Research and Experiments on Adaptive Root Cutting Using a Garlic Harvester Based on a Convolutional Neural Network

Abstract

Aimed at the problems of a high leakage rate, a high cutting injury rate, and uneven root cutting in the existing combined garlic harvesting and root-cutting technology, we researched the key technologies used in a garlic harvester for adaptive root cutting based on machine vision. Firstly, research was carried out on the conveyor alignment and assembly of the garlic harvester to realize the adjustment of the garlic plant position and the alignment of the bulb’s upper surface before the roots were cut, to establish the parameter equations and to modify the structure of the conveyor to form the adaptive garlic root-cutting system. Then, a root-cutting test using the double-knife disk-type cutting device was carried out to examine the root-cutting ability of the cutting device. Finally, a bulb detector trained with the IRM-YOLO model was deployed on the Jetson Nano device (NVIDIA, Jetson Nano(4GB), Santa Clara, CA, USA) to conduct a harvester field trial study. The pass rate for the root cutting was 82.8%, and the cutting injury rate was 2.7%, which tested the root cutting performance of the adaptive root cutting system and its field environment adaptability, providing a reference for research into combined garlic harvesting technology.

1. Introduction

Garlic (Allium sativum L.) is a widely grown vegetable seasoning with both edible and medicinal values [1,2,3,4]. Studies have shown that garlic consumption can effectively reduce the risk of diseases such as hypertension and diabetes [5,6]. In addition to its disease-preventive properties, garlic has antibacterial, anti-inflammatory and insecticidal properties [7,8,9,10]. The organosulfur compounds and polyphenols contained in garlic give it a unique flavor [11]. Garlic is one of the most important flavoring vegetables in the world [12], and China is the number one producer of garlic in the world [13]. At present, garlic harvesting in China is dominated by manual harvesting, and the technological level of mechanized harvesting equipment is low, which hinders the development of the Chinese garlic industry.

Root cutting is an essential process in garlic harvesting. Since the roots of freshly emerged garlic are entrapped within moist soil, failure to remove the roots will result in moldy or even rotting bulbs. Garlic that has been rooted will also look better, which will increase its selling price. Usually, garlic heads vary in size, there are differences in the location of the roots, garlic heads have different postures in the soil, and garlic heads can be watery and have thin skins. Due to their biological characteristics, garlic and garlic roots are easy to dehydrate, which reduces their toughness, meaning that the technical difficulty involved in mechanized root-cutting operations is high. Moreover, cutting the bulb can easily lead to pathogen invasion, causing food safety problems [14,15,16], and garlic root cutting is mainly carried out manually with a low production efficiency. To improve production efficiency and control food safety, there is an urgent need for garlic harvesters that can realize high-quality root-cutting functions.

At present, garlic harvesters are mainly digging and spreading types or the combined-harvesting type. The combined-harvesting-type garlic harvester can carry out the functions of vibration digging, clamping and conveying, cutting stems, throwing seedlings, and garlic collection and storage at the same time [17,18,19,20,21]. The HZ1 garlic harvester from the YANMAR company in Japan includes a root-cutting function in the process of combined harvesting. It uses a double-disk cutter to cut the garlic root continuously, but the height of the double-disk cutter cannot be adjusted, which has led to the emergence of the problem that the garlic roots cannot be cut cleanly [17,22]. In the mechanized garlic harvesting process, the height of the root knife in the root-cutting device can be adjusted according to differences in the size of the garlic heads; the height of the root-cutting knife in the root-cutting device can be adjusted to improve the rate of cutting the garlic roots cleanly, which is referred to as an adaptive root-cutting technology. The research on adaptive root cutting in garlic harvesters is still in the exploratory and experimental stages [23,24]. Z. Yu et al. [25] developed a floating garlic root-cutting mechanism with a spiral guard fence and a return plate spring, which improved the rate of clean garlic root cutting by increasing the number of root-cutting channels. J. Hou et al. [26] designed a press-type root cutting device to cut the garlic root under the action of a clamping conveyor mechanism and a disk knife, which realized garlic root disk alignment and garlic root cutting through the rotation of a paddle wheel and the deformation of a press. K. Yang et al. [27,28,29] utilized machine vision and deep learning technology to detect the demarcation line between the garlic bulb and root, move the disk root-cutting knife to the corresponding position according to the detection result, and realize adaptive garlic root cutting based on image recognition technology on a test bench. However, due to the harvester’s high functional integration, complexity, efficiency, and compactness [30,31,32,33,34,35,36], arranging the adaptive garlic root-cutting system on the harvester and performing the harvesting operation pose significant challenges.

Machine vision and deep learning technologies have been heavily applied to study the information perception aspect of improving the intelligence of harvesters. Q. Zhang et al. [37] acquired image information through the camera installed on a wheat harvester and used the YOLO algorithm and an onboard deep learning system to predict the harvester’s feeding volume and realize the closed-loop control of intelligent harvesting operations. Z. Guan et al. [36] used a rice harvester’s camera on the seed sampling device to achieve continuous image acquisition, proposed an impurity recognition algorithm based on Mask R-CNN, and evaluated the impurity detection system through bench and field tests. A. Nasirahmadi et al. [38] positioned a camera above the cleaning turbine of the beet harvester to acquire images in real time when the harvester was cleaning the beets and through a deep learning system developed based on CNN to detect beet damage in the harvester automatically. W. Yang et al. [39] installed a camera, edge computing equipment, and related supporting equipment on a dig-and-pull cassava harvester, utilized the improved YOLO algorithm to detect cassava stalks in the field in real time, and adjusted the position of the harvester’s clamping and pulling mechanism to align the stalks according to the results of the detection, thus reducing harvesting losses.

Aimed at the problem that existing garlic harvesters do not have an adaptive root-cutting function and the root-cutting quality is low, this study developed a garlic harvester with an adaptive root-cutting function. Combining theoretical analysis, bench tests, field tests, and other methods, we study the adaptive root-cutting process of the garlic harvester conveyor assembly on the role of garlic plants, garlic plant attitude adjustment, root force, and other dynamic characteristics; explore the adaptive root-cutting system matching, quality-influencing factors, and mechanism optimization scheme; provide a reference for the garlic harvester’s function and technology improvement.

2. Materials and Methods

2.1. 4DSL-7 Garlic Harvester

Relying on the National Natural Science Foundation of China (NSFC) project, the National Key R&D Program and the Jiangsu Modern Agricultural Machinery Equipment and Technology Demonstration and Promotion Project, Nanjing Agricultural Mechanization Research Institute of the Ministry of Agriculture and Rural Affairs of China (NAMRI) has developed the 4DLB-2 garlic harvester (Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing, China), which can harvest two rows at a time, and the 4DSL-7 garlic harvester (Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing, China), which can harvest seven rows at a time. The 4DSL-7 garlic harvester (shown in Figure 1) mainly consists of the following components: profiling depth-limiting wheel, splitter, vibrating digging shovel, clamping and conveying device, aligning and cutting seedling mechanism, seedling and fruit collecting and transmitting device, scraper lifting and conveying device, fruit box (which can be unloaded backward), chassis walking mechanism, and so on. Among them, garlic plant sorting alignment, fruit and seedling separation, and seedling and fruit collection and delivery are a vital operation link and key technology. Alignment of seedling cutting mechanism and seedling and fruit collection and delivery device of the structure and operation parameter setting directly determine the operational performance of the machine.

