Poplar growth dynamic monitoring system integrated with multi-stage branch parameter acquisition function

Abstract

The present application provides a poplar growth dynamic monitoring system integrated with multi-stage branch parameter acquisition function, and belongs to the technical field of intelligent forest phenotyping monitoring. The system of the present application is installed with a liftable and rotatable intelligent sensing ring at different heights of the poplar trunk, and is integrated with an RGB-D camera, a laser ranging, an ultrasonic wave, a pressure sensing and an inclination sensor, so as to realize the whole-year continuous and in-situ automatic monitoring of the east-west-south-north crown width, the number of first to third branches, the branch angle and the base thickness. The system of the present application has the functions of self-adaptive ring diameter adjustment, solar and wind energy combined power supply, IP67 protection and tree bark simulation hidden design, and can stably operate in a high canopy stand. The present application can obtain data in real time through a remote platform, significantly improve the observation accuracy and efficiency, reduce the labor cost, and provide key technical support for poplar ideal plant type breeding, precise tending and intelligent forestry.

Description

Technical Field

[0001] This invention relates to the field of intelligent phenotypic monitoring technology for forest trees, and more specifically, to a poplar growth dynamic monitoring system that integrates multi-level branching parameter acquisition functions. Background Technology

[0002] Poplar is one of the world's most widely distributed and cultivated fast-growing timber species, playing a vital role in global timber production, bioenergy development, and ecological protection systems. In my country, it is not only one of the five priority tree species for national reserve forest construction but also a core model species for forest genetic improvement and molecular design breeding. With the deepening of the goals of timber security and ecological security, cultivating new poplar varieties with ideal plant types, including narrow crowns, few lateral branches, large branching angles, and strong trunks, has become a key task for the high-quality development of modern forestry.

[0003] However, the accurate observation of poplar tree morphology currently faces multiple technical bottlenecks: (1) There are many varieties of poplar trees. Individuals with different genotypes show significant differences in canopy structure, branching level, first, second, third and above, and spatial configuration. Traditional phenotyping methods are difficult to quantify systematically. (2) As the tree ages, the tree height increases rapidly, with an average annual height growth of 2–3 meters. The upper and top canopy is severely obscured by dense branches and leaves, making it impossible to effectively obtain key branching parameters, including the number of narrow branches, branching angle, and base thickness. (3) Due to the high afforestation density and large canopy closure, conventional observation can usually only be carried out during the leaf fall period, and it is necessary to rely on climbing or manual visual inspection, which is not only inefficient and dangerous, but also results in scattered and poor representative data. (4) In order to achieve the directional selection of ideal plant type, it is necessary to continuously and accurately monitor the core traits during the dynamic growth process, including the east-west and north-south crown width, the number of branches at each level, the branching angle and the thickness of the branch base. However, the existing methods are difficult to meet this requirement. (5) Although LiDAR and UAV remote sensing technologies have been tried to be applied to the acquisition of tree phenotypes in recent years, their penetration ability in high-density forest stands is limited, the recognition rate of small branches, especially third-level branches is low, the point cloud data processing is complicated, the extraction error is large, and the equipment cost is high and the operation is highly professional, making it difficult to apply on a large scale in grassroots forest farms.

[0004] Therefore, an intelligent monitoring system capable of in-situ, real-time, automated, and high-precision acquisition of multi-level branching structure parameters of poplar trees is needed to break through the technological loop of visibility, accuracy, speed, and usability, and to support the breeding of superior poplar varieties and intelligent forestry management. Therefore, a poplar growth dynamic monitoring system integrating multi-level branching parameter acquisition functions is proposed. Summary of the Invention

[0005] The purpose of this invention is to address the problems raised in the existing background technology. To achieve the above-mentioned objective, this invention provides the following technical solution: a poplar growth dynamic monitoring system integrating multi-level branching parameter acquisition function, comprising intelligent sensing rings, installed at intervals along the axial direction of the poplar trunk, with an installation height ranging from 1.0 m to 6.0 m, and a vertical spacing of 1.0–2.0 m between adjacent sensing rings; The multimodal sensing module, integrated on each sensing ring, is used to simultaneously collect data on east-west and north-south canopy width, number of first- to third-level branches, branch angle, and thickness of branch base; Edge computing units built into each sensing ring are used to process raw sensing data locally and extract structured growth parameters. The wireless communication module supports LoRaWAN or NB-IoT protocols, with a communication distance of ≥3 km and a data upload frequency adjustable from 1 time / 6 h to 1 time / 24 h. The self-powered and environmental protection unit ensures continuous and stable operation of the system under ambient temperature conditions of −25℃ to +60℃, relative humidity ≤95%, and rainfall intensity ≤50 mm / h. A remote monitoring and data management platform is used by users to remotely view, analyze, and control the monitoring process.

