Design and Experimentation of Comb-Spiral Impact Harvesting Device for Camellia oleifera Fruit

Abstract

Camellia oleifera is one of the four largest woody oil species in the world, with more than 5 million hectares planted in China alone. Reducing bud damage and improving harvesting net rate and efficiency have become the key challenges to mechanized harvesting of Camellia oleifera fruits. This paper presents a novel comb-spiral impact harvesting device primarily composed of four parts, which are lifting mechanism, picking mechanism, rotating mechanism, and tracked chassis. The workspace of the four-degree-of-freedom lifting mechanism was simulated, and the harvesting reachable area was maximized using MATLAB R2021a software. The picking mechanism, which includes dozens of spirally arranged impact pillars, achieves high harvesting efficiency through impacting, brushing, and dragging, while maintaining a low bud shedding rate. The rotary mechanism provides effective harvesting actions, and the tracked chassis guarantees free movement of the equipment. Simulation experiments and field validation experiments indicate that optimal performance can be achieved when the brushing speed is set to 21.45 r/min, the picking finger speed is set to 341.27 r/min, and the picking device tilt angle is set to 1.0°. With these parameters, the harvesting quantity of Camellia oleifera fruits is 119.75 kg/h, fruit shedding rate 92.30%, and bud shedding rate as low as 9.16%. This new model for fruit shedding and the comb-spiral impact harvesting principle shows promise as a mechanized harvesting solution for nut-like fruits.

1. Introduction

Camellia oleifera, a distinctive native woody oil species planted in the southern hilly regions of China, yields seed with a high nutritional value [1]; specifically, the seeds have an unsaturated fatty acid content of 93%, exceeding that of traditional oilseeds, such as peanut and rapeseed oils [2,3,4]. The central government of China aims to increase the national Camellia oleifera plantation area to over 6 million hectares by 2025 [5]. With strong support from both national and local governments, the cultivation and development of the Camellia oleifera industry have been extensively promoted.

Presently, the harvesting of Camellia oleifera fruits predominantly relies on manual or semi-mechanized methods, characterized by low levels of mechanization, high labor intensity, and low economic efficiency [6,7,8]. Given the extensive planting scale and a market potential in the billions, mechanized harvesting has emerged as a critical constraint on the rapid advancement of the Camellia oleifera industry.

In recent years, both domestic and international research institutions have conducted extensive studies on Camellia oleifera fruit harvesting equipment. Based on the different mechanisms of action of harvesting devices on Camellia oleifera trees, they can be categorized into vibration-based, comb-based, and contact-based harvesting methods [9]. Du et al. developed a tracked high-gap Camellia oleifera fruit vibration harvesting machine, employing a crank–rocker mechanism to drive multiple rows of picking rods in reciprocating motion and inducing vibrations of a certain frequency on the Camellia oleifera tree canopy to facilitate fruit detachment [10,11]. While they elaborated on the overall machine and key component designs, they did not explore the relationship between vibration frequency and fruit shedding rate. Wang et al. designed a vibration-based Camellia oleifera green fruit harvesting machine, utilizing a rotatable picking arm to move the picking head to the tree trunk position and clamp it, adjusting the crank speed by controlling the motor speed to regulate the output vibration frequency of the vibrator [12]. The experimental results indicate that, as the vibration frequency gradually increases, both the harvesting rate of Camellia oleifera fruits and the bud shedding rate increase progressively. Although this harvesting method achieves a higher harvesting rate, due to the shallow root system of the Camellia oleifera tree, harvesting can only be performed by clamping the main trunk. Additionally, since the planting conditions typically include sandy soil fields, this can easily lead to damage to the main trunk and forced vibration-induced breakage of the tree root system [13]. In research regarding vibratory harvesting for forest fruits, both excessively high and unduly low frequencies can diminish the harvesting efficiency; an overly large amplitude may inflict damage upon the fruits and the arboreal body, whereas an insufficient amplitude might cause difficulties in fruit detachment [14,15,16,17]. In other words, the vibrating harvesting device demonstrates high fruit-harvesting efficiency, but it causes significant damage to the flower buds and the tree trunk, which negatively impacts the fruit-bearing rate of the Camellia oleifera trees in the following years.

Since Camellia oleifera fruits are primarily located on the outer surface of the tree crown, with fewer fruits in the inner canopy, many more researchers in the forestry fruit harvesting industry have gradually applied the brushing and combing harvesting principle to Camellia oleifera fruit harvesting [18]. Wu et al. designed a twisting comb-type Camellia oleifera fruit harvesting end effector, utilizing the combined action of torque, shear force, and impact force generated during the rotation of the twisting comb component to achieve fruit harvesting [19]. However, comb-based harvesting devices are limited by the length of the picking fingers and cannot harvest fruits from the inner layers of the tree. Rao et al. developed a layered Camellia oleifera fruit harvesting device, where the upper and lower rotating rubber roller components of the picking head rotate synchronously in reverse, compressing and colliding with the Camellia oleifera fruits to facilitate harvesting [20]. Wang et al. designed a lightweight electric hydraulic Camellia oleifera fruit harvesting machine, employing a small hydraulic pump station to drive the rotation and rolling of the rubber roller components on a diamond-shaped bearing, continuously squeezing and rolling the Camellia oleifera fruits off the branches [21]. While contact-based harvesting equipment exhibits high harvesting efficiency, the rotation of the upper and lower rubber rollers may entangle with tree branches, resulting in a low fruit harvesting rate and poor adaptability to Camellia oleifera fruits of different sizes.

In summary, some existing harvesting equipment types, such as vibration-based, combing, and squeezing methods, show single operation modes and suboptimal harvesting performance. Developing harvesting machinery that integrates multiple operation modes is crucial for achieving efficient Camellia oleifera fruit harvesting with minimal flower bud damage. Field experiments with different devices have shown that comb-based harvesting devices have a higher fruit cleaning rate and lower bud shedding rate, while vibration-based devices exhibit higher harvesting efficiency [22].

This study addresses the challenges of manual harvesting and the low efficiency of mechanical harvesting devices in hilly regions by developing a novel comb-spiral impact harvester for Camellia oleifera fruits. Combining the operation principle of comb and contact types, the tooth comb structure is composed of several groups of spiral impact fingers, and the rotary lifting mechanism is designed to improve picking rate and harvesting efficiency while minimize flower bud damage. Based on the design of key components, this study conducts a single-factor and orthogonal experiment on the picking finger structure parameters, prototypes a comb-spiral impact harvesting devices, and performs harvesting performance experiments in the typical hilly regions of Ankang, Shaanxi Province.

2. Materials and Methods

2.1. Camellia oleifera Fruit and Flower Bud Material

This study selected the “Qinba No. 1” variety of Camellia oleifera fruits from the Camellia oleifera plantation located in Hongshan Township, Hanbin District, Ankang City, Shaanxi Province, China. The plantation is well-managed, with a clear planting layout, including a minimum row spacing of 3.5 m and a minimum plant spacing of 2.0 m within rows. As Figure 1 shows, the plantation is easy for small and medium-sized equipment to pass through and has the conditions needed for mechanized harvesting operations. A random sampling method was used to select five sampling points, each containing a 3 × 3 arrangement of Camellia oleifera trees. Basic parameters of the Camellia oleifera trees were measured using a tape measure, as

Table 1. Basic parameters of Camellia oleifera forest.

Index	Crown Diameter (m)	Tree Height (m)	Minimum Result Height (m)	Result Layer Thickness (m)	Camellia oleifera Fruit Diameter (mm)	Bud Length Diameter (mm)	Short Diameter of Bud (mm)
Numerical range	1.52~2.13	1.58~2.42	0.48	0.25~0.33 m	18.99~58.78	13.43~19.85	7.29~9.67
Standard Deviation	0.10	0.14	0	0.013	6.63	1.07	0.40

shows. The critical detachment force of Camellia oleifera fruits and flower buds was measured using a force gauge, as

Table 2. The critical shedding force of Camellia oleifera fruit and bud.

Index	Camellia oleifera			Bud Shedding Force (N)
	Transversal Shedding Force (N)	Longitudinal Shedding Force (N)	Lateral Shedding Force (N)
Numerical range	9.27~45.88	9.27~45.88	9.27~45.88	4.28~16.30
Standard Deviation	6.10	4.65	5.17	2.00

shows.

2.2. Overall Structure and Working Principle

Figure 2 shows the structure of the comb-spiral impact harvesting device for Camellia oleifera fruit, which includes a lifting mechanism, power box, counterweight, control box, rotating mechanism, tracked chassis, picking mechanism, and brush actuator.

