Forestry seedling raising device and method

Abstract

The invention relates to the technical field of forestry seedling raising, in particular to a forestry seedling raising device and method. An environmental data analysis system; a seedling scheme generation system; a greenhouse environment control system; a nursery stock growth monitoring system; domesticating the regulation and control system; by accurately analyzing the environment of the target land and cultivating the seedlings in a customized mode, the seedlings gradually adapt to the environmental conditions of the target afforestation land in the seedling raising stage, environmental mutation after transplanting is greatly reduced, and the afforestation survival rate is increased.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of forestry seedling raising, in particular to a forestry seedling raising device and method. BACKGROUND

[0004] Traditional seedling raising mainly relies on experience, and the seedling raising parameters lack scientific basis. The same batch of seedlings used for afforestation in different site conditions cannot be universally adapted. Seedling raising units often do not know the final planting site and environmental conditions of the seedlings, and cannot carry out targeted cultivation.

[0005] The seedling raising and afforestation links are disconnected, and there is a lack of detailed environmental information of the target afforestation site. Even if there is some information, it is mostly a rough qualitative description, lacking quantitative and accurate data support, and it is difficult to guide seedling raising practice.

[0006] Traditional seedling raising lacks systematic acclimation before the seedlings leave the nursery. The seedlings are directly transplanted from the greenhouse to the natural environment, and the environmental mutation causes serious transplant shock. The seedlings lack resistance training and are prone to die in harsh environments.

[0007] Seedling raising units rarely track the performance of seedlings after afforestation, and cannot optimize seedling raising technology according to afforestation results. There is a lack of information feedback between seedling raising and afforestation, and successful experience and failure lessons are difficult to accumulate, so the seedling raising technology improves slowly.

[0008] Although the existing technology has made progress in container seedling raising, facility seedling raising, and precise irrigation, it still lacks systematic investigation of the environment of the target afforestation site and customized design of seedling raising parameters, lacks acclimation and adaptation of seedlings to the target environment, and lacks a closed-loop feedback mechanism for the whole process of seedling raising and afforestation. Therefore, a new technical solution is needed to systematically solve these problems. SUMMARY

[0009] To achieve the above-mentioned purpose, the technical solution adopted by the present application is: a forestry seedling raising device, comprising.

[0010] A target site environment perception system is used to collect environmental data of the target afforestation site, including a satellite remote sensing monitoring unit, an unmanned aerial vehicle cruising monitoring unit, and a ground sensor monitoring unit.

[0011] An environmental data analysis system is used to process and analyze multi-source environmental data, and extract soil properties, climate characteristics, water conditions, light conditions, and biological community characteristics of the target site.

[0012] A seedling raising scheme generation system is used to generate a customized seedling raising scheme based on the environmental characteristics of the target site, including tree species selection, environmental parameter setting, and acclimation strategy.

[0013] A greenhouse environment control system for regulating greenhouse environment according to a seedling raising scheme, including temperature, humidity, light and water and fertilizer supply.

[0014] A seedling growth monitoring system for monitoring seedling growth process, recording morphological and physiological indicators.

[0015] An acclimatization control system for gradually adjusting environmental parameters to approach target site conditions in the later stage of seedling raising, improving seedling adaptability.

[0016] A feedback optimization system for tracking afforestation effects and optimizing seedling raising schemes.

[0017] As an embodiment of the present application, the target site environment perception system comprises.

[0018] The satellite remote sensing monitoring unit acquires multispectral remote sensing images with a spatial resolution better than 10 meters, extracts terrain, vegetation and soil information.

[0019] The unmanned aerial vehicle cruising monitoring unit flies at an altitude of 50-150 meters, collects visible light images, multispectral images and thermal infrared images with a resolution of centimeters.

[0020] The ground sensor monitoring unit arranges sensor nodes on the target afforestation site, with a node spacing of 50-100 meters, monitors soil temperature and humidity, soil nutrients, meteorological parameters and light intensity, and the data collection frequency is 15-30 minutes.

[0021] As an embodiment of the present application, the environmental data analysis system adopts a multi-source data fusion algorithm.

[0022] Satellite remote sensing images, unmanned aerial vehicle images and ground sensor data are spatio-temporally registered and standardized in format.

[0023] Weighted fusion methods are used to integrate data from different sources and scales.

[0024] Environmental characteristic parameters are extracted, including soil texture, soil nutrients, annual average temperature, precipitation, evapotranspiration capacity, light radiation and dominant species.

[0025] As an embodiment of the present application, the seedling raising scheme generation system comprises.

[0026] A tree species ecological adaptability database is established, containing temperature adaptation range, water demand, soil requirement and stress resistance characteristics of candidate tree species.

[0027] A comprehensive evaluation method is used to calculate the adaptability index of tree species to the target site environment.

[0028] Seedling temperature, humidity, light and nutrient supply parameters are calculated according to the target site environmental conditions.

[0029] Establish a seedling growth model to predict survival rate and growth performance after afforestation.

[0030] As an embodiment of the present application, the greenhouse environment control system.

