pineapple intelligent harvesting equipment is still in the process of theoretical research, and there is no fully automated or intelligent equipment that can be directly applied to pineapple harvesting.At the same time, due to the limitation of the protection of fruit and bud seedlings during the picking process, the requirements for automatic picking and intelligent picking are high, and the realization of automatic picking is extremely challenging.Although many achievements have been made in the research of automatic picking technology, there are still some problems in actual production, such as a low success rate of picking, a high rate of fruit injury, and damage to plants and sprouts, resulting in the harvest of pineapple still in the stage of manual picking.Therefore, the automatic picking technology needs to be further studied, especially the design of the picking mechanism.For the agronomic mode suitable for mechanized operation, it is also necessary to design a suitable mobile walking platform.
Few researchers are investigating pineapple harvesting equipment at home or abroad, and the existing research can be considerably improved.Moreover, large agricultural machinery manufacturing enterprises are not willing to develop and manufacture special equipment for pineapple harvesting, limiting the development of intelligent and automated pineapple harvesting equipment.Although some good results have been achieved in studies on specific technology, the existing pineapple harvesting equipment and technology still have the following problems.
(1) It is difficult for pineapple harvesting equipment to reach the ground.The planting density of pineapple has no effect on the weight of single fruits, the total fruit length, or the fruit quality [102].An increase in total yield does not require a reduction in the weight of a single fruit, so the planting density of pineapple generally exceeds 60,000 plants/ha.For example, the planting density in Bali, the main cultivar in China, is 60,000 to 67,500 plants/ha [103].The yield of MD-2, an important foreign cultivar, is highest when the planting density exceeds 70,000 plants/ha [104].Pineapple is planted at a high density, and the plant is completely sealed during the fruit harvesting period.The walking mechanism is commonly used for pineapple harvesting and transportation equipment, but picking robots cannot walk without damaging the plant.
(2) The success rate of automatic pineapple picking methods is low.At present, research on automatic pineapple fruit harvesting equipment involves the use of one-time harvesting equipment and selective picking robots.One-time harvesting refers to harvesting all edible and non-edible organs of the plant.Most of the crops that are selectively harvested are perennial, or one-year-old, but the crop has a long flowering period, multiple flowering and fruit setting, or multiple germination of scale buds.Each harvest operation needs to target individual fruit and only harvest mature fruit [105].The picking mechanism of one-time harvesting equipment cannot adapt to differences in fruit size, height, or posture, resulting in fruit clamping failure or fruit separation failure, which in turn affects the picking success rate.In complex field environments, the recognition, classification, and positioning accuracy of pineapple fruit picking robots are affected by basic conditions such as the performance of the visual system and image processing algorithm, as well as illumination changes and occlusion due to pineapple leaves.It is impossible to accurately identify the fruit and obtain fruit position information, resulting in misrecognition or missed recognition and grasping failure of the end effector.This, in turn, leads to missed picking or picking failure.Even if pineapple fruits are successfully identified and located, the control accuracy and stability of the end effector during the grasping process also affect the picking success rate.
(3) The picking efficiency of pineapple fruit picking robots is low.Existing pineapple fruit picking robots mainly include a single mechanical arm, and the total cycle time of fruit picking is long, which leads to low picking efficiency.During the picking operation, the pineapple fruit picking robot needs to perform tasks such as fruit recognition, maturity classification, positioning, and picking action control of the manipulator and end effector.Moreover, illumination changes and occlusions need to be addressed.The network structure of the recognition and positioning algorithm and the path planning control strategy of the manipulator and the end effector are relatively complex.Due to the limited computing power of the control system, the processing time of perceptual information is long, resulting in poor real-time performance of pineapple fruit picking robots.
(4) Fruit damage can occur during pineapple harvesting.The picking mechanism and the end effector cannot automatically adjust the load on the pineapple fruit.During the picking process, the picking mechanism, the end effector, and the fruit directly interact.Due to obvious differences in the physical and mechanical properties of different varieties of pineapple fruits, such as their individual shape and size and compressive strength [106,107], the designs of the picking mechanism and the end effector are relatively simple, and their adaptability to different varieties of pineapple fruit is poor.When the picking mechanism and the end effector apply a fixed separation load to the fruit, some fruits are subjected to excessive load, and damage occurs.Moreover, after the pineapple fruit is successfully picked, collisions may occur between the individual fruits or between the fruit and the conveying mechanism during the conveying process, and some fruits may be damaged due to excessive collisions or extrusions.
(5) Little attention has been given to plant damage during mechanized pineapple harvesting.At present, pineapple seedlings are bred mainly by suckers, terminal buds, descendants, and ground buds.Seedling breeding is also an important part of the economic benefit of pineapple planting.The degree of damage to pineapple plants and their sprouts during fruit picking is related to the quantity and quality of the bred pineapple seedlings.The design and working principle of existing picking mechanisms are based on the combination of clamping and breaking, twisting, or cutting.These methods aim to accurately and nondestructively separate fruits.However, few studies have investigated the mechanisms by which pineapple plants and sprouts are damaged and their protection strategies.In particular, although automatic one-time pineapple picking methods have high picking efficiency, the damage to pineapple plants and sprouts during their operation has been less studied, and a response method needs to be proposed.(6) The cost of pineapple harvesting equipment is high.Due to the limitations of pineapple planting agronomy and the low versatility of existing equipment, current pineapple harvesting equipment is mainly based on order-based research and manufacturing.In particular, large-scale pineapple harvesting and transportation equipment used in foreign applications and large-capacity, high-clearance self-propelled pineapple harvesting equipment used in domestic applications have high overall manufacturing costs.In terms of pineapple fruit picking robots, in unstructured field environments, the performance requirements of visual systems, multi-degree-of-freedom manipulators, end effectors, and control systems are high, increasing the overall cost of such robots.