The core component of the 4DSL-7 garlic harvester, the structure of the conveyor alignment assembly, is shown in Figure 2, which mainly consists of a hydraulic motor, clamping conveyor chain, harvesting table welding frame, sorting-alignment device, transmission system, etc. Usually, each conveyor alignment assembly completes one row of garlic harvesting, which can be installed in 3 to 7 groups. Usually, each conveyor alignment assembly completes harvesting one row of garlic, and the prototype of a 4DSL-7 garlic harvester can be installed with 3–7 sets of assemblies to harvest 3–7 rows of garlic at one time. During operation, after the garlic plants are dug out of the soil, they are clamped by the clamping conveyor chain and transported backward (the direction of transport is shown by the blue arrow in Figure 2). The garlic plants are aligned along the upper surface of the bulb (as indicated by the yellow arrow in Figure 2). The garlic plants’ position is adjusted by the joint action of the clamping conveyor chain and the toggle finger chain of the sorting-aligning device. Based on bulb alignment, the final separation of the fruit and seedling is realized with the help of a stem-cutting device.

The conveyor alignment assembly realizes bulb alignment along the upper surface by forced expansion using an inclined configuration between the gripping conveyor chain and the toggle finger chain of the sorting-aligning device. By changing the speed ratio of the gripping conveyor chain and the toggle finger chain, the position of the garlic plant can be adjusted when the bulbs are aligned along the upper surface so that the adjustment of the position of the garlic plant and the alignment of the bulbs along the upper surface can be accomplished in the front or middle section of the sorting-aligning device. At the same time, when the bulb is aligned along the upper surface, the clamping conveyor chain applies a lifting force in the direction of the garlic bulb, which will cause the upper surface of the bulb to rest on the lower part of the two adjacent fingers of the toggle finger chain, similar to the state of holding the bulb in the hand when cutting the root manually. The force of the bulb is shown by the red arrows in Figure 2. The realization of the above functions allows for adaptive root cutting in garlic.

When a garlic harvester is in operation, the elongation of the traveling wheels determines the initial clamping height of the clamping conveyor chain on the garlic plant, and the initial clamping height of the garlic plant influences the initial position on the sorting-alignment device at the which alignment of the bulbs along the upper surface is accomplished. The initial clamping height of the garlic plant needed to satisfy the bulb alignment along the upper surface was determined by harvesting tests in the pre-tests of the field trials to obtain a suitable amount of traveler wheel elongation, which will not be repeated in this text. Thus, the garlic plant position adjustment and bulb alignment along the upper surface are only related to the speed ratio of the clamping conveyor chain and the finger-toggle finger chain.

2.2. Speed Relationship Between Clamp Conveyor Chain and Toggle Finger Chain

To study the change rule of garlic plant position in the conveyor alignment assembly, to ensure that the garlic plant is perpendicular to the plane of the toggle finger chain, and to realize the alignment of the upper surface of the bulb during the subsequent root-cutting operation, it is necessary to analyze the speed of the clamping conveyor chain V_JC and the speed of the toggle finger chain V_BZ. Let the ratio of clamping conveyor chain speed V_JC and toggle finger chain speed V_BZ be K_X.

In Figure 3, α_E is the angle between the garlic plant and the clamping conveyor chain (°); β_A is the angle between the garlic plant and AD (°); γ_BC is the angle between the clamping conveyor chain and the toggle finger chain (°); point A is the front rotating center of the toggle finger chain; and point B is the initial position of completing the bulb alignment along the upper surface. The distance between points A and B is l_AB, mm. Points D and C are the projection points on the clamping conveyor chain in the vertical direction of points A and B along the sorting-alignment device. The projection point on the clamping conveyor chain, the distance between points D and C is l_CD, mm; when the garlic plant enters the sorting-aligning device at an angle of α_E with the clamping conveyor chain, the initial clamping point of the garlic plant on the clamping conveyor chain is point E. The distance between points D and E is l_DE, mm; between points A and D is l_AD, mm; and between points BC is l_BC, mm.

The distance l_CD between the projection points of points A and B along the plane normal to the plane in which the toggle finger chain is located on the clamping conveyor chain is:

$$l_{CD}=\sqrt{l_{AB}^2+{\left(l_{BC}-l_{AD}\right)}^2}$$

(1)

The angle γ_BC between the clamping conveyor chain and the toggle finger chain is:

$${\gamma }_{BC}=\arctan {\displaystyle \frac{l_{BC}-l_{AD}}{l_{AB}}}$$

(2)

In Equations (1) and (2), l_AB is the action distance of the sorting-alignment device when the garlic plant is perpendicular to the plane in which the toggle finger chain is located, mm; l_AD is the projection distance along the plane of the toggle finger chain where point A is located in the expected direction on the clamping conveyor chain, mm; l_BC is the projection distance along the plane of the toggle finger chain where point B is located in the expected direction on the clamping conveyor chain, mm.

When the garlic harvester is in operation, the angle of inclination of the conveyor alignment assembly is different, and the angle α_E between the garlic plant and the clamping conveyor chain after shoveling and pulling out the soil will be different. To realize that the cutting surface of the cut root is perpendicular to the garlic plant, it is required that the garlic plant is perpendicular to the plane where the toggle finger chain is located when the root is cut. Effective matching of the speed ratio between the clamping conveyor chain and the toggle finger chain of the sorting-alignment device ensures that the garlic plant is perpendicular to the plane of the toggle finger chain, so it is necessary to study the ratio of the speed of the clamping conveyor chain, V_JC, and the speed of the toggle finger chain, V_BZ, to be K_X. To facilitate this study, it is assumed that the digging depth of the excavation shovel is unchanged, the tilt angle of the conveyor alignment assembly is unchanged, and the corresponding angle α_E between the garlic plant and the clamping conveyor chain is also a definite value. Point A is the initial point of action of the toggle finger chain, and the BC line is the target conveying position. Then, the ratio of the speed of the clamping conveyor chain V_JC and the speed of the toggle finger chain V_BZ is K_X

$$K_X=V_{JC}/V_{BZ}=\left(l_{CD}-l_{DE}\right)/l_{AB}$$

(3)

In the above formula, V_JC is the speed of the clamping conveyor chain, m/s; V_BZ is the speed of the toggle finger chain, m/s; l_CD is the distance between the projection points on the clamping conveyor chain along the plane of the toggle finger chain where points A and B are located in the expected direction, mm; l_DE is the amount of adjustment of the sorting-alignment device on the clamping conveyor chain on the clamping point of the garlic plant, mm; l_AB is the role of the sorting-alignment device when the garlic plant is perpendicular to the plane of the toggle finger chain, mm. distance, mm.

By the sine theorem

$$l_{DE}=l_{AD}\sin {\beta }_A/\sin {\alpha }_E$$

(4)

In the above equation, l_AD is the projection distance of point A along the normal direction of the plane where the finger-pulling chain is located on the clamping conveyor chain, mm; β_A is the angle between the garlic plant and AD (°); α_E is the angle between the garlic plant and the clamping conveyor chain (°).