[0031] Temperature regulation uses heat pump technology to achieve heating and cooling, with a temperature control accuracy of ±1°C, and implements day-night temperature difference regulation.

[0032] Humidity regulation uses atomization humidification and ventilation dehumidification, with a humidity control accuracy of ±5%RH.

[0033] Light regulation uses LED plant growth lamps for light supplementation and sunshade nets for light shading, with an adjustable light intensity range of 100-1000 micromoles per square meter per second, and a light period that can be set to 8-16 hours.

[0034] Water and fertilizer supply uses a drip irrigation system and water and fertilizer integration technology, with supply adjusted in stages according to the growth stage of the seedlings.

[0035] As an embodiment of the present application, the seedling growth monitoring system.

[0036] Machine vision technology is used to automatically measure seedling height, ground diameter, and leaf area, with a monitoring frequency of 1-2 times per week.

[0037] Chlorophyll meters and photosynthesis meters are used to measure physiological indicators.

[0038] A seedling growth file is established to record the morphological indicators, physiological indicators, and environmental conditions of each seedling.

[0039] Machine learning algorithms are used to analyze growth patterns and predict the time to reach the nursery standard.

[0040] As an embodiment of the present application, the acclimatization control system implements acclimatization treatment in the later growth stage of seedlings, with an acclimatization period of 4-8 weeks before the seedlings are ready for transplanting.

[0041] The greenhouse temperature is gradually adjusted to approach the target ground average temperature, with a decrease of 1-2°C per week.

[0042] Air humidity and irrigation frequency are gradually reduced.

[0043] Drought resistance training is conducted through water control treatment, with irrigation intervals extended to reduce soil moisture to a moderate drought level before irrigation.

[0044] Cold resistance training is conducted through low-temperature treatment.

[0045] Hardening treatment is conducted 1-2 weeks before the seedlings are ready for transplanting, with artificial control measures removed to allow the seedlings to adapt to the natural environment.

[0046] As an embodiment of the present application, the feedback optimization system.

[0047] The survival rate and growth status of seedlings after afforestation are tracked using unmanned aerial vehicles and satellite remote sensing.

[0048] A database of afforestation effects is established to record the correspondence between seedling raising parameters and afforestation effects.

[0049] Data mining techniques are used to analyze key factors affecting survival rate.

[0050] Machine learning algorithms are used to establish an optimization model for seedling raising parameters.

[0051] Based on the feedback of afforestation effects, the seedling raising scheme is continuously optimized.

[0052] As an embodiment of the present application, the tree species adaptability evaluation uses the analytic hierarchy process.

[0053] An evaluation index system including temperature adaptability, water adaptability, soil adaptability, light adaptability, and stress resistance is established.

[0054] According to the main limiting factors of the target site, the weights of each index are assigned.

[0055] The comprehensive adaptability scores of each candidate tree species are calculated.

[0056] The tree species with the highest adaptability score is recommended as the afforestation tree species.

[0057] A forestry seedling raising method, comprising the following steps.

[0058] Step S1: Satellite remote sensing, unmanned aerial vehicles, and ground sensors are used to collect environmental data of the target afforestation site.

[0059] Step S2: Multi-source data are fused and processed to extract soil, climate, water, light, and biological community characteristics.

[0060] Step S3: Tree species adaptability is evaluated, seedling raising environmental parameters are calculated, and a customized seedling raising scheme is generated.

[0061] Step S4: The temperature, humidity, light, and water and fertilizer supply of the greenhouse are regulated according to the seedling raising scheme.

[0062] Step S5: Seedling morphology and physiological indicators are monitored, and a growth file is established.

[0063] Step S6: Environmental acclimation and stress resistance training are implemented in the later stage of seedling raising.

[0064] Step S7: Afforestation effects are tracked and the seedling raising scheme is optimized.

[0065] Beneficial effects: Compared with the prior art, the present application has the following beneficial effects.

[0066] This application achieves this by accurately analyzing the target site environment and customizing seedling cultivation, enabling the seedlings to gradually adapt to the environmental conditions of the target afforestation site during the seedling stage. This significantly reduces environmental changes after transplanting and improves the survival rate of afforestation.

[0067] This application utilizes seedlings cultivated through customized seedling cultivation that rapidly recover and grow after transplanting, with a growth rate 20-30% faster than conventional seedlings, reaching the standard for mature forest 1-2 years earlier, thus accelerating the realization of ecological and economic benefits.

[0068] This application obtains comprehensive, accurate, and quantitative information about the target site environment through multi-source data fusion, providing a scientific basis for setting seedling parameters, reducing setting errors by more than 50%, and avoiding blind seedling cultivation and trial-and-error costs.

[0069] This application achieves precise control of the greenhouse environment, supplying temperature, humidity, light, water, and nutrients as needed, thereby reducing greenhouse energy consumption, increasing water use efficiency, improving fertilizer use efficiency, and lowering seedling costs.