The entire system is powered by a battery pack integrated into the power box, ensuring the normal operation of each component, including the hydraulic pump station. The power box is equipped with a 48 V/100 Ah lithium battery pack, along with an additional backup battery pack. Each battery pack can provide continuous power supply for at least 6 h (including the energy consumption of the hydraulic system and the drive motor), meeting the daily operation requirements. Initially, the remote control guides the tracked chassis to the midpoint between the two outermost rows of Camellia oleifera trees in the forest. The hydraulic lifting and overturning cylinders are then adjusted via the hydraulic pump station to minimize the ground clearance of the picking mechanism. The rotating mechanism is activated to position the picking mechanism at a 1/4 range of the Camellia oleifera trees on the left side, and the four driving motors of the picking mechanism are engaged to propel the picking assembly and fingers.

Subsequently, the hydraulic lifting cylinder elevates the picking mechanism from the bottom to the top of the tree crown, inducing disturbances within the Camellia oleifera tree crown. This process prompts the fruits and branches to interact with the picking components, resulting in detachment when the combined external force exceeds the fruit stalk connection force, completing a picking operation.

After picking 1/4 of the outer two trees, the machine is repositioned at the midpoint of the four Camellia oleifera trees. The position of the picking mechanism is adjusted through the rotating mechanism (at angles of 45°, 135°, 225°, and 315° relative to the forward direction of the crawler chassis), and subsequently, 1/4 of the surrounding four trees are picked in sequence.

2.3. Design of Key Components

2.3.1. Design of the Lifting Mechanism

The lifting mechanism, powered by a hydraulic pump station and cylinders, enables the vertical motion and flipping of the picking mechanism, thereby enhancing the coverage of the tree canopy and ensuring the collection rate. The rational design of this mechanism is crucial to preventing the overturning of the entire machine.

As Figure 3 illustrates, the lifting mechanism comprises a pillar, crossbeam, connecting rod, lifting cylinder, tipping cylinder, and support frame. The pillar is fixed to the rotating mechanism, while the crossbeam’s end is hinged to the upper end of the pillar. The picking mechanism is hinged to the front end of the crossbeam, with the lifting cylinder hinged to the upper surface of the rotating mechanism and the lower surface of the crossbeam. The overturning cylinder is hinged to the upper surface of the crossbeam and the upper end of the connecting rod of the picking mechanism.

The hinge centers of the lifting cylinder are denoted as G and F, and the hinge centers of the tipping cylinder are denoted as C and D. AB represents the pillar, DE is the connecting rod, and BE is the crossbeam.

Given the substantial mass of the picking mechanism, it is essential to strategically position the lifting and tipping cylinders on the crossbeam. To maintain operational balance, a counterweight block is positioned on the support frame behind the pillar.

The lifting mechanism offers four degrees of freedom, with the lifting cylinder rotating between 35° and 75° and having a length ranging from 825 mm to 1425 mm. The tipping cylinder rotates between 25° and 65°, with a length varying from 345 mm to 545 mm.

2.3.2. Design of Picking Finger

The picking mechanism consists of multiple sets of spiral impact fingers. Each spiral uniaxial picking finger is composed of a striking column and a rotating shaft, with the striking columns welded along three interwoven spiral lines on the rotating shaft. The spiral impact fingers collide with the pruned Camellia oleifera fruits, generating an impact force that exceeds the critical detachment force, completing the picking within a very short time to ensure high efficiency. The picking fingers are evenly spaced, with the gap between adjacent striking columns larger than the flower bud diameter but smaller than the Camellia oleifera fruit diameter, allowing flower buds to pass through the gaps and minimizing damage to the buds.

The picking finger rotates along the axis of the brush actuator, gently combing through the branches. By using the striking pillar to impact the Camellia oleifera fruits and drag them along the brush actuator’s rotation, the picking process is efficiently completed.

During the picking operation, the structural parameters of the picking finger have a great influence on the picking results. These parameters are the diameter d_z and length l_z of the spindle, the diameter d_j and length l_j of the striking pillar, the gap δ_j between the two striking pillars, and the pitch, length, number, and initial spacing of the spirals, as Figure 4 shows.

The design range of the above parameters was determined by combining the average diameters of the Camellia oleifera fruits and bracts, as

Table 3. Design parameters and range of picking fingers.

Parameters	Range	Parameters	Range
Diameter of spindle (mm)	12~16	Pitch of spiral thread (mm)	100~110
Length of spindle (mm)	330~360	Spiral length (mm)	300~330
Diameter of striking pillar (mm)	6~8	Number of helix groups	3~5
Length of striking pillar (mm)	10~15	Spiral start interval (mm)	8~10
Clearance of two-strike pillar (mm)	13.61~20.52	Adjacent shaft clearance (mm)	45~50

shows.

The impact between the striking pillar and the Camellia oleifera fruit is extremely short, so at the moment of impact, the Camellia oleifera fruit can be considered an unconstrained stationary body. When the Camellia oleifera fruit slides along the axial direction of the harvesting finger, the interaction between the two remains unchanged. The interaction between the Camellia oleifera fruit and the harvesting finger can be simplified as a two-dimensional plane collision effect. By analyzing the forces acting on the Camellia oleifera fruit, the harvesting process can be simplified into four stages: fruit entry, contact, brushing, and dropping.

The brush actuator moves the picking finger into the canopy of the Camellia oleifera tree. With the bottom-up rotary motion of the brush actuator and the anisotropic synchronized rotary motion of the picking finger, the pendulous growing Camellia oleifera fruit is forced into the picking finger between the two striking pillars. As Figure 5a shows, the Camellia oleifera fruit had not yet made contact with the picking finger.

When the Camellia oleifera fruit contacts the striking pillars, it experiences equal impact forces from the left and right striking pillars, both of which are directed towards the center of the Camellia oleifera fruit. Additionally, due to the roughness of the Camellia oleifera fruit surface, friction forces are generated at the contact point along the tangent to the Camellia oleifera fruit in the direction opposite to its motion.

As a result of the combined rotation and frictional forces acting on the Camellia oleifera fruit by the picking finger, the Camellia oleifera fruit accelerates upwards along its center, as Figure 5b illustrates. Interaction with the picking finger alters the forces acting on the Camellia oleifera fruit, and an analysis of the forces acting on the Camellia oleifera fruit reveals

$$\left\{\begin{array}{l}{\mathit{F}}_{\mathit{g}}={\mathit{F}}_{z1}\cos {\theta }_{z1}+{\mathit{F}}_{z2}\cos {\theta }_{z2}-{\mathit{f}}_{z1}\sin {\theta }_{z1}-{\mathit{f}}_{z2}\sin {\theta }_{z2}-\mathit{G}\hfill \\ {\mathit{f}}_{z1}\sin {\theta }_{z1}={\mu }_{\mathit{g}}{\mathit{F}}_{z1}\\ {\mathit{f}}_{z2}\sin {\theta }_{z2}={\mu }_{\mathit{g}}{\mathit{F}}_{z2}\\ {\mathit{F}}_{z1}\sin {\theta }_{z1}+{\mathit{f}}_{z1}\cos {\theta }_{z1}-{\mathit{F}}_{z2}\sin {\theta }_{z2}-{\mathit{f}}_{z2}\cos {\theta }_{z2}=0\hfill \end{array}\right.$$

(1)

where ${\mathit{F}}_{\mathit{g}}$ is the vertical upwards force of the Camellia oleifera fruit after impact (N), ${\mathit{F}}_{z1} \mathrm{and} {\mathit{F}}_{z2}$ are the positive pressure on Camellia oleifera fruit (N), ${\mathit{f}}_{z1} \mathrm{and} {\mathit{f}}_{z2}$ are the friction of picking fingers on Camellia oleifera fruit (N), ${\theta }_{z1}$ and ${\theta }_{z2}$ are the angle between the colliding picking fingers and the line connecting the rotational centers of the two picking fingers (°), and ${\mu }_g$ is the friction factor of Camellia oleifera fruit in contact with picking fingers, G is the gravitational force of the Camellia oleifera fruit.

As the picking mechanism brushes upward and the picking fingers rotate, the drooping Camellia oleifera fruits are brushed upward. At this time, the Camellia oleifera fruit is subjected to the upwards impact force of the branch of the picking finger and the dragging force of the comb brush actuator, forcing the displacement of the Camellia oleifera fruit to reach the limiting position of the thin branch, as Figure 5c shows.

It is known that, at this time, the critical condition of Camellia oleifera fruit shedding is

$$\left\{\begin{array}{l}{F}_{z1}\cos {\theta }_{z1}+F_{z2}\cos {\theta }_{z2}+{\mu }_g{F}_{\mathrm{z}1}+{\mu }_g{F}_{z2}>G+F_j\hfill \\ △s=v_{z}t+\sqrt{2\cos (\pi {\omega }_{z}t/30)}{R}_z>△l\hfill \end{array}\right.$$

(2)

where $△s$ is the running distance between Camellia oleifera fruit and picking finger from contact to separation (m), $v_z$ is the vertical velocity of the comb actuator (m/s), t is the time from contact between Camellia oleifera fruit and picking finger to shed (s), $F_j$ represents the longitudinal critical abscission force of Camellia oleifera fruit (N), ${\omega }_z$ is the picking finger rotational velocity (r/min), $R_z$ is the radius of rotation of the striking pillar (m), and $△l$ is the fruit stalks and branch extension limit (m).

