Self-adaptive supplementary lighting crop inspection robot and method

Abstract

The crop inspection robot comprises a trolley carrying plate, hubs are installed at the four corners of the bottom of the trolley carrying plate at equal intervals, a metal shell is installed at the top of the trolley carrying plate, and side covers are fixedly connected to the left side and the right side of the metal shell through a plurality of fixing bolts; a supporting platform is connected to the top of the metal shell through bolts, and supporting columns are installed on the upper surface of the supporting platform. According to the crop inspection robot and method for self-adaptive supplementary lighting, precise dynamic supplementary lighting adjustment of 50-1000 lux is achieved through the environment light intensity-PID double-closed-loop control technology; a multi-mode sensing unit composed of a five-waveband spectrum camera (400-1000nm) and a meteorological sensor array is arranged; the high-precision navigation system adopting dual-laser radar SLAM positioning and combining with an Ackerman steering chassis can be widely applied to precise agricultural scenes such as crop growth monitoring, stress diagnosis and yield prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural monitoring equipment, and in particular to a crop inspection robot and method with adaptive supplementary lighting. Background Art

[0002] In modern agricultural production, the development of precision agriculture is crucial to improving crop yield and quality. Spectral imaging technology is widely used to monitor crop growth status, but in greenhouse environments, changes in light conditions, humidity, and temperature pose challenges to traditional equipment. In modern facility agricultural production, precise monitoring of greenhouse crop growth status faces the following key challenges.

[0003] The lighting environment in a greenhouse has significant dynamic characteristics, and its light intensity fluctuates within three orders of magnitude (10^2-10^5 lux). The main influencing factors include: (1) natural light fluctuations: affected by weather conditions, the instantaneous light intensity can vary by ±15,000 lux; (2) facility structure influence: the attenuation of greenhouse film transmittance and the shading effect of plant canopy can cause local light attenuation of 40% to 60%; (3) environmental coupling effect: when the relative humidity is >80%, it causes light path scattering, and the temperature gradient causes the sensor response to drift by about ±5%.

[0004] When using traditional monitoring systems, the system adopts a fixed gain mode, which makes it difficult to adapt to the wide dynamic range lighting conditions of 10^2-10^5 lux. It lacks a real-time environmental feedback mechanism (lack of closed-loop control capabilities to dynamically perceive ambient light and automatically adjust), and lacks effective compensation for the temperature and humidity coupling effect (light path scattering when humidity > 80%). The single-point fixed acquisition method cannot reflect the vertical gradient distribution characteristics of environmental parameters, the spatial coverage is seriously insufficient, the sampling frequency is low (typical value ≥ 1 day / time), it is difficult to capture the dynamic changes of environmental parameters, the spatial resolution is limited, and it is impossible to build an accurate three-dimensional environmental field model. In addition, the traditional agricultural inspection model relies entirely on manual operation and cannot achieve continuous monitoring. At the same time, there is a subjective judgment bias in manual observation, resulting in insufficient detection accuracy. A single inspection operation takes up to 4-6 hours, resulting in high labor intensity and a lack of unified environmental parameter quantification standards.

[0005] Therefore, we propose a crop inspection robot and method with adaptive supplementary lighting to solve the above problems. Summary of the Invention

[0006] The purpose of the present invention is to provide a crop inspection robot and method with adaptive supplementary lighting, so as to solve the problems proposed in the above background technology, namely, that the traditional monitoring system supplementary lighting is in a fixed gain mode, is difficult to adapt to wide dynamic lighting, lacks an environmental feedback mechanism and temperature and humidity coupling compensation means, has a single-point fixed acquisition method, a low sampling frequency, poor spatial coverage and resolution, and agricultural inspection relies on manual labor, with subjective bias, low precision, long time consumption, high labor intensity and no unified quantitative standard.