[0006] As a preferred technical solution of the present invention, the liftable and rotatable intelligent sensing ring is made of carbon fiber reinforced polyamide composite material, with an overall weight of ≤800 g and an adjustable outer diameter range of 15 cm to 40 cm. The outer surface is coated with an anti-ultraviolet polyurethane coating with a bark-like texture and a reflectivity of ≤15%. Microencapsulated animal repellent is embedded in the ring, providing an effective protection radius of ≥30 cm.

[0007] As a preferred technical solution of the present invention, the multimodal sensing module includes: An RGB-D depth camera with a resolution of 1280×720, a depth measurement accuracy of ±2 mm, and an effective detection distance of 0.3–5.0 m; A pair of laser rangefinders are arranged in the east-west direction and the north-south direction, with a range of 0.1–10 m and a repeatability of ±1 mm; An ultrasonic sensor array, operating at a frequency of 40 kHz, has an effective detection depth ≥1.5 m penetrating the leaf canopy. Annular flexible pressure sensing band, measuring range 0–50 N, nonlinear error ≤ ±1.5%FS, used for inverting the diameter of branch base, measuring range 5–50 mm, accuracy ±0.5 mm; Digital tilt sensor with a measurement range of ±90°, a resolution of 0.1°, and an accuracy of ±0.3°.

[0008] As a preferred technical solution of the present invention, the multiple intelligent sensing rings are arranged in three layers according to vertical height: the low-level ring is installed at a height of 1.0–2.0 m and is used to monitor the first-level branches; the middle-level ring is installed at a height of 2.5–4.0 m and is used to monitor the second-level branches; the high-level ring is installed at a height of 4.5–6.0 m and is used to monitor the third-level and above fine branches, forming a vertical gradient observation network covering the entire canopy.

[0009] As a preferred technical solution of the present invention, the edge computing unit is equipped with a lightweight artificial intelligence model. This model is based on an improved PointNet++ architecture, with a parameter size of ≤1.2 MB, an inference speed of ≥8 FPS on an ARM Cortex-A53 processor, a recall rate of ≥92% for identifying branches of level two and above, and a false detection rate of ≤5%.

[0010] As a preferred technical solution of the present invention, the branch angle is calculated by combining the measured value of the tilt sensor with the spatial coordinate transformation algorithm, and the comprehensive measurement error is ≤±2°; the thickness of the branch base is obtained by matching the real-time deformation of the pressure sensing strip with the pre-calibrated diameter-pressure mapping database, with a calibration sample number ≥200 sets, and the root mean square error of thickness inversion RMSE ≤0.8 mm.

[0011] As a preferred technical solution of the present invention, the liftable and rotatable intelligent sensing ring has a built-in lifting guide rail and a rotating mechanism driven by a micro stepper motor. The lifting stroke is 0–30 cm, the lifting speed is 0.5–2 cm / s, the rotation angular velocity is 0.5–5° / s, and it supports automatic execution of 1–4 complete 360° scanning cycles per day.

[0012] As a preferred technical solution of the present invention, the self-powered and environmental protection unit includes a flexible monocrystalline silicon solar thin film with a conversion efficiency of ≥22% and an area of ​​≥0.06 m², a micro vertical axis wind turbine with a starting wind speed of ≤2.5 m / s, an IP67 waterproof and dustproof sealed shell, and a low-power power management module to ensure that the system can operate continuously for ≥180 days without external power supply and has a standby power consumption of ≤15 mW.

[0013] As a preferred technical solution of the present invention, the intelligent sensing ring is equipped with an adaptive ring diameter adjustment mechanism. The ring tension is monitored in real time by strain gauges. When the average daily radial growth of the tree trunk is detected to be ≥0.05 mm, the ring diameter fine adjustment mechanism is automatically triggered with an adjustment step of 0.1 mm / time and a maximum adjustment range of ±10 mm, ensuring long-term fit without damaging the cambium.