When the Camellia oleifera fruit reaches its extreme position under the combined action of the picking finger and the comb brush actuator, the fruit stem is forced to break, causing the fruit to detach from the picking finger at a critical upwards velocity $v_g$, as Figure 5d shows.

Equation (2) indicates that the shedding of Camellia oleifera fruits is primarily influenced by the vertical speed of the comb actuator, the rotational speed of the picking finger, and the radius of rotation.

The picking device will have a combing and impact on the Camellia oleifera fruit in addition to a spiral dragging effect. As Figure 5 shows, picking finger spiral rows of striking pillars to the end of the picking finger drags the Camellia oleifera fruit to the outside of the comb brush to facilitate the collection of fallen Camellia oleifera fruit. Camellia oleifera fruit are subjected to the normal thrust and tangential friction, gravity, and stalk bonding forces of spirally arranged striking pillars. The critical condition of relying on the dragging effect of the striking pillar to make the Camellia oleifera fruit fall off is as follows (one of the two can be satisfied):

$$\left\{\begin{array}{l}{F}_{\mathrm{z}}\sin {\theta }_z+f_z\cos {\theta }_z+G=F\sin ({\theta }_z+\theta )+G\ge F_t\cos \alpha +F_j\sin \alpha \hfill \\ F_z\cos {\theta }_z+f_z\sin {\theta }_z+G=F\cos ({\theta }_z+\theta )+G\ge F_t\sin \alpha +F_j\cos \alpha \hfill \end{array}\right.$$

(3)

where ${\theta }_{\mathrm{z}}$ is the lead angle (°), $\theta$ is the angle between the direction of the collision force between the striking pillar and the Camellia oleifera fruit and the tangential direction of the helix (°), F_z is the normal extrusion force on the striking pillar (N), and f_z is the tangential friction force on the striking pillar (N).

The helix angle of the striking pillar can be calculated using Equation (4).

$${\theta }_{\mathrm{z}}=\arctan (l_l/(\pi d_z+2\pi l_j))$$

(4)

Moreover, an analysis of Figure 6 indicates that the tangential friction between the striking pillar and the Camellia oleifera fruit impedes the fruit’s movement toward the end of the picking finger. To achieve the spiral dragging effect, it is necessary to ensure that the horizontal component of the propulsion force exceeds the horizontal component of the frictional force.

$$\cot {\theta }_{\mathrm{z}}>{\mu }_{jc}=\cot {\theta }_{z1}$$

(5)

where ${\mu }_{jc}$ is the coefficient of friction between the striking pillar and the Camellia oleifera fruit.

From the trigonometric relationship and the sine-cosine theorem, organizing Equation (5) yields

$${\theta }_{z1}-\pi <{\theta }_z<{\theta }_{z1}$$

(6)

Figure 6 clearly shows that as the distance between the Camellia oleifera fruit and the rotating shaft decreases, ${\theta }_{\mathrm{z}}$ decreases and $\theta$ increases, aligning with the criteria outlined in Equation (6) for the drag picking of fruit by the striking pillar. The shedding of Camellia oleifera fruits is primarily influenced by the diameter of the spindle and the pitch and length of the striking pillar, as indicated by Equations (2) and (3).

2.4. Kinematic Analysis of the Lifting Mechanism

2.4.1. Forward and Reverse Kinematic Analysis

Positive kinematic analysis is a method of calculating the position and attitude of the operating object’s coordinate system with respect to the base coordinate system, given the angular values of each joint [23,24,25]. The lifting mechanism can be simplified as a tandem multi-rigid structure consisting of 3 joints. The picking device is fixed to the 3rd joint and has no joint angle (equivalent to adding a section of connecting rod after the 3rd joint). Based on the D–H parametric method [26,27], the D–H linkage coordinate system of the lifting mechanism is established, as Figure 7 shows.

According to the D–H parameter method, parameter ${\alpha }_{\mathrm{i}-1}$ represents the link twist angle, which is the angle of rotation around axis $x_{\mathrm{i}-1}$, from axis $z_{\mathrm{i}-1}$ to axis $z_i$. Parameter $a_{\mathrm{i}-1}$ represents the link length, which is the distance along axis $x_{\mathrm{i}-1}$, from axis $z_{\mathrm{i}-1}$ to axis $z_i$. Parameter ${\theta }_{\mathrm{i}}$ represents the joint angle, which is the angle of rotation around axis $z_i$, from axis $x_{\mathrm{i}-1}$ to axis $x_{\mathrm{i}}$. Parameter $d_{\mathrm{i}}$ represents the link offset, which is the distance along axis $z_i$, from axis $x_{\mathrm{i}-1}$ to axis $x_{\mathrm{i}}$. The D-H link parameters for the lifting mechanism’s mechanical arm are ultimately obtained, as

Table 4. D-H parameters of the lifting mechanism integrated with the toothed comb-picking mechanism.

Joint $\mathit{i}$	${\mathit{\alpha }}_{\mathit{i}-1}$ (°)	${\mathit{a}}_{\mathit{i}-1}$ (mm)	${\mathit{\theta }}_{\mathit{i}}$ (°)	${\mathit{d}}_{\mathit{i}}$ (mm)	Articulation Variable (°)
1	0	0	${\theta }_1$	0	(−45~45)
2	−90	216	${\theta }_2$	0	(−15~30)
3	0	581	${\theta }_3$	0	−(${\theta }_2+{\theta }_4$)
4	0	325	${\theta }_4$	180	(−10~60)

shows.

In actual work, the number of degrees of freedom of the mechanism: $M=3n-2j_1-j_2=3\times 6-2\times 7=4$. The number of moving components, $n=6$, includes columns AB, connecting rods BE, tilting cylinders CD (cylinder body and piston rod), lifting cylinders GF (cylinder body and piston rod), and end picking devices. The number of low-order joints $j_1=7$, with 5 rotating joints, i.e., hinge points B, C, D, G, and F, and 2 moving joints, i.e., the moving joints between the cylinder body and piston rod in the tilting cylinder and lifting cylinder. The number of high-order or composite hinges $j_2=0$.

The homogeneous transformation matrix for the connecting rod of the lifting mechanism is as follows:

$${}_1{}^{0}T=\left[\begin{array}{cccc}{c}_1& -s_1& 0& 0\\ s_1& c_1& 0& 0\\ 0& 0& 1& 0\\ 0& 0& 0& 1\end{array}\right] {}_2{}^{1}T=\left[\begin{array}{cccc}{c}_2& -s_2& 0& 0\\ 0& 0& 1& 0\\ -s_2& -c_2& 0& 0\\ 0& 0& 0& 1\end{array}\right] {}_3{}^{2}T=\left[\begin{array}{cccc}{c}_3& -s_3& 0& {\alpha }_2\\ s_3& c_3& 0& 0\\ 0& 0& 1& 0\\ 0& 0& 0& 1\end{array}\right] {}_4{}^{3}T=\left[\begin{array}{cccc}{c}_4& -s_4& 0& {\alpha }_3\\ s_4& c_4& 0& 0\\ 0& 0& 1& d_4\\ 0& 0& 0& 1\end{array}\right]$$

(7)

In equations $\cos {\theta }_i=c_i$, $\sin {\theta }_i=s_i$, and $i$ are 1, 2, 3, and 4, respectively.

Based on the homogeneous transformation matrices of each link in Equation (7), the transformation matrix of the harvesting device relative to the base coordinate system of the lifting mechanism is as follows [28]:

$${}_4{}^{0}T={}_1{}^{0}T{}_2{}^{1}T{}_3{}^{2}T{}_4{}^{3}T=\left[\begin{array}{cccc}{n}_x& o_x& a_x& p_x\\ n_y& o_y& a_y& p_y\\ n_z& o_z& a_z& p_z\\ 0& 0& 0& 1\end{array}\right]$$

(8)

The position vector $p$ is

$$\left\{\begin{array}{l}{p}_x=(581c_3+216)c_1{c}_2-581c_1{s}_2{s}_3\hfill \\ p_y=(581c_3+216)c_1{c}_2-581s_1{s}_2{s}_3+180c_1\hfill \\ p_z=(581c_3+216)c_1{c}_2-581c_2{s}_3\hfill \end{array}\right.$$

(9)

The attitude matrix is

$$\left[\begin{array}{ccc}n& o& a\end{array}\right]=\left[\begin{array}{ccc}{n}_x& o_x& a_x\\ n_y& o_y& a_y\\ n_z& o_z& a_z\end{array}\right]$$

(10)

which $\left[\begin{array}{c}{n}_x\\ n_y\\ n_z\end{array}\right]=\left[\begin{array}{c}{c}_1{c}_2{c}_{34}-c_1{s}_2{s}_{34}\\ s_1{c}_2{c}_{34}-s_1{s}_2{s}_{34}\\ -s_2{c}_{34}-c_2{s}_{34}\end{array}\right]$$\left[\begin{array}{c}{o}_x\\ o_y\\ o_z\end{array}\right]=\left[\begin{array}{c}-c_1{c}_2{s}_{34}-c_1{s}_2{c}_{34}\\ -s_1{c}_2{s}_{34}-s_1{s}_2{c}_{34}\\ 0\end{array}\right]$$\left[\begin{array}{c}{a}_x\\ a_y\\ a_z\end{array}\right]=\left[\begin{array}{c}-s_2{c}_{34}-c_2{s}_{34}\\ s_2{s}_{34}-c_2{c}_{34}\\ 0\end{array}\right]$.