[0007] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: a crop inspection robot with adaptive supplementary lighting, comprising a trolley carrier plate, wheel hubs are installed at equal intervals at the four corners of the bottom of the trolley carrier plate, and a metal shell is installed on the top of the trolley carrier plate, and the left and right sides of the metal shell are fixedly connected to side covers by multiple fixing bolts; it also includes: the top of the metal shell is bolted to a support platform, and the upper surface of the support platform is installed with a support column, the top of the support column is rotatably connected to a spectral camera and a fill light respectively through a main rotating shaft and a side rotating shaft, a bracket is fixed to the upper surface of the trolley carrier plate, and a sixteen-line laser radar is installed at the front end of the top of the bracket, a motor is installed at the lower part of the inside of the metal shell, and the output end of the motor is fixedly connected to a telescopic rod, a fixing clamp is provided on the top of the telescopic rod, and a meteorological multi-element shutter box is installed on the top of the fixing clamp.

[0008] Preferably, an Ackerman steering shaft is fixedly connected between every two wheel hubs, and the wheel hubs are rotatably connected to the bottom of the trolley cargo plate through the Ackerman steering shaft, and side window panels are bolted to the left and right sides of the bracket on the trolley cargo plate.

[0009] Preferably, width indicator lights are installed at equal intervals above the front and rear surfaces of the trolley loading plate, and a display screen and a power switch are respectively installed at the rear of the trolley loading plate.

[0010] Preferably, stop bars are fixedly connected to the lower sides of both the front and rear surfaces of the trolley loading plate, and both ends of the stop bars are in arc shape.

[0011] Preferably, a control panel and a power socket are respectively provided on one side of the power switch at the rear of the trolley loading plate, and a metal sheet for contact charging is installed below the power socket at the rear of the trolley loading plate.

[0012] Preferably, an industrial computer and a sixteen-line small laser radar are installed at the front end of the upper surface of the trolley loading board, and the left side bolts on the upper surface of the trolley loading board are connected to a switch, and the bottom bolts of the trolley loading board are connected to a battery.

[0013] Preferably, a relay is fixed to the bottom of the inner wall of the metal shell, a gyroscope is bolted to the right side of the upper surface of the trolley loading plate, and a TBOX box and a 4G wireless router are bolted to the rear end of the upper surface of the trolley loading plate.

[0014] The method for using the crop inspection robot with adaptive supplementary lighting includes the following steps: S1, system startup, the robot uses a sixteen-line laser radar and a sixteen-line small laser radar and SLAM technology to scan the greenhouse, build a 3D point cloud map, optimize the inspection path with a multi-target path planning algorithm, and set the site location; S2, the robot inspects according to the planned path, and after arriving at the site, the telescopic rod is adjusted to a preset height, and the meteorological multi-factor shutter sensor collects environmental data; S3, the adaptive fill light module uses PWM and PID algorithms to adjust the brightness of the fill light based on the ambient light sensor data, helping the spectral camera to obtain high-quality images; S4, after data collection at each site is completed, the robot uploads the spectral image to the cloud via FTP, and uses TCP / IP to transmit environmental parameters in real time; S5, the cloud uses a distributed database to store and manage data, and forms a fusion feature vector through multi-source data fusion, which is input into a random forest model to predict crop growth status; S6, the system mines crop growth patterns based on historical data, optimizes the prediction model, generates a growth status report, and provides decision support for farmers.

[0015] Preferably, the multispectral camera and meteorological multi-factor shutter box respectively collect multi-band spectral images of plants and environmental parameters (such as temperature and humidity, light intensity, CO2 concentration, etc.), based on the reflectivity model: R(H, T, I) = R0·ea-(H-Ho)2.eB-(T-To)2.ey.(I-In)2.

[0016] Preferably, the fill light adopts PWM dimming and PID closed-loop control algorithm to dynamically adjust the LED brightness:

[0017]

[0018] , ensuring that the spectral camera obtains high-quality images under different environmental conditions, and all data are uploaded to the cloud server through the TCP protocol for big data analysis and modeling to provide decision support for precision agriculture.

[0019] Compared with the prior art, the present invention has the following beneficial effects: the crop inspection robot and method with adaptive supplementary lighting adopt a new structural design, the specific contents of which are as follows:

[0020] (1) Based on the multi-physical quantity coupling feedback mechanism, the PWM-PID closed-loop control architecture of the adaptive fill light system is integrated to realize dynamic spectral compensation under the synergistic effect of multiple parameters such as light intensity, ambient temperature and air humidity. Through synchronous acquisition technology, the spatiotemporal alignment of multispectral imaging data and environmental parameters is completed, and a crop phenotypic characteristic database with spatiotemporal consistency is constructed, providing high-quality multi-dimensional original data sets for precision agricultural analysis.