Suggestions and Prospects
With the development of the Chinese economy, the improvement in people's living standards, and the demand for continued improvement, the demand for fresh pineapple fruit and canned pineapple products is increasing.Moreover, the dependence on overseas pineapple imports has been reduced, promoting the continuous expansion of the Chinese pineapple planting industry and its industrial scale.With the "14th Five-Year Plan" period, the work performed by agricultural workers, rural workers, and farmers in China has changed, promoting rural revitalization and accelerating the modernization of agricultural and rural areas.This finding highlights the urgent need for new development opportunities for pineapple harvesting equipment and promotes the development of intelligent and green technology.In the future, research on pineapple harvesting equipment and technology should focus on the following aspects.
(1) The organic combination of agricultural machinery and agronomy is an inevitable trend in the modern pineapple industry.At present, the agronomic modes of pineapple planting are diverse, the planting density is large, and the technical requirements for seedling protection limit the performance of existing harvesting and transportation equipment, increasing the difficulty of researching automatic and intelligent harvesting equipment.Therefore, in terms of agronomic parameters, adjusting the row spacing of pineapple plants can be considered.For example, for the Bali variety pineapple, the strip planting mode (4 standard rows + 1 wide row) can be adopted, while for the Tainong variety pineapple, the wide-narrow row planting approach (narrow 2 rows + 1 wide row) can be adopted, with wide rows spaced by more than 80 cm [23].With respect to seedling breeding, the traditional propagation of pineapple seedlings has several disadvantages, including a low propagation coefficient, slow speed, easy accumulation, and the potential spread of disease.Tissue culture technology is an important in vitro propagation approach for pineapple plants, ensuring efficient seedling breeding and consistent traits among different plants [108,109].Through tissue culture of explants and large-scale factory approaches for raising seedlings, seedlings can be protected during the operation of harvesting equipment.By integrating agricultural machinery and agronomy, the working environment of pineapple harvesting equipment can be improved, the difficulty of pineapple harvesting can be reduced, the efficiency of pineapple harvesting can be increased, and mechanized pineapple harvesting equipment can be rapidly developed.
(2) The integration of automated and intelligent technology should be the focus of pineapple harvesting equipment research.Due to the various commercial uses of pineapple fruit, constraints such as harvesting production costs, fruit quality control, and the different requirements for fruit maturity must be considered, and pineapple harvesting operations are reasonably refined despite these limitations.Moreover, two mechanized pineapple harvesting modes, one-time harvesting and selective harvesting, have been developed.After overcoming the difficulty of reproducing the mother plant of pineapple seedlings, one-time harvesting is a highly efficient mechanized harvesting method for pineapple fruits that do not require consistent maturity.This approach integrates automatic regulation, intelligent control, and other technologies into the picking mechanism and applies intelligent monitoring technology under different operating conditions.Furthermore, the unmanned operation of pineapple harvesting equipment with unmanned driving technology has improved the quality and efficiency of one-time pineapple harvesting equipment.To obtain fresh and sellable pineapple fruits with consistent maturity, selective picking robots are the best solution for mechanized harvesting operations.Tactile end effectors, multiple sensors, fast and highly precise recognition and positioning algorithms, versatile and low-cost robot operating systems, multi-manipulator collaboration systems, collaborative control systems for robots, and cloud robot data analysis and processing are important research topics for pineapple fruit picking robots.With continued research, nondestructive fruit picking, accurate identification, rapid decision making, and efficient harvesting can be achieved in unstructured environments in the future.
(3) Modular design and universal expansion are important research directions for pineapple harvesting equipment.According to the use of pineapple fruit and harvesting requirements, modular designs with one-time picking mechanisms, selective picking manipulators, planting systems, spraying systems, and other field production management modules can be realized through multifunctional self-propelled mobile platforms.Combined with the common technical requirements of similar fruit picking systems, the picking mechanism, the end effector of the manipulator, and the recognition and positioning algorithm are modularly designed to develop pineapple harvesting equipment that can be applied in diverse application scenarios.Through the modular design and universal expansion of pineapple harvesting equipment, the utilization rate of the equipment can be improved, and costs can be reduced.