In equations $\cos {\theta }_i=c_i$, $\sin {\theta }_i=s_i$, $\cos ({\theta }_i+{\theta }_j)=c_{ij}$, $\sin ({\theta }_i+{\theta }_j)=s_{ij}$, $i$, and $j$ retrieve 1, 2, 3, and 4, respectively.

The inverse kinematic analysis of the lifting mechanism involves deriving the positions, velocities, and accelerations of each joint based on the position, orientation, and velocity of the end effector. It is more challenging to solve than forward kinematics and often prone to having no solution or multiple solutions [29,30,31,32].

${\theta }_1$ can be derived from $p_x$ and $p_y$.

$$\mathrm{From} \left\{\begin{array}{l}{p}_x=(581c_3+a_2)c_1{c}_2-581c_1{s}_2{s}_3\hfill \\ p_y=(581c_3+a_2)c_1{c}_2-581s_1{s}_2{s}_3+180c_1\hfill \end{array}\right.$$

(11)

It can be concluded that ${\theta }_1=\arccos \left({\displaystyle \frac{p_y-p_x}{180}}\right)$.

By multiplying both sides of the equation by ${}_1{}^{0}T^{-1}({\theta }_1)$, we obtain:

$${}_1{}^{0}T^{-1}({\theta }_1){}_4{}^{0}T={}_2{}^{1}T({\theta }_2){}_3{}^{2}T({\theta }_3){}_4{}^{3}T(0)$$

(12)

$$\left[\begin{array}{cccc}{c}_1& s_1& 0& 0\\ -s_1& c_1& 0& 0\\ 0& 0& 1& 0\\ 0& 0& 0& 1\end{array}\right]\left[\begin{array}{cccc}{n}_x& o_x& a_x& p_x\\ n_y& o_y& a_y& p_y\\ n_z& o_z& a_z& p_z\\ 0& 0& 0& 1\end{array}\right]={}_4{}^{1}T$$

(13)

$${}_4{}^{1}T={}_2{}^{1}T{}_3{}^{2}T{}_4{}^{3}T$$

(14)

Let the corresponding elements on both sides of Equations (13) and (14) be equal; then, we find

$$\left\{\begin{array}{l}-216s_2+180=p_z\\ 216c_2{s}_4=c_1{p}_y-s_1{p}_x\end{array}\right.$$

(15)

It can be concluded that ${\theta }_2=\arcsin ({\displaystyle \frac{180-p_z}{216}})$, ${\theta }_4=\arcsin ({\displaystyle \frac{c_1{p}_y-s_1{p}_x}{216\sqrt{1-{\left(\frac{180-p_z}{216}\right)}^2}}})$, and ${\theta }_3=-({\theta }_2+{\theta }_4)$.

The obtained angle values of each joint above clarify the relationship between the link lengths and joint angles of the lifting mechanism during the picking operation and the position and orientation of the picking device. This provides theoretical support for solving the operational space of the lifting mechanism of the integrated comb-spiral impact harvesting device.

2.4.2. Workspace Analysis

The working space of the lifting mechanism robotic arm is the collection of positions reached by the end effector when the joints of the robotic arm are in random combinations within their working ranges [33]. The methods for determining the working space of a robotic arm can be categorized into three types: graphical methods, analytical methods, and numerical methods.

When these traditional methods are applied to complex multi-joint lifting mechanisms, they all face the challenge of ensuring accuracy. Graphical and analytical methods struggle to accurately depict the full extent of the working space, while numerical methods are cumbersome to calculate and cannot guarantee the reliability of boundary surfaces.

With the rapid development of computer science and intelligent algorithms, the difficulty of performing large-scale data composite operations has significantly decreased. Therefore, this paper adopts the Monte Carlo method, a random probability algorithm derived from numerical methods, to associate the solution with a certain probability model.

Numerical simulations were conducted using MATLAB R2021a, and the Rand function in MATLAB was utilized to generate 100,000 sets of random joint angle parameter combinations. The collection of these random solutions constitutes the working space of the robotic arm.

2.4.3. Optimization Method

The structural parameters of the picking finger significantly influence the picking operation, as revealed by the analysis of the picking mechanism. To streamline manufacturing costs and development time, stress simulations of Camellia oleifera fruit and the picking finger during the picking process are performed using the LS-Dyna and Explicit Dynamics modules in ANSYS 2022R1 software.

The LS-Dyna module of Ansys 2022R1 software was used to simulate the process of picking Camellia oleifera fruits. To reduce the computational load and simulation time, and to simplify the complexity of the picking components, the picking model was established. Since there was only one fruit per unit length that directly collided with the striking column during the picking process, and the stress and strain at the collision point between the fruit and the striking column were the same as those of a single fruit, the picking component composed of 5 groups of picking fingers was simplified to 1 group of picking fingers. The support seat, transmission shaft, and pulley were simplified to a single rectangular support. The length of the picking fingers was shortened, and a Camellia oleifera fruit model with branches and fruit stalks was added between the two picking fingers, as Figure 8a and Figure 9a show.

The material properties were Q235 (modulus of elasticity of 2.1e + 11 MPa, Poisson’s ratio of 0.278, and density of 7900 kg/m³) and high-toughness resin (modulus of elasticity of 2650 MPa, Poisson’s ratio of 0.43, and density of 1120 kg/m³). With a density of 1120 kg/m³, branches, fruit stalks, and Camellia oleifera fruits as flexible materials, the following material coefficients were experimentally determined: branches (modulus of elasticity of 211.3 MPa, Poisson’s ratio of 0.32, density of 0.69 g/cm³), fruit stalks (modulus of elasticity of 19.59 MPa, Poisson’s ratio of 0.34, density of 1.52 g/cm³) and Camellia oleifera fruits (modulus of elasticity of 165.78 MPa, Poisson’s ratio of 0.19, and density of 0.98 g/cm³). These coefficients were meshed in a size-driven manner for each component, as Figure 8b and Figure 9b show.

The fruit-picking process of the comb-spiral impact harvesting device consists of four stages, i.e., entry, contact, combing, and detachment, making the picking process relatively complex. By adjusting the position and orientation of Camellia oleifera fruits on branches, we simulated the combing and detachment processes with simulation software. Combining actual picking situations, we set the initial conditions and motion states of the picking model.

A fixed constraint is added to the bottom of the branch to define the type of contact between the picking finger and the Camellia oleifera fruit as frictional contact; at the same time, a speed of 0.15 m/s along the positive direction of the Y-axis of the support is set to simulate the brushing motion, a “sliding plane” is added to the end face of the support to control the operation of the picking finger, and a “sliding plane” is added to the end face of the support.

At the same time, we add a “Sliding plane” on the end face of the support to control the running boundary of the picking finger, add a rotating vice at the connection between the picking finger and the finger holder, set the rotating vice with a rotational motion of 10π rad/s from inwards to outwards in a repulsive direction, and ensure that the picking finger only carries out the translatory motion along the Y-axis, and the rotational motion along the axes using remote displacement.

At the end of the simulation, the equivalent stresses of the Camellia oleifera fruit model were analyzed using the postprocessing module of ANSYS 2022R1 software as an index to evaluate the results of the optimization experiment.

Fixed constraints are added to the bottom end of the branches, defining the contact type between the picking fingers and tea fruits as frictional contact. Additionally, a translational speed of 0.15 m/s along the positive Y-axis direction is applied to the support to simulate the combing motion.

A “sliding plane” is added to the end face of the support to control the rotational boundary of the picking fingers. A rotational pair is added at the connection between the picking fingers and the finger seat, with a rotational speed of 10π rad/s for outwards rotation. Remote displacement ensures that the picking fingers only undergo translational motion along the positive Y-axis direction and rotational motion along the axis.

After simulation, the ANSYS 2022R1 software postprocessing module is used to analyze the equivalent stress distribution of the Camellia oleifera fruit model, which serves as an indicator for evaluating the optimized experimental results.