[0021] (2) The design of an integrated device for adaptive fill light and spectral acquisition uses a horizontal coaxial integrated fill light and a multispectral camera, and achieves brightness adjustment through PWM dimming and PID control; the integrated meteorological louver collects environmental parameters in real time and performs dynamic compensation based on the reflectivity model, effectively eliminating light scattering in high humidity environments; the device is equipped with an adjustable telescopic rod to adapt to the monitoring needs of crops at different growth stages, and has strong adaptability, which improves the quality of spectral data and monitoring reliability.

[0022] (3) It adopts the Ackerman steering chassis architecture, integrates the hub motor servo drive system, multi-line laser radar SLAM three-dimensional modeling module and automatic charging device to realize the construction of 3D environmental point cloud map and autonomous energy replenishment; through the multi-target path planning algorithm, the system has the ability to autonomously navigate under complex working conditions of facility agriculture, can perceive environmental obstacles in real time and complete autonomous obstacle avoidance; this design improves the positioning accuracy and operating stability in confined spaces, and enhances the continuous operation capability of greenhouse environments; combined with intelligent charging management, it supports all-weather inspections and provides a reliable mobile platform for agricultural monitoring.

[0023] (4) Through the multi-dimensional fusion of spectral and environmental data and the combination of physical models, the phenotypic characteristics of crops are analyzed to provide a scientific basis for precision agriculture decision-making.

[0024] (5) An intelligent path planning algorithm is used to achieve dynamic optimization of the inspection path, and an automatic charging device is configured to support the continuous operation of the equipment. At the same time, a remote monitoring platform is built based on a wireless communication network to achieve real-time monitoring of the equipment's operating status and synchronous transmission of operation data, thereby improving the intelligent management level of automated operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the three-dimensional structure of the present invention;

[0026] Figure 2 This is a schematic diagram of the connection structure between the meteorological multi-factor shutter box and the fixing clip of the present invention;

[0027] Figure 3 This is a schematic diagram of the connection structure between the telescopic rod and the motor of the present invention;

[0028] Figure 4 This is a schematic diagram of the three-dimensional structure of the trolley's cargo plate, wheel hub and stop bar of the present invention;

[0029] Figure 5 This is a schematic diagram of the connection structure between the battery and the vehicle loading plate of the present invention;

[0030] Figure 6 This is a schematic diagram of the structure in which the side cover and the metal shell are separated according to the present invention;

[0031] Figure 7 This is a schematic diagram of the connection structure of the spectral camera, fill light and side shaft of the present invention;

[0032] Figure 8 This is a flow chart of the method for using the present invention;

[0033] Figure 9 Graphs showing the parameters in the reflectivity model of the present invention at different wavelengths.

[0034] In the figure: 1. Spectral camera; 2. Main rotating shaft; 3. Side rotating shaft; 4. Fill light; 5. Support column; 6. Support platform; 7. Metal casing; 8. 16-line laser radar; 9. Industrial computer; 10. 16-line small laser radar; 11. Switch; 12. Side window panel; 13. Side cover; 14. Fixing bolt; 15. Meteorological multi-factor shutter box; 16. Fixing clamp; 17. Telescopic rod; 18. Motor; 19. Relay; 20. TBOX box; 21. 4G wireless router; 22. Gyroscope; 23. Car loading board; 24. Width indicator light; 25. Display screen; 26. Power switch; 27. Control panel; 28. Power socket; 29. ​​Metal sheet for contact charging; 30. Stop bar; 31. Wheel hub; 32. Ackerman steering shaft; 33. Battery. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0036] See also Figures 1-9 , the present invention provides the following technical solutions: a crop inspection robot and method with adaptive supplementary lighting;