From the triangular relationship

$${\beta }_A=90°-\left({\alpha }_E+{\gamma }_{BC}\right)$$

(5)

In the above formula, γ_BC is the clamping conveyor chain and toggle finger chain angle (°).

Equations (3)–(5) can be obtained by the association of

$$K_X={\displaystyle \frac{l_{CD}\sin {\alpha }_E-l_{AD}\cos \left({\alpha }_E+{\gamma }_{BC}\right)}{l_{AB}\sin {\alpha }_E}}$$

(6)

From the above equation, when K_X satisfies Equation (6), it can be ensured that the garlic plant is perpendicular to the plane where the toggle finger chain is located when cutting the roots [40,41]. Equation (6) also shows that the distance l_CD between the projection points, the projection distance l_AD, the distance l_AB, the angle α_E between the garlic plant and the clamping conveyor chain, and the angle γ_BC between the clamping conveyor chain and the toggle finger chain are the common influencing factors. The ratio K_X of the speed V_JC of the clamping conveyor chain and the speed V_BZ of the finger-pulling chain is determined; i.e., it affects the adjustment of the position of the garlic plant by the sorting-alignment device.

It is the ability of the conveyor alignment assembly to realize the positional adjustment of the garlic plant and the alignment of the upper surface of the bulb that makes it possible to realize adaptive root cutting for a garlic harvester. However, the conveyor alignment assembly is compact. It has no extra installation space, and its structure needs to be modified to arrange a machine vision-based adaptive root-cutting system for garlic harvesters.

2.3. Cutting Device Design and Research

To meet the need for adaptive root cutting, a cutting device containing a camera, a cutting device, and a stepper motor for position adjustment is designed. As shown in Figure 4, the cutting device mainly includes a cutter with a connecting piece, a lifting arm, a micro switch, a stationary seat, a dual-out shaft stepper motor, a clearing servo, a clearing rocker arm, a camera, a damping tension spring, etc. Among them, four lifting arms of equal length, a fixed seat, and a cutter with a connector form a parallel four-bar mechanism. The line between the rotation centers of the upper lifting arm and the lower lifting arm mounted on the fixed seat is perpendicular to the lower bottom surface of the fixed seat. A damping spring increases the lifting force of the parallel four-bar mechanism, and a dual-out shaft stepper motor (KEMING Motor, 57MM44A100, Wenzhou, China) provides the driving force for the two lower lifting arms. A microswitch (Omron, SS-5GL2, Kyoto, Japan) is used to reset the lifting arm; the camera (KINGCENT, KS1A14, Shenzhen, Guangdong, China) has a high-definition 1.0 megapixel and a 60° lens. To address the problem of dust that can cover the camera lens during harvesting, a dirt-clearing rocker arm with a brush was designed to erase the dirt from the transparent lens guard. A servo powers the clearing arm.

When the lower lifting arm triggers the microswitch, the angle of rotation of the dual-out shaft stepper motor is recorded as 0°, at which time all four lifting arms are parallel to the lower bottom surface of the stationary base; when the cutting device completes the reset, and the optical axis of the camera passes through the plane where the circular knife disk of the cutting device is located, the angle of rotation of the dual-out shaft stepper motor is recorded as α_DJ (°), as shown in Figure 5. When the dual-out shaft stepper motor makes the first root-cutting rotation to adjust the cutting device’s height, the rotation angle is Δα₁ (°). Then, the angle turned by the dual-out shaft stepper motor is:

$${\theta }_1={\alpha }_{DJ}+\mathsf{\Delta }{\alpha }_1$$

(7)

where θ₁ is the angle, in °, that the dual-out shaft stepper motor turns relative to the position of the lower lifting arm, triggering the microswitch.

The distance between the two rotation centers of the upper and lower lifting arms is equal, expressed in l_JS, mm.

When θ₁ > α_DJ, the height of movement of the cutting device relative to the lower bottom surface of the stationary base is:

$${\Delta h}_1^{\prime }=l_{JS}·\left(\tan {\theta }_1-\tan {\alpha }_{DJ}\right)$$

(8)

When α_DJ > θ₁, the cutting device moves a height relative to the lower bottom surface of the stationary base:

$${\Delta h}_1^{\prime }=l_{JS}·\left(\tan {\alpha }_{DJ}-\tan {\theta }_1\right)$$

(9)

In Equations (8) and (9), Δh’₁ is the height of the cutting device relative to the lower bottom surface of the stationary base after the first rotation of the dual-out shaft stepper motor for the cutting root, mm; l_JS is the distance between the two rotating centers of the lifting arm, mm. Moreover, the cutting device raises Δh’₁ when θ₁ > α_DJ, and the cutting device decreases Δh’₁ when α_DJ > θ₁.

For the n′ + 1st rotation (n′∈N+) of a dual-out shaft stepper motor as a tangent root, there is:

$${\theta }_{n^{\prime }+1}={\theta }_{n^{\prime }}+{\Delta \alpha }_{n^{\prime }+1}$$

(10)

where θ_n′+1 is the angle of rotation of the dual-out shaft stepper motor concerning the bottom surface of the stationary base after the n′ + 1st rotation of the dual-out shaft stepper motor cut the root (°); θ_n′ is the angle of rotation of the dual-out shaft stepper motor concerning the bottom surface of the stationary base after the n′th rotation of the dual-out shaft stepper motor to cut the root (°); Δθ_n′+1 is the n′ + 1st rotation angle of the dual-out shaft stepper motor to cut the root (°).

When θ_n′+1 ˃ θ_n′, the height of movement of the cutting device concerning the lower bottom surface of the stationary base after the n′ + 1st rotation of the dual-out shaft stepper motor to cut the root is:

$${\mathsf{\Delta }h}_{n^{\prime }+1}^{\prime }=l_{JS}·\left(\tan {\theta }_{n^{\prime }+1}-\tan {\theta }_{n^{\prime }}\right)$$

(11)

When θ_n′ ˃ θ_n′+1, the height of movement of the cutting device concerning the lower bottom surface of the stationary base after the n′ + 1st rotation of the dual-out shaft stepper motor to cut the root is:

$${\mathsf{\Delta }h}_{n^{\prime }+1}^{\prime }=l_{JS}·\left(\tan {\theta }_{n^{\prime }}-\tan {\theta }_{n^{\prime }+1}\right)$$

(12)

In Equations (11) and (12), Δh′_n′+1 is the moving height of the cutting device relative to the lower bottom surface of the stationary base after the n′ + 1th rotation of the dual-out shaft stepper motor it cut the root, mm; l_JS is the distance between the two rotating centers of the lifting arm, mm. In particular, the cutting device raises Δh′_n’+1; when θ_n′ ˃ θ_n′+1, the cutting device decreases Δh′_n′+1.

So far, the equations of the motion relationship between the angle of the stepper motor of the double outgoing axis in the cutting device and the change in the height of the cutting device are deduced, which lays the foundation for the design of the adaptive root-cutting control algorithm for garlic based on machine vision.