The stress distribution of the picking fingers is simulated and analyzed using the explicit dynamics module of ANSYS 2022R1 software. To increase the computational speed, the three groups of spiral-arranged striking pillars are simplified into one group, with other conditions like those of the stress analysis model of Camellia oleifera fruits, as Figure 9a shows. To improve computational accuracy, the mesh of striking pillars in contact with Camellia oleifera fruits is refined using size-driven methods, while grids are allocated for other components.

To ensure the consistency of the simulation results, the same contact, load, and constraint conditions as those of the stress analysis model of Camellia oleifera fruits are applied to the picking finger stress analysis model. This analysis evaluates the equivalent stress distribution of the picking finger model and serves as a benchmark for evaluating optimized experimental results.

2.4.4. Single-Factor Experiment and Orthogonal Experimental Design of Structural Parameters of the Picking Finger

By analyzing the principles of finger picking, the main factors affecting the effectiveness and reliability of finger picking operations are identified. These parameters include the diameter of the striking pillar, the length of the striking pillar, the pitch of the helix and the spacing between adjacent rotor shafts. Therefore, a single-factor simulation experiment was designed with the objective of optimizing the above parameters, and the experiment factors and levels are

Table 5. Factor levels of the single-factor experiment.

Level	Factors
	Diameter of Striking Pillar (mm)	Length of Striking Pillar (mm)	Pitch of Spiral Thread (mm)	Neighboring Spindle Spacing (mm)
1	6	11	90	45
2	7	12	95	46
3	8	13	100	47
4	9	14	105	48
5	10	15	110	49

shows. Due to the limitations of the simulation model, which cannot simulate the quantity, volume, and distribution of the picking objects on the Camellia oleifera trees, the simulation experiments cannot use the overall picking rate of the machine as an evaluation index for optimization. Therefore, only the fruit shedding rate and flower bud shedding rate of Camellia oleifera are used as evaluation indicators.

Based on the thickness of the Camellia oleifera fruit layer, the length of the pivot axis is 330 mm, and the diameter of the pivot axis is 14 mm. Using the bud and Camellia oleifera fruit diameters as a reference, the optimal gap between two adjacent striking pillars on a single spiral thread was determined to be 16 mm. According to the pivot axis parameters, the spiral thread length is designed to be 300 mm, with 3 sets of spiral striking pillar groups, and the initial gap of the spiral thread is 10 mm.

2.5. Field Validation Experiment Design

In October 2023, a harvesting experiment was conducted to evaluate the performance of the new harvester on Camellia oleifera fruits of the “Qinba No. 1” variety at a Camellia oleifera plantation in Ankang City, Shaanxi Province, China (31°42′–33°49′ N, 108°1′–110°0′ E). Within the experimental site, the four Camellia oleifera trees with a higher quantity of fruits and buds were selected for harvesting trials using a random sampling method. The trees were divided into four harvesting zones along the circumference, each spanning an angle of 90°. The number of flower buds and Camellia oleifera fruits in each experimental zone was manually recorded in preparation for conducting field performance experiments of the harvesting machine.

The tracked chassis, driven by a wireless controller, moved around the trees. The DC brushless motor was controlled to adjust the speed of the picking fingers and combing mechanism (DT-2236B tachometer, accuracy ±0.05–1 d). The picking device tilt angle was changed by flipping the hydraulic cylinder (Protractor, accuracy 0.1°). The coordination of the lifting and rotating mechanisms adjusts the comb-spiral impact harvesting device to the lowest point of the canopy in the harvesting area, performing a bottom-up brushing operation on the Camellia oleifera tree. During this process, the fruits on the branches are subjected to spiral impact from the picking fingers, causing the fruits to detach, as Figure 10 shows.

Based on the working principle of the entire machine in Section 2.2 and the picking principle of the picking fingers in Section 2.3, the key operational parameters affecting the harvesting efficiency, fruit shedding rate, and bud shedding rate of Camellia oleifera are identified as the brushing speed, picking finger speed, and picking device tilt angle. To further clarify the impact of each factor on the evaluation criteria, a single-factor experiment was conducted to determine the optimal value range for each factor.

Table 6. Factor levels of the single-factor experiment.

Level	Factors
	Brushing Speed (r/min)	Picking Finger Speed (r/min)	Picking Device Tilt Angle (°)
1	5	250	−10
2	10	300	−5
3	15	350	0
4	20	400	5
5	25	450	10

shows the experimental factors and levels. During the single-factor experiments on the harvester’s working parameters, other parameters were set at three levels and repeated five times per group. The harvesting time, fruit weight, total fruit count, remaining fruit count, total flower bud count, and dropped flower bud count were recorded. The evaluation criteria were calculated using Equation (16). The average of the five repetitions for each group was taken as the experimental result.

$$\left\{\begin{array}{l}{T}_1=3.6\ast m_{cz}/t_t\hfill \\ T_2=(N_{cz}-N_{cnd})/N_{cz}\hfill \\ T_3=N_{fd}/N_{fz}\hfill \end{array}\right.$$

(16)

where T₁ represents the Camellia oleifera fruit picking rate (kg/h), T₂ represents the Camellia oleifera fruit shedding rate (%), T₃ represents the bud damage rate (%), m_cz represents the total mass of Camellia oleifera fruits (g), t_t represents the time spent on a single field experiment (s), N_cz represents the total number of Camellia oleifera in the experimental area, N_cnd represents the number of unshed Camellia oleifera fruits in the experimental area, N_fd represents the number of buds dropped, and N_fz represents the total number of buds in the experimental area.

To further determine the optimal working parameter combination for the harvester, a three-factor, three-level response surface experiment was designed based on the results of the single-factor experiments. The experimental factors were the brushing speed (Y₁), picking finger speed (Y₂), and picking device tilt angle (Y₃), with harvesting rate (T₁), fruit shedding rate (T₂), and bud shedding rate (T₃) as the evaluation criteria. The experiment consisted of 12 factorial trials and 5 central trials to assess model errors.

Based on the field response surface experiment results, the interaction effects of each factor on the evaluation criteria were analyzed. The regression equations between Y₁, Y₂, Y₃ and T₁, T₂, T₃ were constructed using Design-Expert 13 software. Solving these equations enables the acquisition of the optimal parameter 2 combination for the harvester. After determining the optimal parameter combination, field validation experiments were conducted according to the above experimental design.

3. Results

3.1. Workspace Analysis of the Lifting Mechanism

MATLAB R2021a is used to plot the workspace of the lifting mechanism robot arm. Figure 11 shows the XY, XZ, and YZ planes. The x-axis ranges from 336 mm to 797 mm. The y-axis ranges from −436 mm to 690 mm. The z-axis ranges from −624 mm to 554 mm. The height of the picking base from the bottom surface is 215 mm. The robotic picking arm is able to meet the needs of large-scale picking of Camellia oleifera fruits without moving the travelling system.

As Figure 11 shows, the 3D workspace of the picking arm is dense and uniform, and the overall workspace is relatively regular. There is no hollow inside, and the continuity is good, which indicates that the movement performance of the lifting mechanism is good, covering the target working space required for Camellia oleifera fruit picking and meeting the actual picking requirements.

3.2. Determination of the Structural Parameters of the Picking Finger

3.2.1. Simulation Results

Simulation experiments were conducted using a single-factor experiment level table, while the remaining parameters were maintained at three level values. Figure 12 presents the simulation results for the 8-13-100-47 group of picking finger structure parameters.

Figure 12a,b shows the results of the equivalent stress and deformation of the Camellia oleifera fruit during the picking process. The maximum equivalent force during picking was 3.1786 MPa, which occurred near the fixed position of the fruit branch. Using the probe measurement of ANSYS 2022R1 software, it can be seen that the maximum value of the equivalent force at the position of fruit stalk breakage is 2.5009 MPa, and the maximum value of the total deformation at the position of breakage is 2.8387 mm. At this time, the fruit stalk is separated from the branch, which indicates that the comb-spiral impact harvesting device can achieve the picking of Camellia oleifera fruits.

Based on Figure 12c,d, the equivalent stress and equivalent strain results of the harvesting finger during the picking process are evident. When the harvesting finger collided with the Camellia oleifera fruit, the maximum equivalent stress was 36.1520 MPa. The maximum equivalent strain near the connection point between the fruit stalk and the branch is 0.1685 mm.

This finding indicates that the collision between the harvesting finger and the Camellia oleifera fruit easily generates an equivalent stress of 5.4951 MPa at the pivot support attachment. A striking pillar connected to the pivot point easily generates an equivalent stress of 4.6096 MPa. The stress at the junction of the fruit stalk and the fruit branch was 2.1921 MPa.

A comparison reveals that, during the picking process, the equivalent stresses generated by the harvesting finger are all lower than the bending strength of the high-toughness resin material (69 MPa) and its tensile strength (42 MPa), indicating that the structural form and material of the harvesting finger can meet the picking requirements.