[0037] Example 1: In order to solve the problems in the prior art that the traditional monitoring system has a fixed gain mode for supplementary light, is difficult to adapt to wide dynamic lighting, lacks an environmental feedback mechanism and temperature and humidity coupling compensation means, has a single fixed acquisition method, a low sampling frequency, poor spatial coverage and resolution, and relies on manual labor for agricultural inspections, which has subjective bias, low accuracy, long time consumption, high labor intensity and no unified quantitative standard, the following solution is disclosed. For specific reference Figure 1-Figure 7As shown, it includes a trolley loading plate 23, wheel hubs 31 are installed at equal intervals at the four corners of the bottom of the trolley loading plate 23, an Ackerman steering shaft 32 is fixedly connected between every two wheel hubs 31, and the wheel hubs 31 are rotatably connected to the bottom of the trolley loading plate 23 through the Ackerman steering shaft 32, the left and right sides of the upper bracket of the trolley loading plate 23 are bolted to the side window panels 12, and a metal shell 7 is installed on the top of the trolley loading plate 23, and the left and right sides of the metal shell 7 are fixedly connected to the side cover 13 through multiple fixing bolts 14; it also includes: a metal The top of the metal shell 7 is bolted to the support platform 6, and the upper surface of the support platform 6 is installed with a support column 5. The top of the support column 5 is rotatably connected to the spectral camera 1 and the fill light 4 through the main shaft 2 and the side shaft 3. The upper surface of the trolley loading plate 23 is fixed with a bracket, and the front end of the top of the bracket is installed with a sixteen-line laser radar 8. The motor 18 is installed at the bottom of the metal shell 7, and the output end of the motor 18 is fixedly connected to the telescopic rod 17. The top of the telescopic rod 17 is provided with a fixing clamp 16, and the fixing clamp 16 is fixed to the top of the telescopic rod 17. A meteorological multi-element shutter box 15 is installed on the top, width lights 24 are installed at equal intervals above the front and rear sides of the trolley loading board 23, a display screen 25 and a power switch 26 are installed behind the trolley loading board 23, and a stop bar 30 is fixedly connected to the bottom of the front and rear sides of the trolley loading board 23, and the two ends of the stop bar 30 are arc-shaped. A control panel 27 and a power socket 28 are respectively provided on one side of the power switch 26 behind the trolley loading board 23, and a power socket 28 is installed below the power socket 28 behind the trolley loading board 23. There is a metal sheet 29 for contact charging, an industrial computer 9 and a sixteen-line small laser radar 10 are respectively installed on the front end of the upper surface of the trolley loading plate 23, and the left side of the upper surface of the trolley loading plate 23 is bolted to the switch 11, the bottom of the trolley loading plate 23 is bolted to the battery 33, a relay 19 is fixed to the bottom of the inner wall of the metal shell 7, a gyroscope 22 is bolted on the right side of the upper surface of the trolley loading plate 23, and a TBOX box 20 and a 4G wireless router 21 are respectively bolted to the rear end of the upper surface of the trolley loading plate 23.

[0038] The robot is made of lightweight materials and equipped with safety protection devices. The telescopic rod 17 enables the meteorological multi-factor shutter box 15 to move up and down to adapt to data collection needs at different heights. The 16-line laser radar 8 is used to build a 3D map to assist navigation and path planning. At the same time, the multi-spectral camera 1 with all-round multi-angle acquisition can capture spectral images of plant growth status in all directions, support multiple band selections, and is suitable for different crop monitoring needs. The meteorological multi-factor shutter box 15 is also used to integrate multiple sensors such as temperature, humidity, and light intensity to monitor meteorological parameters in the agricultural production environment. The detected ambient light intensity and temperature and humidity are used as the basis for adjusting the brightness of the fill light 4, and are integrated in the spectral camera 1. It uses high-brightness LED lights, which can dynamically adjust the brightness according to the ambient light, temperature and humidity conditions to ensure the best shooting effect. The robot uses a lightweight, modular wheeled chassis, equipped with a hub motor drive and an Ackerman steering system, with stable motion performance, integrated emergency stop switch and power-on switch, adapting to the complex environment of the greenhouse, and carrying multiple sensors (spectral camera 1, ambient light sensor, temperature and humidity sensor, etc.), real-time collection and storage of data, upload to the server through TCP protocol and FTP protocol, support remote monitoring and analysis, and cooperate with the built-in industrial computer 9 to support platform command control, combined with the sixteen-line laser radar 8 and the sixteen-line small laser radar 10 to achieve three-dimensional mapping and autonomous navigation, and complete inspection tasks according to preset routes or instructions.