2.4. Adaptive Root-Cutting Conveyor Alignment Assembly Study

The analysis in Section 2.2 shows that the conveyor alignment assembly can realize the positional adjustment of the garlic plant and the alignment of the upper surface of the bulb, which is a prerequisite for realizing adaptive root cutting on the garlic harvester. Due to the limited space, it is necessary to improve the existing conveyor alignment assembly to arrange the adaptive garlic root-cutting device based on machine vision. The adaptive root-cutting system for the garlic harvester proposed in this study relies on the clamping conveyor chain of the conveyor alignment assembly, the sorting-alignment device, and the harvesting table weld, and it is suggested to remove the garlic roots after completing the adjustment of the position of the garlic plant and the alignment of the upper surface of the bulb.

To carry out adaptive root cutting, the harvesting table weld of the original conveyor alignment assembly was improved, and the space under the lower part of the sorting-alignment device was enlarged without changing the overall structure of the original conveyor alignment assembly, so that the cutting device could be arranged on top of the lower beam of the harvesting table weld and ensure that its disk cutter was parallel to the plane in which the toggle finger chain was located. A laser alignment sensor was mounted on the middle beam of the harvesting table weld (LUOSHIDA, LQD-31NO, Dongguan, China); the adaptive root-cutting conveyor alignment assembly is shown in Figure 6. For adaptive cutting root delivery alignment assembly, in the working state of the laser alignment sensor, with real-time detection of the garlic plant to arrive at the image acquisition position, the garlic plant passes through the laser alignment sensor, triggering the laser alignment sensor to send a signal to the adaptive cutting root control system, allowing the cutting device to work.

2.5. Study on the Speed Relationship Between Clamped Conveyor Chain and Toggle Finger Chain

Before cutting the roots, the sorting-alignment device of the adaptive root-cutting conveyor alignment assembly adjusts the garlic plant, as shown in Figure 7. The red arrows in Figure 7 indicate the conveying direction of the clamping conveyor chain and the conveying direction of the toggle finger chain, respectively. When the inclined garlic plant starts to enter the sorting-aligning device, point Z₁ is the clamping point of the clamping conveyor chain on the garlic plant, and point B₁ is the point of the toggle finger chain’s action on the garlic plant. Next, due to the difference in speeds between the speed of the clamping conveyor chain and the speed of the toggle finger chain, the garlic plant’s position is altered during the conveying process. The upper surface of the bulb is aligned. In terms of surface alignment, the upper surface of the bulb is against the lower surface of the two adjacent toggle fingers by adjusting the clamping conveyor chain speed and the speed of the toggle finger chain speed ratio, to ensure that the upper surface of the bulb is aligned perpendicular to the toggle finger chain located in the plane of the garlic plant, which triggers the laser alignment sensor. At this time, Z₂ is the clamping conveyor chain on the garlic plant of the clamping point, and the B₂ point is the toggle finger chain on the bulb of the equivalent role of the point; with the continuation of the conveying, clamping begins. As the conveying continues, the angle between the clamping conveyor chain and the toggle finger chain makes the garlic plant always subject to the tension along the stalk direction, the upper surface of the bulb is always tightly pressed against the lower surface of the two neighboring toggle fingers, and the anisotropic stalk will undergo bending and deformation, so that the centerline of the bulb can be kept perpendicular to the plane where the toggle finger chain is located under the joint action of the two neighboring toggle fingers. When the root cutting starts, the Z₃ point and the B₃ point are the clamping points of the clamping conveyor chain and the toggle finger chain, respectively, and the equivalent action point of the toggle finger chain on the bulb. Points Z₃ and B₃ are the equivalent action points of the clamping conveyor chain on the garlic plant and the toggle finger chain on the bulb, respectively, when root cutting starts.

According to the clamping conveyor chain and the toggle finger chain uniform speed, movement can be seen.

$${\displaystyle \frac{l_{Z12}}{l_{Z23}}}={\displaystyle \frac{l_{B12}}{l_{B23}}}$$

(13)

In the above equation, l_Z12 is the projection in the horizontal direction of the movement distance between the garlic plant at the clamping points Z₁ and Z₂, mm; l_Z23 is the projection in the horizontal direction of the movement distance between the garlic plant at the clamping points Z₂ and Z₃, mm; l_B12 is the projection in the horizontal direction of the distance between the point of action of the finger-toggle finger chain on the garlic plant, B₁, and the point of action of the finger-toggle finger chain on the bulb equivalent action point, B₂, mm; and l_B23 is the projection in the horizontal direction of the distance between the finger-toggle finger chain is the projection in the horizontal direction of the distance between the equivalent action point B₂ of the toggle finger chain on the bulb and the equivalent action point B₃ of the toggle finger chain on the bulb, mm.

After the sorting-alignment device process to adjust the position of the garlic plant, it is necessary to study the speed ratio between the speed of the clamping conveyor chain and the speed of the toggle finger chain to realize the completion of the alignment of the upper surface of the bulb perpendicular to the plane of the toggle finger chain of the garlic plant, triggering the laser alignment sensor.

From Equation (6) and Figure 7b, the ratio K_G between the speed of the gripping conveyor chain and the speed of the toggle finger chain required to achieve the triggering of the laser alignment sensor by the garlic plant perpendicular to the plane in which the toggle finger chain is located to accomplish the alignment of the upper surface of the bulb is:

$$K_G={\displaystyle \frac{l_{D_K{Z}_2}\sin {\alpha }_E-l_{B_1{D}_K}\cos \left({\alpha }_E+{\gamma }_{BC}\right)}{l_{B_1{B}_2}\sin {\alpha }_E}}$$

(14)

where α_E is the angle between the garlic plant and the clamping conveyor chain when it enters the sequencing-alignment device (°); γ_BC is the angle between the clamping conveyor chain and the finger-dialing chain (°); l_DKZ2 is the distance moved from the clamping point of the clamping conveyor chain of the garlic plant when the garlic plant is kept perpendicular to the plane where the finger-dialing chain is located to the point of action of the clamping conveyor chain of the triggering laser alignment sensor, mm; l_B1DK is the distance between the clamping point of the distance between the gripping point of the clamping conveyor chain and the point of action of the toggle finger chain when the garlic plant enters the sorting-aligning device when the garlic plant remains perpendicular to the plane in which the toggle finger chain is located, mm; l_B1B2 is the distance between the point of action of the toggle finger chain on the garlic plant and the equivalent point of action of the toggle finger chain on the bulb from the time when the garlic plant enters the sorting-aligning device to the triggering of the laser alignment sensor, mm.

Substituting the value of each distance, the angle between the clamping conveyor chain and the toggle finger chain into Equation (14), we have

$$K_G={\displaystyle \frac{150\sin {\alpha }_E-84\cos \left({\alpha }_E+15°\right)}{140\sin {\alpha }_E}}$$

(15)

where α_E is the angle between the garlic plant and the clamping conveyor chain when entering the sorting-alignment device (°).