[0014] As a preferred technical solution of the present invention, the remote monitoring and data management platform provides a web and mobile visualization interface, supports querying historical data by tree number, forest compartment, and time interval, synthesizes crown expansion rate curves with an accuracy of ±2cm / month, branch development heatmaps and three-dimensional tree shape evolution animations, and has an abnormal early warning function: when the branch angle mutation is greater than 15° / week or the crown growth stagnates for more than 30 days, an alarm message is automatically pushed.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention constructs a vertical gradient observation network by deploying a liftable and rotating intelligent sensing ring at different heights on the tree trunk, and for the first time realizes the synchronous, continuous and in-situ collection of the number of primary, secondary and tertiary branches, branch angles, branch base thickness and crown width in the east, west, south and north directions, effectively overcoming the observation blind spots caused by foliage obstruction and forest canopy closure.

[0016] The system of this invention supports daily automatic scanning and runs 24 / 7. It can capture dynamic events such as the monthly expansion rate of the crown and sudden damage to branches during the growing season, avoiding the loss of growth information caused by traditional annual or quarterly observations. It provides high-frequency, high-dimensional data support for the study of plant type development patterns and early selection of superior varieties.

[0017] This invention allows users to remotely view, analyze, and control equipment via the web or mobile devices, truly enabling people to work indoors while data is stored in the cloud, significantly reducing the intensity of fieldwork and labor costs.

[0018] The high-precision plant type parameters obtained by this invention can be seamlessly integrated with the forest tree genome selection platform to accelerate the screening process of ideal plant type individuals such as narrow crown, few branches, and large angles; at the same time, it supports early warning of abnormal growth, including branch angle mutation and crown stagnation, providing real-time basis for pruning, thinning and other tending decisions, and helping to build high-quality national reserve forests.

[0019] This invention fundamentally solves the industry pain points of difficulty in observing, inaccurately measuring, and unsustainable multi-level branching traits in poplar trees, fills the technological gap in the field of intelligent phenotyping equipment for forest trees, and has important application value for promoting the improvement of forest tree varieties in my country and ensuring national timber and ecological security. Attached Figure Description

[0020] Figure 1 This is a data block diagram of the overall architecture of the poplar growth dynamic monitoring system provided by the present invention; Figure 2 This is a block diagram of the sensor loop characteristic data provided by the present invention; Figure 3 This is a block diagram of the sensor data of the multimodal sensing module provided by the present invention; Figure 4 This is a data block diagram of the vertical layout of the intelligent sensing ring provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention.

[0022] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely illustrates some embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. It should be noted that, in the absence of conflict, the embodiments and features and technical solutions in the embodiments of the present invention can be combined with each other. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0023] Example 1: A poplar growth dynamic monitoring system integrating multi-level branching parameter acquisition function, including intelligent sensor rings, which are installed at intervals along the axial direction of the poplar trunk, with an installation height range of 1.0 m to 6.0 m and a vertical spacing of 1.0–2.0 m between adjacent sensor rings; The multimodal sensing module, integrated on each sensing ring, is used to simultaneously collect data on east-west and north-south canopy width, number of first- to third-level branches, branch angle, and thickness of branch base; Edge computing units built into each sensing ring are used to process raw sensing data locally and extract structured growth parameters. The wireless communication module supports LoRaWAN or NB-IoT protocols, with a communication distance of ≥3 km and a data upload frequency adjustable from 1 time / 6 h to 1 time / 24 h. The self-powered and environmental protection unit ensures continuous and stable operation of the system under ambient temperature conditions of −25℃ to +60℃, relative humidity ≤95%, and rainfall intensity ≤50 mm / h. A remote monitoring and data management platform is used by users to remotely view, analyze, and control the monitoring process.

[0024] The liftable and rotating smart sensing ring is made of carbon fiber reinforced polyamide composite material, with an overall weight of ≤800 g and an adjustable outer diameter range of 15 cm to 40 cm. The outer surface is coated with a UV-resistant polyurethane coating with a bark-like texture and a reflectivity of ≤15%. It also incorporates microencapsulated animal repellent, providing an effective protection radius of ≥30 cm.