[0039] Example 2: Different from Example 1, this example innovatively integrates an autonomous navigation robot platform, a multi-spectral fusion sensor system, and adaptive fill light technology to build a fully automatic intelligent monitoring solution suitable for closed greenhouse environments. Figure 8As shown, S1. After the system is started, the robot uses the sixteen-line laser radar 8 and the sixteen-line small laser radar 10 combined with SLAM (simultaneous localization and mapping) technology to perform high-precision scanning of the greenhouse environment, build a 3D point cloud map in real time, and optimize the inspection path based on the multi-target path planning algorithm (Multi-Goal Path Planning), and set the location information of the inspection site and the automatic charging site; S2. The robot performs the inspection task according to the planned path. After arriving at each site, it automatically adjusts to the preset height through the telescopic rod 17. The meteorological multi-factor shutter box 15 integrates sensors to synchronously collect multi-dimensional environmental data such as CO2 concentration, temperature and humidity, atmospheric pressure and light intensity; S3. At the same time, the adaptive fill light module uses the ambient light sensor to feedback data in real time, and combines PWM (pulse width modulation) and PID (proportional-integral-differential) closed-loop control algorithms to dynamically adjust the brightness of the fill light 4 to ensure that the spectral camera 1 obtains high-quality multi-band spectral images under different lighting conditions; S4. After the data collection task of each site is completed, the robot will spectrum Image data is uploaded to the cloud server via FTP (File Transfer Protocol), and environmental parameter data is transmitted in real time via TCP / IP protocol to ensure data efficiency and integrity; S5, the cloud server uses a distributed database (such as Hadoop) to store and manage the uploaded spectral images and environmental parameters, and combines the spectral feature vector (Fspectral) with the environmental parameter feature vector (Fenvironment) through multi-source data fusion technology to form a fused feature vector (Xfused = [Fspectral; Fenvironment]), which is input into the random forest (RandomForest) model to predict crop growth status and output classification results of plant status, disease, etc.; based on historical data, the system further explores crop growth patterns through cluster analysis, principal component analysis and machine learning algorithms, and uses cross-validation and hyperparameter tuning techniques to optimize the prediction model, and finally generates a crop growth status report to provide farmers with accurate plant status and pest and disease control decision support.

[0040] Example 3: Different from Example 1, this example can optimize the fill light in real time, ensuring that the spectral camera 1 obtains high-quality and highly consistent multi-band spectral images, and improves data accuracy and reliability. For details, refer to Figure 9 As shown, the multispectral camera 1 and the meteorological multi-factor shutter box 15 respectively collect multi-band spectral images of plants and environmental parameters (such as temperature and humidity, light intensity, CO2 concentration, etc.). Based on the reflectivity model: R(H, T, I) = R0·ea-(H-Ho)2.eB-(T-To)2.ey.(I-In)2, the reflectivity model at different wavelengths is:

[0041] R(H, T, I, λ) = R0(λ)·e-α(λ)·(H-Ho)²·e-β(λ)·(T-To)²·e-γ(λ)·(I-In)², which corrects for ambient light. The fill light module uses PWM dimming and PID closed-loop control algorithms to dynamically adjust the LED brightness:

[0042]

[0043] Ensure that the spectral camera obtains high-quality images under different environmental conditions; all data are uploaded to the cloud server via the TCP protocol for big data analysis and modeling, providing decision support for precision agriculture.

[0044] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A crop inspection robot with adaptive supplementary lighting, comprising a trolley loading plate (23), wherein wheels (31) are installed at equal intervals at the four corners of the bottom of the trolley loading plate (23), and a metal shell (7) is installed on the top of the trolley loading plate (23), and the left and right sides of the metal shell (7) are fixedly connected to side covers (13) through multiple fixing bolts (14); characterized in that: Also includes: The top of the metal shell (7) is bolted to a support platform (6), and a support column (5) is installed on the upper surface of the support platform (6). The top of the support column (5) is rotatably connected to a spectral camera (1) and a fill light (4) through a main rotating shaft (2) and a side rotating shaft (3). A bracket is fixed to the upper surface of the trolley loading plate (23), and a sixteen-line laser radar (8) is installed at the front end of the top of the bracket. A motor (18) is installed below the inside of the metal shell (7), and the output end of the motor (18) is fixedly connected to a telescopic rod (17). A fixing clamp (16) is provided on the top of the telescopic rod (17), and a meteorological multi-element shutter box (15) is installed on the top of the fixing clamp (16).