Although the mesh quality of the impacting pillar exceeds that of the Camellia oleifera fruits during the simulation of the picking process using the explicit dynamic module, the equivalent stress derived from the detachment of the Camellia oleifera fruits using the LS-Dyna module is considered the experimental outcome.

3.2.2. Results of the Single-Factor Experiment and Orthogonal Experiment of Structural Parameters of the Picking Finger

Figure 13 shows the results of the single-factor simulation experiment for the structural parameters of the harvesting finger. Based on the optimization objectives of minimizing the equivalent stress on the harvesting finger and maximizing the equivalent stress on the Camellia oleifera fruit, the influencing factors and parameter ranges were selected.

As Figure 13a shows, as the diameter of the striking pillar increases, the contact area between the Camellia oleifera fruit and the striking pillar also increases. The equivalent stress on the harvesting finger gradually decreases, and the rate of decrease accelerates. Moreover, the equivalent stress on the Camellia oleifera fruit gradually increased, with the rate of increase first increasing and then decreasing. Therefore, the optimal range for the diameter of the harvesting finger is chosen to be 8–10 mm.

Based on the findings presented in Figure 13b, it is evident that the equivalent stresses experienced by both the picking device and the Camellia oleifera fruit exhibit a continuous increase in tandem with the diameter of the striking pillar.

Notably, when the length of the striking pillar surpasses 13 mm, the stress on the picking device rapidly increases, while the stress on the Camellia oleifera fruit increases at a more subdued pace. This phenomenon can be attributed to the cantilever beam-like structure of the striking pillar, wherein an increase in its length augments the impact area and torque, consequently increasing the likelihood of fracture at the junction between the striking pillar and the pivot axis. To avert potential failures, such as fracture and wear of the picking device during the harvesting process, it is advisable to select a striking pillar length within the range of 11–13 mm.

Upon analyzing the impact of the helical pitch on the assessment parameters, as Figure 13c shows, it becomes apparent that, with an increase in the helical pitch, the equivalent stress on the picking finger initially decreases before ascending, whereas the equivalent stress on the Camellia oleifera fruit follows an opposite trend, initially increasing before declining.

Nevertheless, the overall trend exhibits a relatively gradual pattern, signifying that the influence of the helical pitch on the evaluation parameters is not substantial. This can be attributed to the fact that alterations in the helical pitch predominantly affect the inclination angle and clearance of the hitting pillar, resulting in minimal impact on the equivalent stress. Consequently, the optimal helical pitch is determined to be 100 mm.

Figure 13d shows that, with an increase in the spacing between adjacent pivots, the equivalent stress on the harvesting finger first decreases and then increases, with the rate of decrease being greater than the rate of increase. Moreover, the equivalent stress on the Camellia oleifera fruit continuously decreased, and the rate of decrease gradually accelerated. This is primarily due to the gradual reduction in the contact area as the spacing between the pivots increases. Therefore, based on the trend of the equivalent stress variation, the preferred spacing between adjacent pivots is chosen to be 46–47 mm.

Based on the results of the single-factor simulation experiments on structural parameters, a three-factor, three-level orthogonal experiment was designed, with the striking pillar diameter (A), striking pillar length (B), and spacing between adjacent pivots (C) as the experimental factors, and the equivalent stress on the Camellia oleifera fruit (E₁) and the equivalent stress on the harvesting finger (E₂) as the evaluation indicators.

This approach comprehensively considers the effects of various factors under different combinations to obtain the optimal combination of structural parameters for the harvesting finger. The coding of the experimental factors is presented in

Table 7. Orthogonal experimental factor levels for optimizing the picking finger structure parameters.

Code	Factors
	A (mm)	B (mm)	C (mm)
−1	8	11	46
0	9	12	47
1	10	13	48

.

According to the orthogonal experiment factor level table, IBM SPSS Statistics 26 software was used to generate the L9 (3³) orthogonal table and adjust the structural parameters to analyze the stress situation of Camellia oleifera fruits and picking fingers, and

Table 8. Orthogonal experimental results for optimizing the picking finger structure parameters.

Number	Factors			Evaluation Indicators
	A (mm)	B (mm)	C (mm)	E1 (MPa)	E2 (MPa)
1	8	13	47	2.50	36.15
2	10	11	48	2.46	35.64
3	9	13	48	2.59	34.52
4	9	11	47	2.47	35.86
5	9	12	46	2.65	33.25
6	10	12	47	2.81	31.82
7	8	12	48	2.37	37.46
8	10	13	46	2.74	32.57
9	8	11	46	2.19	38.97

shows the results of the experiment.

Using the experimental data extracted from Table 8, a comprehensive analysis of variance was conducted through the use of IBM SPSS Statistics 26 software. The resulting results are meticulously tabulated in

Table 9. Variance analysis for optimizing the structural parameters of picking fingers.

Source	Degrees of Freedom	E1			E2
		Sum of Square	F Value	p Value	Sum of Square	F Value	p Value
Model	6	0.048	623.429	0.002 **	7.216	114.767	0.009 **
Intercept	1	57.659	7413226.28	0.0001 **	11111.97	176723.33	0.0001 **
A	2	0.079	1010.714	0.001 **	13.920	221.386	0.004 **
B	2	0.056	720.143	0.001 **	6.434	102.333	0.010 *
C	2	0.011	139.429	0.007 **	1.294	20.582	0.046 *
Inaccuracy	2	7.778 × 10−5			0.063
Total	9
R2		99.8%			99.7%
Amended total	8

Note: ** indicates an extremely significant difference (p < 0.01); * indicates a significant difference (0.01 < p < 0.05).

, while the visual interpretation of the orthogonal experiment has been meticulously computed and is visually represented in

Table 10. Visual analysis of optimizing the structural parameters of picking fingers.

Number	E1 Average k1	E1 Average k2	E1 Average k3	E1 Range R	E2 Average k1	E2 Average k2	E2 Average k3	E2 Range R
A (mm)	2.35	2.57	1.85	0.72	37.53	34.54	33.34	4.18
B (mm)	2.37	2.61	1.78	0.83	36.82	34.18	34.41	2.65
C (mm)	2.53	1.76	1.65	0.87	34.93	34.61	35.87	1.26

for enhanced clarity and interpretation.

The analysis of variance presented in Table 9 reveals that the R² value exceeded 95%, signifying a high model fit for the modified models of Camellia oleifera fruit and picking finger equivalent force, both of which were deemed highly significant. Each structural parameter had a distinct impact on the equivalent force of the Camellia oleifera fruit and picking finger, following the order of striking pillar diameter, striking pillar length, and spacing between neighboring axes.

Notably, the striking pillar diameter exhibits a positive correlation with the collision contact area, exerting a direct influence on the magnitude of the equivalent force. Furthermore, the impact of striking pillar length variation on the contact area surpasses the effect of neighboring rotary axis spacing. Consequently, the striking pillar length exerts a more pronounced impact on the equivalent force than does the neighboring rotary axis spacing.

According to Table 10, the range of the equivalent stress of the picking finger R is $R_A>R_B>R_C$. Based on the mean values k of each factor, it is known that when the equivalent stress of the picking finger is minimized, the optimal parameter combination for the picking finger is A₃B₂C₂.

Table 9 shows that the range of the equivalent stress of Camellia oleifera fruit R is $R_C>R_B>R_A$. According to the mean values k of each factor, when the equivalent stress of Camellia oleifera fruit is maximized, the optimal parameter combination for the picking finger is A₂B₂C₁.

To achieve the design goal of maximizing the equivalent stress of Camellia oleifera fruit and minimizing the equivalent stress of the picking finger, considering the ranges R of the two evaluation indicators, the optimal parameter combination for the picking finger is determined to be A₃B₂C₁, with a picking finger diameter of 10 mm, a striking rod length of 12 mm, and an adjacent shaft spacing of 46 mm.

3.3. Results of Optimization of Operating Parameters Experiment

3.3.1. Results of Single-Factor Experiment for Optimization of Operating Parameters

The single-factor optimization experiment was conducted according to the experimental design and requirements outlined in Section 2.5, and Figure 14 shows the results. Based on the optimization objectives of maximizing the fruit shedding rate and minimizing the bud shedding rate, key factors and their optimal parameter ranges were selected.

According to Figure 14a, as the brushing speed increases, the number of brushing actions by the harvesting device increases, and both the Camellia oleifera fruit shedding rate and bud shedding rate continue to rise. The fruit shedding rate reaches a maximum of 93.03% at a brushing speed of 20 r/min. When the brushing speed increases from 20 r/min to 25 r/min, the bud shedding rate accelerates. Considering this, the brushing speed optimization experiment design range is selected as 15–25 r/min.