When the harvester operates after the digging depth is fixed by adjusting the travel wheels, it can be considered that the angle α_E between the plucked garlic plant and the clamping conveyor chain is fixed. The angle α_E can be deduced from the inclination angle of the conveying alignment assembly, thus determining the ratio of the speed of the clamping conveyor chain and the speed of the toggle finger chain, K_G [40,41]. During harvesting operation, the inclination angle of the conveyor alignment assembly is usually 11°~34° [13,19,42]. The garlic plants in the farmland are approximately perpendicular to the ground, so the deduction shows that the angle αE is 56°~79°, and the value of KG is 0.836~1.114 by substituting into Equation (15). Then, K_G takes the intermediate value of 0.975 during the combined garlic harvesting.

2.6. Garlic Adaptive Root-Cutting System Working Principle

According to the need to establish the relationship between the pixel coordinate system and the image coordinate system, the adaptive root-cutting system of the garlic harvester resets the cutting device first when it starts to work. The adaptive root-cutting conveying and aligning assembly pulls the garlic plant dug by the digging shovel out of the soil using the clamping conveyor chain. The garlic plant enters the sorting-aligning device along the clamping conveyor chain. It continues to be conveyed under the joint action of the clamping conveyor and finger-pulling chains. The clamping point moves along the direction of the garlic plant’s stem due to the structure of the clamping chain, the long bristle brush, and the garlic plant’s position being adjusted. The bulb’s upper surface is aligned under the finger-pulling chain action, and the stem is aligned with the laser alignment sensor, triggering the laser alignment sensor, and the cutting device camera takes a bulb image. The Matlab-based IRM-YOLO bulb detector [27,28] was converted to NVIDIA CUDA code by GPU Coder (NVIDIA, Santa Clara, CA, USA) and deployed in the edge computing device Jetson Nano. The Jetson Nano (NVIDIA, Jetson Nano(4GB), Santa Clara, CA, USA) deployed with the bulb detector detects the bulb by convolutional neural network to obtain the classification and pixel position information of the target, i.e., to obtain the pixel position information of the cutting line. Based on the cutting device’s control algorithm and the cutting line’s pixel position information, we obtain the digital command code to control the rotation angle of the dual-out shaft stepper motor. The Jetson Nano processor sends the digital command code to the STM32F103 development board (ALENTEK, Guangzhou, China) through serial communication, and the dual-out shaft stepper motor will be rotated to the corresponding angle to complete the adjustment of the cutting height under the control of the STM32F103 development board. After that, the cutting height will be adjusted, and the garlic root is removed by rotating the circular knife disk.

The IRM-YOLO bulb detection convolutional neural network model, shown in Figure 8 [27,28], accurately, quickly, and reliably detects bulb targets in images with variable light and soil by learning bulb features.

Five hundred randomly selected pairs of captured bulb images constitute the test set [27], and the confidence score of the bulb detector trained using the IRM-YOLO algorithm model is 0.98228, with an AP of 99.2%; the detection time is 0.0356 s. The detection results are shown in Figure 9. The space the detector occupies is 24.2 MB. The detection results of IRM-YOLO are shown in Figure 10. The results show that the proposed IRM-YOLO has excellent detection performance. Firstly, IRM-YOLO can work generally under different environmental brightness conditions without supplemental light treatment, which reduces the detection cost. Secondly, the soil adhering to the bottom of the bulb does not affect the detection performance of IRM-YOLO, which can still make correct and fast detection. Finally, IRM-YOLO is an intelligent means of detection, with the ability to predict the cutting position of bulbs of different shapes.

The detection results of the bulb target provide a basis for the cutting device to adjust the cutting height. Adaptive root cutting based on machine vision can avoid cutting bulbs and improve the clean-cutting rate of garlic roots.

2.7. System Design and Software Synergy

The adaptive root-cutting control system’s hardware consists of the edge computing device Jetson Nano, power module, camera, STM32F103 development board, laser alignment sensors, dual-out shaft stepper motor, soil clearing servo, and stepper motor driver, as shown in Figure 11.

Edge computing device Jetson Nano is a high-performance processor developed by NVIDIA with powerful performance, compact size, and suitable for mobile equipment, with specific specifications and parameters shown in

Table 1. Jetson Nano device specifications list.

Configuration	Parameters
AI algorithm	472GFLOPS
GPU	NVIDIA Maxwell build, 128 NVIDIA CUDA cores
CPU	Quad-core ARM Cortex-A57 MP Core processor
Display memory	4 GB 64-bit LPDDR4
Power input	5 V-4 A DC/5 V-3 A micro USB
Size	100 × 80 × 29 mm

, capable of running a variety of algorithms and AI frameworks, such as TensorFlow, Keras, PyTorch, Caffe, and other applications in the fields of image categorization, target detection, face recognition, speech processing, object recognition tracking, etc. Jetson Nano includes a rich set of peripheral interfaces, including SPI, UART, USB, etc., which are suitable for controlling many different types of peripherals, as well as a comprehensive NVIDIA JetPack SDK containing acceleration libraries for computer vision, deep learning, graphics, multimedia, and more.

Using GPU Coder, it is possible to transcode Matlab code to CUDA code, automatically cross-compile and deploy the resulting code to the Jetson Nano, and accelerate deep learning networks and other computationally intensive parts of the algorithm.

UART communication is used between the Jetson Nano and the STM32F103 development board. The baud rate is set to 115,200 b/s, and the control commands are transmitted using decimal numeric command codes, with values ranging from 0 to 199. The meanings of the numeric command codes are shown in

Table 2. Turning angle digital command code.

Items	Value	Meaning
a1	0	Dual-out shaft stepper motor angle reduction
	1	Dual-out shaft stepper motor corner increase
a	0~99	Decrease in angle of rotation, angle of rotation
	100~199	Increased angle of rotation, angle of rotation

Note: a₁ is the hundredth value of a.

.

The STM32F103 development board receives the digital command code, as shown in Table 2, to control the rotation of the dual-out shaft stepper motor. Adjusting the subdivision and setting the stepper motor drive to 5000 pulse/rev give the pulse-width-modulated signal a single pulse turn angle of 0.072°. Using STM32CubeMX and Keil5 development software, set the clock of the STM32F103ZET6 chip as 72 MHz; obtain the pulse width modulation signal period T_min as 0.08 ms by configuring timer, counter, and interrupt controller; and stipulate that five consecutive single pulses corresponding to 1 minimum rotational angle α_min so that the rotation accuracy of dual-out shaft stepper motor is 0.36°, and the time consumed for one minimum rotation angle α_min is 0.4 ms. The maximum angle range of each rotation of the dual-out shaft stepper motor is [−35.64, 35.64] (unit: °), but the actual cutting of the root is to avoid the collision of the cutting device with other parts, which restricts the range of the rotation angle of the dual-out shaft stepper motor.

2.8. Adaptive Root-Cutting Control Algorithm Design

The adaptive root-cutting control algorithm is more complex than the trial bench root-cutting control algorithm because the rotation of the dual-out shaft stepper motor of the cutting device regulates the cutting height. However, the logic of the adaptive root-cutting control algorithm is the same as that of the test bench root-cutting control algorithm. After the relationship between the pixel coordinate system and the image coordinate system is established, the rotation angle of the dual-out shaft stepper motor is calculated based on the pixel position information of the cutting line. Then, the cutting device is moved to the appropriate cutting height to cut the root.