Conclusions
At present, Chinese agricultural machinery and pineapple harvesting equipment have rapidly developed.This paper reviews the research status of pineapple harvesting equipment and highlights that existing technology has problems such as fruit and plant damage, low harvesting efficiency, and a low picking success rate.Agricultural machinery and agronomy could be combined, and the application of automated and intelligent technology, modular and generalized designs, and future research directions are proposed, with a focus on pineapple harvesting equipment.This review can serve as a reference for follow-up research on pineapple harvesting equipment.Moreover, the presented analysis may promote the development of automated and intelligent technology, accelerate the commercial application of automatic pineapple harvesting equipment and technology, realize the automatic and intelligent harvesting of pineapple, reduce labor intensity and production costs for fruit farmers, enhance the competitiveness of Chinese pineapple in the international market, and promote the healthy and sustainable development of the pineapple industry.

Figure 1 .
Figure 1.Schematic diagram of the structural composition of the pineapple plant.

Figure 2 .
Figure 2. Pineapple harvested area, production, and production costs in China.(a) Pineapple harvested area and production in China, 2012-2022; (b) costs of materials and labor in various stages of pineapple production.

Figure 7 .
Figure 7. Self-propelled pineapple harvesting truck with a high-gap crawler.

Figure 8 .
Figure 8. Flexible finger roller for the pineapple harvesting mechanism.

Figure 12 .
Figure 12.Schematic diagram of the calyx of pineapple.

Figure 13 .
Figure 13.Schematic diagram of the separation layer of pineapple.

Figure 14 .
Figure 14.Schematic diagram of the clamping action of pineapple fruit for end-effector.(a) Radial clamping of pineapple fruit.(b) Axial clamping of pineapple fruit.

Table 1 .
Comparison of pineapple fruit recognition algorithms.