[0025] The multimodal sensing module includes: An RGB-D depth camera with a resolution of 1280×720, a depth measurement accuracy of ±2 mm, and an effective detection distance of 0.3–5.0 m; A pair of laser rangefinders are arranged in the east-west direction and the north-south direction, with a range of 0.1–10 m and a repeatability of ±1 mm; An ultrasonic sensor array, operating at a frequency of 40 kHz, has an effective detection depth ≥1.5 m penetrating the leaf canopy. Annular flexible pressure sensing band, measuring range 0–50 N, nonlinear error ≤ ±1.5%FS, used for inverting the diameter of the branch base, measuring range 5–50 mm, accuracy ±0.5 mm; Digital tilt sensor, measuring range ±90°, resolution 0.1°, accuracy ±0.3°.

[0026] Multiple intelligent sensor rings are arranged in three layers according to vertical height: the low-level ring is installed at a height of 1.0–2.0 m and is used to monitor first-level branches; the middle-level ring is installed at a height of 2.5–4.0 m and is used to monitor second-level branches; the high-level ring is installed at a height of 4.5–6.0 m and is used to monitor third-level and above fine branches, forming a vertical gradient observation network covering the entire canopy.

[0027] The edge computing unit is equipped with a lightweight artificial intelligence model based on an improved PointNet++ architecture. The model has ≤1.2 MB of parameters, an inference speed of ≥8 FPS on an ARM Cortex-A53 processor, a recall rate of ≥92% for identifying branches of level two and above, and a false positive rate of ≤5%.

[0028] The branch angle is calculated by combining the measured value of the tilt sensor with the spatial coordinate transformation algorithm, and the overall measurement error is ≤±2°; the thickness of the branch base is obtained by matching the real-time deformation of the pressure sensing strip with the pre-calibrated diameter-pressure mapping database, with a calibration sample size of ≥200 sets, and the root mean square error of the thickness inversion RMSE ≤0.8 mm.

[0029] The liftable and rotatable smart sensor ring has a built-in micro stepper motor-driven lifting guide rail and rotation mechanism. The lifting stroke is 0–30 cm, the lifting speed is 0.5–2 cm / s, and the rotation angular velocity is 0.5–5° / s. It supports automatic execution of 1–4 complete 360° scanning cycles per day.

[0030] The self-powered and environmental protection unit includes a flexible monocrystalline silicon solar thin film with a conversion efficiency of ≥22% and an area of ​​≥0.06m², a micro vertical axis wind turbine with a starting wind speed of ≤2.5 m / s, an IP67-rated waterproof and dustproof sealed housing, and a low-power power management module to ensure continuous operation of the system for ≥180 days without external power supply, with a standby power consumption of ≤15 mW.

[0031] The intelligent sensing ring is equipped with an adaptive ring diameter adjustment mechanism. It monitors the ring tension in real time through strain gauges. When the average daily radial growth of the tree trunk is detected to be ≥0.05 mm, the ring diameter fine adjustment mechanism is automatically triggered. The adjustment step is 0.1 mm / time, and the maximum adjustment range is ±10 mm, ensuring long-term fit without damaging the cambium.

[0032] The remote monitoring and data management platform provides a web and mobile visualization interface, supports querying historical data by tree number, forest compartment, and time interval, synthesizes crown expansion rate curves with an accuracy of ±2 cm / month, branch development heatmaps and three-dimensional tree type evolution animations, and has an abnormal early warning function: when the branch angle mutation is greater than 15° / week or the crown growth stagnates for more than 30 days, an alarm message is automatically pushed.

[0033] This invention relates to a poplar growth dynamic monitoring system integrating multi-level branching parameter acquisition. By deploying height-adjustable, rotating intelligent sensing rings at different heights on the poplar trunk, and combining multimodal sensing, edge computing, and remote communication technologies, it achieves non-contact, high-precision, and continuous dynamic monitoring of key growth traits of the poplar canopy structure. Its working principle is as follows: Multiple intelligent sensing rings are installed on the trunk of the target poplar tree at preset heights, such as 1.5 m, 3.0 m, and 4.5 m. The initial inner diameter of each sensing ring is slightly larger than the trunk diameter, and the ring tension is monitored in real time by a built-in strain sensor. When the system detects radial expansion of the trunk due to growth, with an average daily growth of ≥0.05 mm, it automatically triggers a micro stepper motor to drive the ring diameter fine-tuning mechanism, expanding outward in steps of 0.1 mm / time to ensure a long-term close fit to the trunk without damaging the cambium, while maintaining the stability of the equipment.