After completing the previous root cutting, the upper computer system converts the pixel coordinates of the cutting line according to the pixel coordinates of the cutting line predicted to obtain the image coordinates of the cutting line. It feeds back to the motor control system to adjust the movement of the cutting device along the height direction. Before and after the two cuts, the movement of the cutting device along the height direction is:

$$\mathsf{\Delta }{h}_{n+1}=\left(k_{n+1}-k_n\right){{\rho }_h}^{″} (\mathrm{n}\in \mathrm{N}+)$$

(16)

where Δh_n+1 is the coordinate difference between the n + 1st cut root and the projection point of the nth cut root cutting line along the height direction in the image coordinate system, and its value is positive when the cutting device moves upward along the height direction and harmful when the cutting device moves downward along the height direction, mm:

k_n is the nth predicted cut line pixel vertical coordinate;

k_n+1 is the n + 1th predicted pixel vertical coordinate of the cut line;

ρ_h″ is the height of each pixel point in the detected image, mm/pixel.

In addition, a relationship exists when the roots are cut for the first time:

$$\mathsf{\Delta }{h}_1=\left(k_1-k_0\right){{\rho }_h}^{″}$$

(17)

where k₀ is the pixel vertical coordinate of the cutting surface of the cutting device after completing the reset.

Based on Equations (7)–(12), (16), and (17), and pixel coordinate information of the predicted cut line position, the following equations can be obtained.

When adjusting the cutting height, the angle Δα₁ of the first rotation of the dual-out shaft stepper motor is:

When k₀ > k₁, the cutting height of the cutting device should rise after the rotation of the dual-out shaft stepper motor

$$\mathsf{\Delta }{\mathsf{\alpha }}_1=\arctan \left[{\displaystyle \frac{l_{\mathrm{J}\mathrm{S}}\tan {\alpha }_{\mathrm{D}\mathrm{J}}+\left(k_0-k_1\right){{\rho }_h}^{″}}{l_{\mathrm{J}\mathrm{S}}}}\right]-{\alpha }_{\mathrm{D}\mathrm{J}}$$

(18)

When k₁ > k₀, the cutting height of the cutting device should decrease after the rotation of the dual-out shaft stepper motor

$$\mathsf{\Delta }{\mathsf{\alpha }}_1=\arctan \left[{\displaystyle \frac{l_{\mathrm{J}\mathrm{S}}\tan {\alpha }_{\mathrm{D}\mathrm{J}}-\left(k_1-k_0\right){{\rho }_h}^{″}}{l_{\mathrm{J}\mathrm{S}}}}\right]-{\alpha }_{DJ}$$

(19)

When adjusting the cutting height, the angle Δα_n′+1 of the n′ + 1st rotation of the dual-out shaft stepper motor is (n′∈N+):

When k_n′ > k_n′+1, the cutting height of the cutting device should rise after the dual-out shaft stepper motor is rotated.

$${\mathsf{\Delta }\alpha }_{n^{\prime }+1}=\arctan \left[{\displaystyle \frac{l_{\mathrm{J}\mathrm{S}}\tan {\theta }_{n^{\prime }}+\left(k_{n^{\prime }}-k_{n^{\prime }+1}\right){{\rho }_h}^{″}}{l_{\mathrm{J}\mathrm{S}}}}\right]-{\theta }_{n^{\prime }}$$

(20)

When k_n′+1 > k_n′, the cutting height of the cutting device should decrease after the rotation of the dual-out shaft stepper motor

$${\mathsf{\Delta }\alpha }_{n^{\prime }+1}=\arctan \left[{\displaystyle \frac{l_{\mathrm{J}\mathrm{S}}\tan {\theta }_{n^{\prime }}-\left(k_{n^{\prime }+1}-k_{n^{\prime }}\right){{\rho }_h}^{″}}{l_{\mathrm{J}\mathrm{S}}}}\right]-{\theta }_{n^{\prime }}$$

(21)

In Equations (18)–(21)

k₀ is the pixel longitudinal coordinate of the cutting surface of the cutting device after completing the reset, 300 pixels in this paper (the camera resolution is 800 × 600 pixels);

k_n′ is the n′th predicted pixel vertical coordinate of the cutting line, pixels;

k_n′+1 is the n′th + 1th predicted pixel vertical coordinate of the cutting line, pixels;

α_DJ is the angle of rotation of the dual-out shaft stepper motor for the completion of the rest of the cutting device (°);

Δα₁ is the angle of the first rotation of the stepper motor for cutting root (°);

Δα_n′+1 is the angle of the n′ + 1th rotation of the dual-out shaft stepper motor for root cutting (°);

θ_n’ is the angle of rotation of the dual-out shaft stepper motor relative to the lower bottom surface of the stationary base after the n′th rotation of the dual-out shaft stepper motor to cut the root (°);

l_JS is the distance between the two rotation centers of the lifting arm, mm in mm.

So far, the parameter equation system between the change in the longitudinal coordinates of the pixels of the cutting line predicted by the target detection and the rotation angle of the dual-out shaft stepper motor has been established, and the adaptive root-cutting control algorithm was designed according to this parameter equation system.

It is to be noted that the number of pulse width modulation signal pulses N_n′ is related to the rotation angle Δα_n′ of the dual-out shaft stepper motor when controlling the dual-out shaft stepper motor for the n′th root-cutting rotation as follows

$${\mathsf{\Delta }\alpha }_{n^{\prime }}={\displaystyle \frac{1}{5}}{N}_{n^{\prime }}{\alpha }_{min}+{\mu }_{n^{\prime }}$$

(22)

The time taken for the n′th root-cutting rotation of the dual-out shaft stepper motor is t_n′

$$t_{n^{\prime } }=N_{n^{\prime }}{T}_{min}$$

(23)

In Equations (22) and (23), Δα_n′ is the rotation angle of the n′th root-cutting rotation of the dual-out shaft stepper motor (°); N_n′ is the number of pulse width modulation signal pulses N_n′controlling the dual-out shaft stepper motor to perform the n′th root-cutting rotation; α_min is the minimum rotation angle of the dual-out shaft stepper motor (°); μ_n′ is the error in the n′th root-cutting (°); t_n′ is the time consumed for the n′th tangential rotation of the dual-out shaft stepper motor, s; T_min is the pulse width modulation signal period, s. The pulse width modulation signal period is the time required for the n′th tangential rotation of the dual-out shaft stepper motor.

The error μ_n′ will be small enough to eliminate the cumulative error by resetting the cutting device again since the rotation accuracy of the dual-out shaft stepper motor is 0.36°. The calibration coefficient of the adaptive root-cutting system ρ_h″ = 30 mm/113 pixels = 0.2655 mm/pixel was measured.

2.9. Validation of the Execution Speed of an Adaptive Root-Cutting System

The machine vision-based root-cutting function is the core of the adaptive root-cutting system. The machine vision-based adaptive root-cutting process includes the adaptive root-cutting conveyor alignment assembly for garlic plant position adjustment and bulb upper-surface alignment, the cutting line position predicted by the target detection model, the cutting height adjustment by the cutting device, and the cutting device for removing the garlic root, which requires precise coordination of multiple systems. Among them, whether the cutting height can be quickly adjusted according to the predicted cutting line position is the key to the success of adaptive root cutting. Hence, the execution speed of the adaptive root-cutting system needs to be verified.