Figure 14b shows that, as the picking finger speed increases, the impact force exerted by the picking fingers on both the fruit shedding rate and bud shedding rate increases, resulting in higher drop rates for both. When the picking finger speed exceeds 350 r/min, the fruit shedding rate increases slowly while the bud shedding rate increases rapidly. This suggests that further increasing the picking finger speed does not significantly improve the fruit drop rate but rapidly increases the bud shedding rate, negatively impacting overall harvesting performance. Therefore, the picking finger speed optimization experiment design range is set to 300–400 r/min.

Figure 14c shows that, as the picking device tilt angle increases, both the fruit and bud shedding rate increase. When the tilt angle is less than 0°, the drop rates increase rapidly with the tilt angle. As the tilt angle continues to increase, the rate of increase in both the fruit shedding rate and the bud shedding rate slows. A tilt angle that is either too small or too large reduces the contact area between the picking fingers and the main branches, which, to some extent, reduces or slows the increase in fruit and flower bud drop rates. Therefore, the tilt angle optimization experiment design range is selected to −5° to 5°.

Analysis of Figure 14d reveals that different factors have varying impacts on the evaluation indicators. When other factors are fixed at the third level, the brushing speed has the largest effect on fruit detachment, while the picking finger speed has the least effect. The bud shedding rate is most affected by the picking finger speed and least affected by the tilt angle. The results from the brushing speed experiment yield fruit shedding rate and bud shedding rates slightly lower than the median, while the picking finger speed experiment shows a fruit shedding rate slightly higher than the mean and a bud shedding rate much higher than the mean. The tilt angle experiment results show a fruit shedding rate greater than the median and a bud shedding rate slightly lower than the median.

Given that the design goal is efficient fruit harvesting with minimal flower bud damage, the optimal brushing speed should be above the third level, while the optimal picking finger speed and tilt angle should be below the third level.

3.3.2. Results of Response Surface Experiment for Optimization of Operating Parameters

Table 11. Results of response surface experiment for optimization of operating parameters.

Serial Number	Factors			Evaluation Indicators
	Y1 (r min−1)	Y2 (r min−1)	Y3 (°)	T1 (kg h−1)	T2 (%)	T3 (%)
1	19	330	−2	108.07	89.68	8.69
2	22	340	−2	120.64	90.73	8.84
3	22	340	1	122.85	94.25	9.23
4	16	330	−0.5	97.08	89.21	8.78
5	19	340	−0.5	109.06	93.24	9.08
6	19	340	−0.5	113.94	93.96	9.14
7	19	340	−0.5	110.49	92.47	9.11
8	19	330	1	108.99	91.31	8.97
9	19	340	−0.5	110.25	92.62	9.12
10	19	350	1	118.63	94.56	9.47
11	22	330	−0.5	110.52	90.26	9.09
12	16	350	−0.5	99.34	91.74	9.37
13	19	340	−0.5	112.10	93.83	9.06
14	16	340	−2	100.33	89.98	8.73
15	16	340	1	104.52	91.89	9.02
16	19	350	−2	110.97	92.27	9.25
17	22	350	−0.5	130.02	94.86	9.65

shows the results of response surface experiment for optimization of operating parameters.

To obtain the optimal working parameter combination for the field experiment, the regression model based on a second-order polynomial equation, as Equation (17) shows, was constructed using Design-Expert 13 software, and the variance of the regression model was analyzed, as presented in

Table 12. Analysis of variance of the field experiment results.

Source	Degrees of Freedom	T1			T2			T3
		Sum of Square	F Value	p Value	Sum of Square	F Value	p Value	Sum of Square	F Value	p Value
Model	9	126.32	30.21	<0.0001 **	5.41	15.29	0.0008 **	0.115	44.11	<0.0001 **
Y1	1	856.15	204.7	<0.0001 **	6.62	18.73	0.0034 **	0.1035	39.69	0.0004 **
Y2	1	147.06	35.17	0.0006 **	21.03	59.45	0.0001 **	0.6105	234.1	<0.0001 **
Y3	1	28.05	6.71	0.0359 *	10.93	30.89	0.0009 **	0.1741	66.74	<0.0001 **
Y1Y2	1	74.30	17.77	0.0040 **	1.07	3.03	0.1254	0.0002	0.086	0.7775
Y1Y3	1	0.98	0.234	0.6431	0.648	1.83	0.2180	0.0025	0.959	0.3601
Y2Y3	1	11.36	2.72	0.1434	0.109	0.308	0.5963	0.0009	0.345	0.5753
$Y_1^2$	1	2.39	0.572	0.4740	4.00	11.30	0.0120 *	0.0004	0.1535	0.7069
$Y_2^2$	1	5.80	1.39	0.2773	2.26	6.38	0.0395 *	0.0714	27.39	0.0012 **
$Y_3^2$	1	11.76	2.81	0.1375	1.21	3.43	0.1063	0.0793	30.41	0.0009 **
Residual	7	4.18			0.354			0.0026
Lack of fit	3	4.99	1.40	0.3661	0.211	0.458	0.726	0.0047	4.63	0.0864
Pure error	4	3.57			0.461			0.0010
R2		97.49%			95.16%			98.27%
Coefficient of variation		1.84%			6.45%			0.561%

Note: ** indicates an extremely significant difference (p < 0.01); * indicates a significant difference (0.01 < p < 0.05).

.

$$\left\{\begin{array}{cc}\hfill T_1& =-576.66828-42.26978Y_1+5.73845Y_2-34.11233Y_3+0.143667Y_1{Y}_2\hfill \\ & -0.11Y_1{Y}_3+0.112333Y_2{Y}_3-0.083778Y_1^2-0.01174Y_2^2+0.742667Y_3^2\hfill \\ \hfill T_2& =-743.89692-1.40239Y_1+4.81748Y_2-4.89894Y_3+0.01725Y_1{Y}_2\hfill \\ & +0.089444Y_1{Y}_3+0.011Y_2{Y}_3-0.108278Y_1^2-0.00732Y_2^2-0.238667Y_3^2\hfill \\ \hfill T_3& =147.70314+0.166861Y_1-0.853825Y_2+0.271778Y_3-0.00025Y_1{Y}_2\hfill \\ & +0.005556Y_1{Y}_3-0.001Y_2{Y}_3-0.001083Y_1^2+0.001302Y_2^2-0.061Y_3^2\hfill \end{array}\right.$$

(17)

The analysis of variance in Table 12 indicates that the p value is less than 0.01, signifying the significant nature of the regression models determined through field experiments. Moreover, the coefficient of determination (R-squared) exceeded 95%, indicating a high degree of model fit. The coefficient of variation (CV) was less than 10%, demonstrating good data stability. Additionally, the lack of fit is greater than 0.05, implying the absence of any omitted factors that could significantly impact the evaluation criteria. In conclusion, this model can be utilized to predict the experimental results of various evaluation criteria under different operating parameters of comb-spiral impact harvesting devices.

From Table 12, within the range of working parameters used in the field experiment, it is evident that the factors influencing the Camellia oleifera fruit picking rate are ranked as follows: comb brush speed > picking finger speed > picking device inclination angle. Similarly, the factors affecting the shedding rate of Camellia oleifera fruit and buds were ranked as follows: picking finger rotation speed > picking device inclination angle > comb brush rotation speed.

Based on the varying effects of each factor on the evaluation indices, the response surfaces of the regression model equations for the interaction effects of different factors (with other factors set to their median values) were plotted using Origin 2019 software, as Figure 15 shows.

Based on Figure 15a–c, the interactive effects of various factors on the Camellia oleifera fruit picking rate (T₁) are analyzed. Figure 15a shows that, when either the brushing speed (Y₁) or the picking finger speed (Y₂) is low, the fruit picking rate (T₁) increases slowly with the increase of the other factor. However, as both the picking finger speed and brushing speed increase, T₁ rises significantly, indicating that the interaction between Y₁ and Y₂ has a highly significant effect on T₁.

Figure 15b shows that the picking device tilt angle (Y₃) has a smaller effect on the fruit picking rate (T₁) compared to the brushing speed (Y₁). When Y₃ is fixed, T₁ increases continuously with the increase in Y₁, and the rate of increase becomes more significant as Y₁ increases. This suggests that T₁ is more influenced by Y₁ than by Y₃, and the interaction between Y₁ and Y₃ has no significant impact on T₁.

Figure 15c shows that the fruit picking rate (T₁) increases with the picking finger speed (Y₂), and the rate of increase becomes steeper as the picking device tilt angle (Y₃) rises. The effect of Y₃ on the picking rate (T₁) is more complex: when Y₂ is below 340 r/min, T₁ rapidly decreases as Y₃ increases, but when Y₂ exceeds 340 r/min, T₁ increases gradually with Y₃. This indicates that, when the picking finger speed is high, the increase in T₁ due to Y₂ is much larger than the decrease caused by an increase in Y₃. The interaction between Y₂ and Y₃ shows that Y₂ has a far greater influence on T₁ than Y₃.