[0034] The system starts the scanning program at set time intervals, such as 2:00 AM every day, avoiding strong light and wind disturbance. Each sensor ring synchronously performs the following actions: first, it completes a 360° rotation along the circumference at an angular velocity of 1–3° / s, during which the RGB-D depth camera continuously acquires local canopy point clouds; Simultaneously, east-west and north-south laser rangefinders record the farthest extension distance of the canopy in real time; an ultrasonic sensor array penetrates the leaf canopy to detect the contour boundaries of obscured branches; and tilt sensors and pressure sensing strips continuously monitor the angle and base stress state of identified branches. After scanning, the edge computing units within each sensing ring process the raw data locally: fusing RGB-D point clouds with ultrasonic contour data to construct a high-density local three-dimensional canopy model; The lightweight PointNet++ neural network model is called to perform semantic segmentation on the point cloud, automatically identify and label first-level, second-level, and third-level branches, and count the number of branches at each level; combined with the measured values ​​of the tilt sensor and the spatial geometric relationship, the angle between each branch and the main trunk or the branch above is calculated with an accuracy of ±2°. Based on the pressure-sensing band deformation, the branch base thickness is retrieved through a pre-calibrated pressure-diameter mapping database, with a measurement range of 5–50 mm and an error ≤ ±0.8 mm. Laser ranging data is filtered and extreme value extracted to output east-west and north-south crown width values ​​with an accuracy of ±1 cm. Extracted structured parameters, including crown width, number of branches, branch angle, and base diameter, are encrypted and uploaded to a cloud management platform via LoRaWAN or NB-IoT modules. Users can remotely access the platform via web or mobile devices to view real-time growth curves and 3D tree shape evolution animations of single or multiple poplar trees, and can issue commands to adjust the scanning frequency, trigger emergency calibration, or remotely restart the equipment.

[0035] The system is powered by a combination of flexible solar thin film and micro wind turbine, coupled with a high-efficiency power management module. Under typical forest lighting conditions, it can achieve energy self-sufficiency with an average daily effective sunshine duration of ≥4 hours. An IP67-rated sealed casing and UV-resistant coating ensure long-term reliable operation in environments ranging from −25℃ to +60℃, high humidity, rainfall ≤50 mm / h, and strong winds ≤8. The platform incorporates a growth model that can perform trend analysis on historical data. When a branching angle mutation >15° / week, no crown growth for 30 consecutive days, or an abnormal absence of branches at a certain level is detected, an automatic early warning mechanism is triggered, indicating potential disease, mechanical damage, or poor plant development, providing a scientific basis for decisions on superior variety selection, precise pruning, or tending.

[0036] The working process of the poplar growth dynamic monitoring system integrating multi-level branch parameter acquisition function of the present invention is divided into six stages: system deployment, adaptive adjustment, periodic scanning, data processing, remote transmission and intelligent application. The specific steps are as follows: Step 1: System Installation and Initial Deployment: Select the target poplar tree for monitoring, and install three liftable and rotating smart sensor rings on its trunk at vertical gradients, such as 1.5 m, 3.0 m, and 4.5 m. Based on the initial diameter of the tree trunk, typically 8–30 cm, manually or automatically adjust the inner diameter of each sensor ring to be slightly larger than the circumference of the tree trunk to ensure that the rings are neither too tight nor too loose; turn on the device power, complete wireless network pairing, LoRaWAN / NB-IoT, time synchronization, and sensor self-test; the edge computing unit loads the pre-trained branch recognition model and establishes a secure communication channel with the cloud platform.

[0037] Step 2: Adaptive Adhesion and Status Maintenance: The system enters standby monitoring mode, and the strain sensor continuously collects ring tension data; when the cumulative radial growth of the trunk is detected to be ≥0.05 mm within 24 consecutive hours, it reflects the activity of the cambium, and the control module starts the fine-tuning program; it drives the micro stepper motor to slowly expand the ring diameter in steps of 0.1 mm / time until the tension is restored to the set safe range; the entire adjustment process is recorded and uploaded to the platform to ensure that the equipment is firmly attached for a long time without damaging the bark.