The harvester clamping conveyor chain speed VJC is 0.75 m/s; according to Section 2.5, it can be seen that the ratio of clamping conveyor chain speed and toggle finger chain speed K_G is 0.975, and the toggle finger chain speed V_BZ is 0.77 m/s. Generally, garlic plant spacing l_ZJ is more than 110 mm [19,26,42,43,44]. After measurement, when the adaptive root-cutting system works, the distance l_LJ from image capture to the beginning of cutting the root bulb moving along the toggle finger chain is more significant than 100 mm. Taking the minimum value of the plant distance l_ZJ and the distance l_LJ of the bulb moving along the toggle finger chain l_LJ = 100 mm, the target detection model predicts that the allowable time for the position of the cutting line and the cutting device to adjust the height of the cut, t_YX, is 0.129 s. Section 2.7 shows that the maximum value of time consumed by each dual-out shaft stepper motor rotation, t_MDJ, is 0.016 s. After testing the IRM-YOLO detection model deployed on the Jetson Nano, the detection time, t_MJC, is within 0.1 s, and by taking t_MJC = 0.1 s, the following relationship is observed:

$$t_{MDJ} + t_{MJC}<t_{YX}$$

(24)

In the formula, t_MDJ is the maximum value of time consumed for each rotation of the dual-out shaft stepper motor during the operation of the adaptive root-cutting system, s; t_MJC is the maximum detection time of the IRM-YOLO detection model deployed on the Jetson Nano, s; t_YX is the permissible time for the target detection model to predict the position of the cutting line and for the cutting device to adjust the height of the cut, s.

Through the above analysis, it can be seen that the cutting device adopts a parallel four-bar mechanism to effectively shorten the time for executing the cutting height adjustment so that the time consumed by the target detection model for predicting the position of the cutting line and the cutting device for adjusting the cutting height is less than the allowable time. The adaptive root-cutting system can complete the root-cutting process based on the target detection of the convolutional neural network.

3. Results

3.1. Cutting Device Root-Cutting Test

A bench test was designed to verify the root-cutting ability of the cutting device. The cutting device adopts four equal-length lifting arms, a fixed base, and a cutting device with connectors to form a parallel four-bar mechanism. It uses a damping spring to increase the lifting force of the parallel four-bar mechanism, which also enhances the ability of the parallel four-bar mechanism to resist impact forces. As a result of the lifting arm, the cutting height adjustment amount corresponding to the rotation angle of the dual-out shaft stepper motor is amplified, thereby effectively shortening the time required for the cutting device to adjust the cutting height. All garlic plants were obtained from the planting base in Jinxiang County, Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, in this experiment. To retain the moisture of the garlic roots, after the garlic was excavated from the planting base, the roots were wrapped entirely with soil. The treated test samples were delivered to the laboratory within 3 days for root-cutting tests. Random selection was used in the sample pool to determine the test subjects for the root-cutting test [45].

Before the test, the cutting device was fixed on the garlic root-cutting test bench so that the bottom surface of the fixed seat of the cutting device was parallel to the surface of the garlic root-cutting test bench, and the cutting device was reset before the root-cutting test was carried out, as shown in Figure 12. The conveying speed of the X-axis slide was set to 0.8 m/s, and the rotational speed of the cutting device was 1200 r/min. Three groups of root-cutting tests were conducted, with 100 samples in each group. Examining the cut root qualified rate index, we can calculate the cut root qualified rate calculation formula

$${\alpha }_{HG}={\displaystyle \frac{N_H}{N_Z}}\times 100\mathrm{\%}$$

(25)

where α_HG is the qualified rate of the cut root, %;

N_H is the number of garlic plants with qualified cut roots;

N_Z is the total number of garlic plants.

A comparison between before and after root cutting is shown in Figure 13. It can be seen that the cutting device can effectively remove the garlic root and keep the cutting height unchanged during the cutting process. The root-cutting pass rates of the three groups of tests were 99%, 96%, and 97%, and the average value of the root-cutting pass rate was 97.33%, with a total of eight samples in which the residual garlic root was too long.

The root-cutting ability of the cutting device was examined through the root-cutting test. The test results show that the cutting device has good consistency in terms of the root-cutting effect, and damping springs effectively improve the lifting force and reliability of the parallel four-bar mechanism for lifting the cutting device.

3.2. Field Trial

A field trial of combined garlic harvesting was conducted in May 2022 at the planting test field in Jinxiang County, Shandong Province, to test the adaptive root-cutting system. The adaptive root-cutting harvesting system was installed on the harvester. The adaptive root-cutting conveyor alignment assembly was arranged at the front of the harvester to complete the digging and pulling conveyance of the garlic plant, the positional adjustment of the garlic plant and the alignment of the upper surface of the bulb, the prediction of the cutting line position by the bulb detector, and the adaptive root cutting to realize the precise synergy of the multi-systems; the harvester finally conveyed the completed root-cutting bulbs to the fruit box. The garlic plants in the experimental field had good uprightness, with garlic root lengths ranging from 76 to 163 mm and bulb heights ranging from 39.26 to 51.12 mm.

The adaptive root-cutting system for the field trial was mainly composed of an electronic control box, a welded harvesting table, a background board, a cutting device, and a blade speed control board, as shown in Figure 14. The upper computer is a Jetson Nano, the lower computer is an STM32F103 development board, and the stepper motor driver (Ruite Technology, DM542, Shenzhen, China) and other components are installed in the electronic control box.

A garlic harvester installed with the adaptive root-cutting system, as shown in Figure 14, was used for continuous harvesting and root-cutting tests to verify the root-cutting capability of the machine vision-based adaptive root-cutting system for garlic harvesters. In the preparation stage of the test, the adaptive root-cutting conveyor alignment assembly, electronic control box, background board, and cutter speed control board were installed. The cutting device was fixed to the harvesting table weldment by bolts, with the lower-bottom surface of the fixing base of the cutting device parallel to the plane of the toggle finger chain. The camera’s position corresponded to the position for completing the adjustment of the position of the garlic plant and the alignment of the upper surface of the bulb, as shown in Figure 7b. The laser alignment sensors were fixed to the harvesting table weldment by bolts. We installed the laser alignment sensor on the harvesting table weld joint so that the camera could capture the bulb image when the garlic plant triggered the laser alignment sensor; the camera was connected to the Jetson Nano through the extended soft row cable; the UART communication between the Jetson Nano and the STM32F103 development board was established; the clearing servo, the laser alignment sensor, and the stepper motor driver were connected to the STM32F103 development board; and the DC brushless motor of the cutting device was connected to the STM32F103 development board. The DC brushless motor of the cutting device was connected to the cutter speed control board. The wires of the required specifications were used to connect each device to the power supply. The adaptive root-cutting system was tested before the subsequent tests were carried out, and the preparation is shown in Figure 15.