For the fruit drop rate (T₂), Figure 15d–f show, the trend of interaction between the factors is similar: the drop rate (T₂) increases as the factors increase. T₂ rises most significantly with Y₂, followed by Y₁, with the smallest effect from Y₃. The optimal value of the fruit drop rate is found at the back of the surface. However, the interaction between Y₁ and Y₂ shows that, at low values of Y₂, T₂ increases initially with Y₁, but after reaching a peak, it decreases, with the increase being greater than the decrease. This differs slightly from the interactions of other factors, but the overall trend is similar.

Figure 15g–i explores the interaction effects of the factors on the bud shedding rate (T₃). The minimum value of T₃ occurs at the lowest values of each factor. Figure 15g shows that the bud shedding rate (T₃) increases as the brushing speed (Y₁) and the picking finger speed (Y₂) rise, with the increase due to Y₂ being much larger than that caused by Y₁. Figure 15h shows that, when the picking device tilt angle (Y₃) is fixed, T₃ increases as the brushing speed (Y₁) rises, and the rate of increase becomes more pronounced with higher values of Y₃. When Y₁ is low, T₃ increases rapidly with Y₃ before gradually decreasing, while at higher values of Y₁, T₃ increases rapidly with Y₃ and then increases more slowly. Finally, as Figure 15f shows, the interaction between the picking finger speed (Y₂) and the tilt angle (Y₃) results in T₃ initially increasing slowly with Y₂, then rising rapidly. With increasing Y₃, T₃ initially increases rapidly and then grows more slowly.

To determine the optimal working parameter combination for the field operation of the comb-type harvesting experiment, with the objectives of maximizing Camellia oleifera fruit picking rate and detachment rate while minimizing bud shedding rate, the optimal values of the regression model equation were obtained using Design-Expert 13 software.

The computational results indicate that, when the brushing speed is 21.45 r/min, the picking finger speed is 341.27 r/min, and the picking device tilt angle is 1.0°, the harvesting machine exhibits optimal performance. In this setting, the Camellia oleifera fruit harvesting rate reached 123.41 kg/h, with a fruit shedding rate of 94.55% and a bud shedding rate of 9.25%, meeting the requirements for high harvesting cleanliness and low floral bud damage.

4. Discussion

To verify the accuracy of the optimal working parameters obtained from the field orthogonal experimental regression model, the optimal parameters were set as follows: brushing speed of 21.45 r/min, picking finger speed of 341.27 r/min, and harvesting device tilt angle of 1.0°.

Five experimental regions with similar tree characteristics, including tree height, crown diameter, total number of Camellia oleifera fruits, and total number of flower buds, were selected for the validation experiment of the optimal working parameters. The mean values of the evaluation indices from each experimental region were taken as the operational performance of the comb-type harvester under the optimal parameter combination. Based on the obtained optimal working parameters, five field validation experiments were conducted, and

Table 13. Field validation experiment results of optimal working parameter combination.

Area	Total Number of Fruits	Total Number of Buds	Number of Fruit Shedding	Number of Buds Shedding	Mass of Shed Fruit/g	Operating Time (s)	Picking Rate(kg/h)	Fruit Shedding Rate (%)	Bud Shedding Rate (%)
1	85	356	79	32	2310.09	70	118.8	92.94	8.99
2	83	364	76	35	2236.26	65	123.85	91.57	9.62
3	79	351	74	33	2052.70	63	117.29	93.67	9.40
4	68	347	63	32	2003.18	60	120.19	92.65	9.22
5	75	338	68	29	2042.89	62	118.62	90.67	8.58
Average value	78	351.20	72	32.20	2129.02	64	119.75	92.30	9.16

shows the experiment results.

Field trials demonstrated that, with the optimal combination of operating parameters, the harvesting rate of Camellia oleifera fruits was 119.75 kg/h, the fruit shedding rate was 92.30%, and the bud shedding rate was 9.16%. The Camellia oleifera fruit shedding rate of the crawler high-clearance Camellia oleifera fruit vibration harvester developed by Du Xiaoqiang et al. [10] was 87.56%, and the flower bud shedding rate was 31.80%. The Camellia oleifera fruit shedding rate of the portable hydraulic Camellia oleifera fruit harvester developed by Wang Yulong et al. [21] based on the squeezing principle was 81.10%, and the flower bud shedding rate was 25.11%. Compared with the vibration harvester, the flower bud shedding rate in this study was reduced by 22.64%; compared with the squeezing harvester, the Camellia oleifera fruit shedding rate was increased by 11.20%. Moreover, the Camellia oleifera fruit shedding rates of most Camellia oleifera fruit harvesters are between 80% and 95%, and the flower bud shedding rates are between 35% and 10%. Therefore, this study meets the picking requirements of high picking efficiency, high fruit shedding rate, and low flower bud shedding rate.

The field experiment results were generally lower than those predicted by the regression model equations. This discrepancy is primarily attributed to variations in the quantity and distribution of Camellia oleifera fruits within the experiment area, leading to fluctuations in experimental outcomes. The average shedding rates of fruits and flower buds in the field trials were slightly lower than the predicted values due to the differing growth positions of the fruits and buds, with some not being within the harvesting area.

Yan et al. developed a handheld impact-combing Camellia oleifera fruit harvesting device, utilizing multiple impact fingers to comb and impact the fruits, causing a sudden change in their kinetic energy and subsequent detachment [34]. However, this device is a small-scale, manually assisted harvesting tool, making it unsuitable for mechanized harvesting. This study integrates the characteristics of combing and impacting operations, providing a novel approach for the development of forest fruit harvesting devices. Future research could combine additional operational forms to create more advanced devices.

Although the device is specifically designed for the unique variety “Qinba No. 1” from Shaanxi Province, China, the model for Camellia oleifera fruits and buds is constructed based on actual measurement results. If this novel harvesting device is widely adopted, it can facilitate harvesting trials for other varieties of Camellia oleifera by simply adjusting the gap between the picking fingers. According to the fruit diameter difference of different varieties of Camellia oleifera fruit, the size of the adjacent shaft interval on the picking finger is adjusted to ensure the balance between the bud passing ability and the fruit picking rate. Furthermore, this harvesting device may also be applicable to the harvesting of nut-like fruits, such as walnuts, Badam, and so on.

5. Conclusions

This study addresses the issues of low harvesting efficiency and high flower bud shedding rates encountered during the mechanical harvesting of Camellia oleifera fruits. It innovatively proposes a tooth comb-spiral impact-type Camellia oleifera fruit harvesting device that integrates multiple harvesting principles, including impact, vibration, and brushing. The device’s performance was validated through simulation optimization and field trials. By integrating a four-degree-of-freedom lifting mechanism, spiral-arranged impact columns as picking fingers, and a tracked chassis, the device achieves efficient Camellia oleifera fruit harvesting while ensuring a high harvesting efficiency rate.

The evenly spaced picking fingers allow flower buds to flow out through the gaps, thereby reducing the rate of flower bud shedding. Using the Ls-Dyna and Explicit Dynamics modules in Ansys 2022R1 software, the equivalent distributed stress conditions of Camellia oleifera fruits and picking fingers were simulated. Through single-factor experiments and orthogonal experiments, the optimal structural parameters of the picking fingers were determined as follows: picking finger diameter of 10 mm, striking rod length of 12 mm, and adjacent shaft spacing of 46 mm. The picking fingers were manufactured according to these structural parameters.

Field single-factor experiments and response surface experiments were conducted to identify the factors and ranges affecting the performance of the picking machine. Regression model equations were established for the relationships between brush speed, picking finger speed, picking device angle, Camellia oleifera fruit picking rate, Camellia oleifera fruit shedding rate, and flower bud shedding rate. The optimal operating parameters obtained by solving the equations using Design-Expert 13 software were brushing speed of 21.45 r/min, picking finger speed of 341.27 r/min, and picking device tilt angle of 1.0°

Field validation tests were conducted using the optimized picking fingers and operating combinations, resulting in a Camellia oleifera fruit picking rate of 119.75 kg/h; the Camellia oleifera fruit shedding rate was 92.30%, and the flower bud shedding rate was 9.16%. Compared to vibrating harvesters, the flower bud shedding rate was reduced by 22.64%; compared to squeezing harvesters, the Camellia oleifera fruit harvesting rate was increased by 11.20%.

However, the research subject of this study was the unique variety “Qinba No. 1” from Shaanxi Province, which has diameter differences compared to other camellia fruit varieties. For large-fruit varieties, such as the “Xianglin” series, efficient harvesting can be achieved by adjusting the spacing between the harvesting finger shafts. In the future, it is necessary to further establish a mapping model between different varieties and harvesting finger structural parameters to expand applicability across multiple varieties.