[0038] Step 3: Periodic Omnidirectional Dynamic Scan: Scanning tasks are triggered according to a preset schedule, such as daily at 02:00 or by remote command; each sensor ring synchronously performs the following actions: Rotational scanning: A 360° horizontal rotation is completed at an angular velocity of 2° / s, and the RGB-D depth camera acquires point clouds at a frequency of 10 Hz, covering a radius of 5 m; Crown width measurement: East-west and north-south laser rangefinders sample 5 times per second to record the distance to the farthest boundary of the canopy; Occlusion penetration detection: The ultrasonic array emits 40 kHz pulses and receives the echo signals of branches blocked by the leaf canopy to construct a supplementary profile; Branch physical sensing: The pressure sensing band monitors the contact force at the base of potential branches in real time, and the tilt sensor records the spatial attitude; a single complete scan cycle takes about 3-5 minutes, after which the device returns to the standby position.

[0039] Step 4: Edge Data Fusion and Parameter Extraction: Scanned data is preprocessed in the local edge computing unit. Denoising and registration of RGB-D point clouds are performed, and then fused with ultrasonic contour data to synthesize a highly complete local 3D model. A lightweight model is used, and PointNet++ is improved to perform semantic segmentation: automatically identifying and classifying first-, second-, and third-level branches, and outputting the number of branches at each level; parameters are calculated by combining multi-sensor information. Branch angle = tilt sensor reading plus spatial coordinate transformation correction, accuracy ±2°; The crown width in all directions is determined by filtering the extreme values ​​of laser ranging through a sliding window, with an accuracy of ±1 cm. Generate a structured JSON data packet containing timestamps, tree IDs, parameter values, and confidence levels.

[0040] Step 5: Remote Data Transmission and Platform Integration: The encrypted data packet is uploaded to the cloud management platform via a low-power wide area network (LoRaWAN / NB-IoT). Upon receiving the data, the platform automatically parses, stores, and updates the digital growth profile of the poplar tree. Users can view the following in real time via Web or mobile app: daily crown width (e.g., 4.21 m east, 4.18 m west, 3.95 m south, 4.02 m north); 8 primary branches, 22 secondary branches, and 47 tertiary branches; average branching angle of 32.5°; and maximum basal diameter of 41.3 mm. The system supports historical data comparison, growth rate curve plotting, and parallel analysis of multiple trees.

[0041] Step Six: Intelligent Early Warning and Breeding Decision Support: The platform has a built-in growth trend analysis engine that models data from more than 30 consecutive days. If the following abnormal situations occur, an early warning will be automatically triggered: a sudden decrease in the number of branches at a certain level >20%; a change in branch angle >15°, possibly due to wind damage or disease; a crown width increase of <1 cm for 30 consecutive days, indicating stagnation of growth. The early warning information will be pushed to the terminals of forest farm managers or breeding experts. The obtained high-precision plant type parameters can be directly input into the poplar molecular design breeding platform for ideal plant types, such as narrow crowns, fewer lateral branches, and large-angle individual selection, supporting the process of improving varieties in national reserve forests.

[0042] The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described herein. Although the present invention has been described in detail with reference to the above embodiments, the present invention is not limited to the specific embodiments described above. Therefore, any modifications or equivalent substitutions to the present invention, as well as all technical solutions and improvements that do not depart from the spirit and scope of the invention, are covered within the scope of the claims of the present invention.

Claims

1. A poplar growth dynamic monitoring system integrating multi-level branching parameter acquisition function, comprising intelligent sensor rings, installed at intervals along the axial direction of the poplar trunk, with an installation height ranging from 1.0 m to 6.0 m, and a vertical spacing of 1.0–2.0 m between adjacent sensor rings; The intelligent sensing ring has a built-in micro stepper motor-driven lifting guide rail and rotation mechanism. The lifting stroke is 0–30 cm, the lifting speed is 0.5–2 cm / s, and the rotation angular velocity is 0.5–5° / s. It supports 1–4 complete 360° scanning cycles per day. The multimodal sensing module, integrated on each sensing ring, is used to simultaneously collect data on east-west and north-south canopy width, number of first- to third-level branches, branch angle, and thickness of branch base; The multimodal sensing module includes: An RGB-D depth camera with a resolution of 1280×720, a depth measurement accuracy of ±2 mm, and an effective detection distance of 0.3–5.0 m; A pair of laser rangefinders are arranged in the east-west direction and the north-south direction, with a range of 0.1–10 m and a repeatability of ±1 mm; An ultrasonic sensor array, operating at a frequency of 40 kHz, has an effective detection depth ≥1.5 m penetrating the leaf canopy. Annular flexible pressure sensing band, measuring range 0–50 N, nonlinear error ≤ ±1.5%FS, used for inverting the diameter of the branch base, measuring range 5–50 mm, accuracy ±0.5 mm; Digital tilt sensor, measuring range ±90°, resolution 0.1°, accuracy ±0.3°; Edge computing units built into each sensing ring are used to process raw sensing data locally and extract structured growth parameters. The wireless communication module supports LoRaWAN or NB-IoT protocols, with a communication distance of ≥3 km and a data upload frequency adjustable from 1 time / 6 h to 1 time / 24 h. The self-powered and environmental protection unit ensures continuous and stable operation of the system under ambient temperature conditions of -25℃ to +60℃, relative humidity ≤95%, and rainfall intensity ≤50 mm / h. A remote monitoring and data management platform is used by users to remotely view, analyze, and control the monitoring process.