During the adaptive root-cutting system test, the cutting device is first reset to establish the relationship between the pixel coordinate system and the image coordinate system. Then, the clamping conveyor chain and toggle finger chain of the adaptive root cutting and conveying and aligning assembly are activated, with the speed of the clamping conveyor chain at 0.75 m/s and the speed of the toggle finger chain at 0.77 m/s. The harvester advances with a uniform speed of 0.36 m/s. The garlic plant is dug out of the ground under the action of the excavating shovel and the shovel pulling of the clamping conveyor chain. Under the action of the digging shovel and the gripping conveyor chain, the garlic plant is excavated out of the soil, and the garlic plant is transported along the direction of the gripping conveyor chain; after the sorting-aligning device starts to act on the garlic plant, the position adjustment of the garlic plant and the alignment of the bulb along the upper surface are accomplished under the joint action of the gripping conveyor chain and the finger-pulling chain, so that the upper surface of the bulb is close to the lower surfaces of the two neighboring finger-pulling fingers and the centerline of the bulb is perpendicular to the plane where the finger-pulling chain is located; the garlic plant triggers the laser-aligning sensor, and the camera captures the bulb. The garlic plant triggers the laser-projected sensor, the camera captures the bulb image, and the IRM-YOLO-based bulb detector deployed in Jetson Nano predicts the position of the cutting line; the cutting height of the cutting device is dynamically adjusted based on the adaptive root-cutting control algorithm, and the disk knife rotates at high speed to excise the garlic root; the harvesting machine transports the bulb to the fruit box. After the harvester stops, the power supply of the adaptive root-cutting system is cut off. The performance of the adaptive root-cutting system is statistically analyzed based on the bulbs collected in the fruit box, and the test site is shown in Figure 16.

The cut rate α_QS is calculated as

$${\alpha }_{QS}={\displaystyle \frac{N_{QS}}{N_{LZ}}}\times 100\mathrm{\%}$$

(26)

where α_QS is the rate of cut wounding, %;

N_Qs is the number of bulbs cut and wounded;

N_LZ is the total number of bulbs.

Three groups of combined harvesting adaptive root-cutting field trials were conducted; 186 bulbs were harvested under standard transportation of garlic plants, of which 154 bulbs were qualified for root cutting, 5 were cut, 27 residual garlic roots were too long, and 0 were missed. The qualified rate of root cutting was 82.8%, and the rate of cutting injury was 2.7%. The test results show that the adaptive root-cutting system effectively reduces the cut rate of the bulb by adjusting the cutting height through the cutting device. The qualified rate of root cutting is above 80%, which verifies the feasibility of the adaptive root-cutting system of the garlic harvester based on machine vision and the multi-system precise cooperative operation through the field test. In the field test, the qualified rate of root cutting was lower than that in Section 3.1 because the uneven ground in the test field caused the angle α_E between the garlic plant and the clamping conveyor chain to change. There was a situation in which the bulb did not realize the alignment of the upper surface when the camera captured the image.

4. Discussion

(1)

In this paper, we studied garlic bulbs grown in large fields, with garlic root lengths ranging from 75 to 168 mm and bulb heights ranging from 38 to 54 mm. The garlic bulb images, including different sunlight conditions, were collected from 8:00 to 19:00, and 2500 images were collected for bulb detector training and testing. The detection results of the detector described in this study may vary for bulb target detection in different planting locations and under different sunlight conditions.

(2)

The operational performance of the adaptive root-cutting system on a garlic harvester in different regions and under different soil moisture and hardness conditions must be further investigated. In addition, the interaction between various components of the garlic harvester needs to be studied.

(3)

The joint application of the adaptive root-cutting system and garlic harvester must also consider different clamping conveyor chain speeds, forward speeds, harvester types, etc., which must be studied.

(4)

In the literature [25], the cutting rate of the bench test was 2.78%, and the root-cutting pass rate was 93.17%; in comparison, the cutting device in this study had a root-cutting pass rate of 97.33% in the bench test, and the harvesting machine field test had a cutting rate of 2.7% and a root-cutting pass rate of 82.8%. Compared to the bench test, there was a decrease in the passing rate of root cutting in the field test of the harvester. The possible reason for the lower pass rate was that the field ground was uneven, and there were cases where the bulbs did not realize the alignment of the upper surface when the camera obtained the image.

(5)

In the literature [46], an improved apple detection model used in orchards was deployed using Jetson Nano. The authors of [47] used a computer vision-based detection model in strawberry harvesting machinery deployed via Jetson Nano. It can be seen that the Jetson family of low-power edge computing devices is often used to deploy AI detectors on agricultural mobile platforms.

(6)

With the further development of information sensing technology and its application in agriculture [48], the root-cutting operation during garlic harvesting also requires new technological breakthroughs to improve the intelligence of garlic harvesting machines.

5. Conclusions

(1)

The existing garlic mechanized root-cutting technology has problems, with a high leakage rate, high cutting injury rate, and uneven root cutting. We propose a non-contact adaptive root-cutting method for bulbs based on the YOLO algorithm based on a 4DSL-7 garlic harvester.

(2)

In this study, for the structural characteristics and working principle of the conveyor alignment assembly of the 4DSL-7 garlic harvester, a cutting device integrating a camera, a circular cutter disk, a dual-out shaft stepper motor, a microswitch, and a lifting arm is designed, and an adaptive root-cutting conveyor alignment assembly is created. The cutting device can realize image acquisition functions, drive the circular knife disk rotation, reset the cutting device, adjust the cutting height, etc. The equations of the relationship between the angle of the dual-out shaft stepper motor and the cutting height are deduced. In the adaptive root-cutting and conveying alignment assembly, the action mechanism of the adaptive root-cutting system, the garlic plant bulb, is studied to realize the bulb alignment along the upper surface and adjust the garlic plant’s position. Through dynamic analysis, the ratio of the clamping conveyor chain’s speed and the toggle finger chain’s speed was deduced to be 0.975.

(3)

The confidence score of the bulb detector trained based on the IRM-YOLO algorithm model is 0.98228, with an AP of 99.2%; the detection time is 0.0356 s, and the storage space occupied by the detector is 24.2 MB. By setting the digital command code, the STM32F103 development board controls the dual-out shaft stepper motor, and the angle of a single rotation is in the range of [−35.64, 35.64] (unit: °). A set of parametric equations between the predicted change in the longitudinal coordinates of the pixels of the cutting line and the rotation angle of the dual-out shaft stepper motor was established, and an adaptive cut-root control algorithm was designed. The allowable time for adjusting the cutting height was calculated to be 0.129 s, and the execution speed of the adaptive root-cutting system was verified.

(4)

The cutting device bench test was designed to verify the cutting device’s root-cutting capability. When the X-axis slide conveyor speed of the test bench was 0.8 m/s and the cutter speed of the cutting device was 1200 r/min, the average value of the qualified root-cutting rate was 97.33%.

(5)

The adaptive root-cutting system was installed on a garlic harvester, the bulb detector was deployed on a Jetson Nano, and the STM32F103 development board was used as the lower computer for a garlic harvesting field test. The speed of the clamping conveyor chain was 0.75 m/s, the speed of the finger-pulling chain was 0.77 m/s, and the harvester moved at a uniform speed of 0.36 m/s. The passing rate of root cutting was 82.8%, and the cutting injury rate was 2.7%. The feasibility of the adaptive root-cutting system and the multi-system precision cooperative operation were tested.