2. The crop inspection robot with adaptive supplementary lighting according to claim 1, characterized in that: An Ackerman steering shaft (32) is fixedly connected between each two wheel hubs (31), and the wheel hubs (31) are rotatably connected to the bottom of the trolley loading plate (23) through the Ackerman steering shaft (32), and the left and right sides of the upper bracket of the trolley loading plate (23) are both bolted to side window panels (12).

3. The crop inspection robot with adaptive supplementary lighting according to claim 1, characterized in that: Width indicator lights (24) are installed at equal intervals above the front and rear surfaces of the trolley loading plate (23), and a display screen (25) and a power switch (26) are installed behind the trolley loading plate (23).

4. The crop inspection robot with adaptive supplementary lighting according to claim 1, characterized in that: Stop bars (30) are fixedly connected to the lower sides of the front and rear surfaces of the trolley loading plate (23), and both ends of the stop bar (30) are in arc shape.

5. The crop inspection robot with adaptive supplementary lighting according to claim 1, characterized in that: A control panel (27) and a power socket (28) are respectively provided on one side of the power switch (26) behind the trolley loading plate (23), and a metal sheet (29) for contact charging is installed below the power socket (28) behind the trolley loading plate (23).

6. The crop inspection robot with adaptive supplementary lighting according to claim 1, characterized in that: An industrial computer (9) and a sixteen-line small laser radar (10) are respectively installed at the front end of the upper surface of the trolley loading plate (23), and a switch (11) is connected to the left side of the upper surface of the trolley loading plate (23) by bolts, and a battery (33) is connected to the bottom of the trolley loading plate (23) by bolts.

7. The crop inspection robot with adaptive supplementary lighting according to claim 1, characterized in that: A relay (19) is fixed to the bottom of the inner wall of the metal shell (7), a gyroscope (22) is bolted to the right side of the upper surface of the trolley loading plate (23), and a TBOX box (20) and a 4G wireless router (21) are bolted to the rear end of the upper surface of the trolley loading plate (23).

8. The crop inspection robot with adaptive supplementary lighting according to claim 1, characterized in that: A method for using a crop inspection robot with adaptive supplementary lighting according to any one of claims 1 to 7, the method comprising the following steps: S1, system startup, the robot uses a 16-line laser radar (8) and a 16-line small laser radar (10) and SLAM technology to scan the greenhouse, construct a 3D point cloud map, optimize the inspection path with a multi-target path planning algorithm, and set the site location; S2, the robot inspects along the planned path, and after arriving at the site, the telescopic rod (17) is adjusted to a preset height, and the meteorological multi-factor shutter box (15) sensor collects environmental data; S3, the adaptive supplementary light module uses PWM and PID algorithms to adjust the brightness of the supplementary light based on the ambient light sensor data, helping the spectral camera to obtain high-quality images; S4, after data collection at each site is completed, the robot uploads the spectral image to the cloud via FTP, and uses TCP / IP to transmit environmental parameters in real time; S5, the cloud uses a distributed database to store and manage data, and forms a fusion feature vector through multi-source data fusion, which is input into a random forest model to predict crop growth status; S6, the system mines crop growth patterns based on historical data, optimizes the prediction model, generates a growth status report, and provides decision support for farmers.

9. The method for using the crop inspection robot with adaptive supplementary lighting according to claim 8, characterized in that: The multispectral camera (1) and the meteorological multi-element shutter box (15) respectively collect multi-band spectral images of plants and environmental parameters (such as temperature and humidity, light intensity, CO2 concentration, etc.) based on the reflectivity model: R(H, T, I) = R0·ea-(H-Ho)2.eB-(T-To)2.ey.(I-In)2.

10. The method for using the crop inspection robot with adaptive supplementary lighting according to claim 8, characterized in that: The fill light (4) uses PWM dimming and PID closed-loop control algorithms to dynamically adjust the LED brightness: ， Ensure that the spectral camera (1) obtains high-quality images under different environmental conditions, and all data are uploaded to the cloud server through the TCP protocol for big data analysis and modeling to provide decision support for precision agriculture.

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