2. The poplar growth dynamic monitoring system integrating multi-level branching parameter acquisition function according to claim 1, characterized in that, The intelligent sensing ring is made of carbon fiber reinforced polyamide composite material, with an overall weight of ≤800 g and an adjustable outer diameter range of 15 cm to 40 cm. The outer surface is coated with a UV-resistant polyurethane coating with a bark-like texture and a reflectivity of ≤15%. It also incorporates microencapsulated animal repellent, providing an effective protection radius of ≥30 cm.

3. The poplar growth dynamic monitoring system integrating multi-level branching parameter acquisition function according to claim 1, characterized in that, The intelligent sensing ring is arranged in three layers according to vertical height: the low-level ring is installed at a height of 1.0–2.0 m and is used to monitor the first-level branches; the middle-level ring is installed at a height of 2.5–4.0 m and is used to monitor the second-level branches; the high-level ring is installed at a height of 4.5–6.0 m and is used to monitor the third-level branches, forming a vertical gradient observation network covering the entire canopy.

4. The poplar growth dynamic monitoring system integrating multi-level branching parameter acquisition function according to claim 1, characterized in that, The edge computing unit is equipped with a lightweight artificial intelligence model based on an improved PointNet++ architecture. The model has ≤1.2 MB of parameters, an inference speed of ≥8 FPS on an ARM Cortex-A53 processor, a recall rate of ≥92% for identifying branches of level two and above, and a false detection rate of ≤5%.

5. The poplar growth dynamic monitoring system integrating multi-level branching parameter acquisition function according to claim 1, characterized in that, The branch angle is calculated by combining the measured value of the tilt sensor with the spatial coordinate transformation algorithm, and the comprehensive measurement error is ≤ ±2°; the thickness of the branch base is obtained by matching the real-time deformation of the pressure sensing strip with the pre-calibrated diameter-pressure mapping database, with a calibration sample size of ≥200 sets and a root mean square error (RMSE) of thickness inversion of ≤0.8 mm.

6. The poplar growth dynamic monitoring system integrating multi-level branching parameter acquisition function according to claim 1, characterized in that, The self-powered and environmental protection unit includes a flexible monocrystalline silicon solar thin film with a conversion efficiency of ≥22% and an area of ​​≥0.06 m². 2 The micro vertical axis wind turbine has a starting wind speed of ≤2.5 m / s, an IP67 waterproof and dustproof sealed housing, and a low-power power management module, ensuring continuous operation of the system for ≥180 days without external power supply, with a standby power consumption of ≤15mW.

7. The poplar growth dynamic monitoring system integrating multi-level branching parameter acquisition function according to claim 1, characterized in that, The intelligent sensing ring is equipped with an adaptive ring diameter adjustment mechanism. It monitors the ring tension in real time through strain gauges. When the average daily radial growth of the tree trunk is detected to be ≥0.05 mm, the ring diameter fine adjustment mechanism is automatically triggered. The adjustment step is 0.1 mm / time, and the maximum adjustment range is ±10 mm, ensuring long-term fit without damaging the cambium.

8. The poplar growth dynamic monitoring system integrating multi-level branching parameter acquisition function according to claim 1, characterized in that, The remote monitoring and data management platform provides a web and mobile visualization interface, supports querying historical data by tree number, forest compartment, and time interval, synthesizes crown expansion rate curves, branch development heat maps, and three-dimensional tree type evolution animations, and has an abnormal early warning function: when the branch angle mutation is greater than 15° / week or the crown growth stagnates for more than 30 days, an alarm message is automatically pushed.

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