[0070] Through domestication and stress resistance training, the seedlings developed in this application have significantly enhanced drought resistance, cold resistance, and disease resistance. Their survival rate under adverse conditions is 30-50% higher than that of conventional seedlings, and they can adapt to difficult sites such as arid and semi-arid areas, high-altitude mountainous areas, and saline-alkali land. Attached Figure Description

[0071] Figure 1 Overall system architecture diagram of the present invention.

[0072] Figure 2 Schematic diagram of the target location environmental perception system.

[0073] Figure 3 Flowchart of multi-source data fusion processing.

[0074] Figure 4 Seedling program generation decision-making flowchart. Detailed Implementation

[0075] Example 1: As Figure 1 As shown in the figure, this embodiment provides a forestry seedling raising device, including...

[0076] The target site environmental perception system is used to collect environmental data of the target afforestation area, including satellite remote sensing monitoring units, UAV patrol monitoring units, and ground sensor monitoring units.

[0077] An environmental data analysis system is used to process and analyze multi-source environmental data, extracting soil characteristics, climate features, water conditions, light conditions, and biological community characteristics of the target location.

[0078] A seedling raising scheme generation system for generating a customized seedling raising scheme based on target site environmental characteristics, including tree species selection, environmental parameter setting, and acclimatization strategy.

[0079] A greenhouse environment control system for regulating greenhouse environment according to the seedling raising scheme, including temperature, humidity, light, and water and fertilizer supply.

[0080] A seedling growth monitoring system for monitoring seedling growth process and recording morphological and physiological indicators.

[0081] An acclimatization control system for gradually adjusting environmental parameters to approach target site conditions in the later stage of seedling raising, improving seedling adaptability.

[0082] A feedback optimization system for tracking afforestation effect and optimizing seedling raising scheme.

[0083] As an embodiment of the present application, the target site environment perception system comprises.

[0084] The satellite remote sensing monitoring unit acquires multispectral remote sensing images with spatial resolution better than 10 meters, extracts terrain, vegetation, and soil information.

[0085] The unmanned aerial vehicle cruising monitoring unit flies at an altitude of 50-150 meters, collects visible light images, multispectral images, and thermal infrared images with centimeter-level resolution.

[0086] The ground sensor monitoring unit arranges sensor nodes on the target afforestation site, with a node spacing of 50-100 meters, monitors soil temperature and humidity, soil nutrients, meteorological parameters, and light intensity, and the data collection frequency is 15-30 minutes.

[0087] As an embodiment of the present application, the environmental data analysis system adopts a multi-source data fusion algorithm.

[0088] Satellite remote sensing images, unmanned aerial vehicle images, and ground sensor data are spatio-temporally registered and standardized in format.

[0089] Different sources and scales of data are integrated using a weighted fusion method.

[0090] Environmental characteristic parameters such as soil texture, soil nutrients, annual average temperature, precipitation, evapotranspiration capacity, light radiation, and dominant species are extracted.

[0091] As an embodiment of the present application, the seedling raising scheme generation system comprises.

[0092] An ecological adaptability database of tree species is established, containing temperature adaptation range, water requirement, soil requirement, and stress resistance characteristics of candidate tree species.

[0093] An integrated evaluation method is used to calculate the adaptability index of tree species to the target site environment.

[0094] Calculate seedling temperature, humidity, light, and nutrient supply parameters according to target site environmental conditions.

[0095] Establish seedling growth model to predict survival rate and growth performance after afforestation.

[0096] As an embodiment of the present application, the greenhouse environment control system.

[0097] Temperature regulation uses heat pump technology to achieve heating and cooling, with temperature control accuracy of ±1℃, and implements day-night temperature difference regulation.

[0098] Humidity regulation uses atomization humidification and ventilation dehumidification, with humidity control accuracy of ±5%RH.

[0099] Light regulation uses LED plant growth lamps for light supplementation and sunshade net for light shading, with adjustable light intensity range of 100-1000 micromoles per square meter per second, and light period can be set to 8-16 hours.

[0100] Water and fertilizer supply uses drip irrigation system and water and fertilizer integration technology, and adjusts supply amount in stages according to seedling growth stages.

[0101] As an embodiment of the present application, the seedling growth monitoring system.

[0102] Automatically measure seedling height, ground diameter, and leaf area using machine vision technology, with monitoring frequency of 1-2 times per week.

[0103] Measure physiological indicators using chlorophyll meter and photosynthesis meter.

[0104] Establish seedling growth file to record morphological indicators, physiological indicators, and environmental conditions of each seedling.

[0105] Analyze growth rules and predict time to reach nursery standard using machine learning algorithm.

[0106] As an embodiment of the present application, the acclimatization control system implements acclimatization treatment in the later stage of seedling growth, with acclimatization period of 4-8 weeks before nursery.

[0107] Gradually adjust greenhouse temperature to approach average temperature of target site, with decrease of 1-2℃ per week.

[0108] Gradually reduce air humidity and irrigation frequency.

[0109] Perform drought resistance training through water control treatment, and extend irrigation interval to reduce soil water content to moderate drought level before irrigation.

[0110] Perform cold resistance training through low temperature treatment.

[0111] Seedling hardening treatment is carried out 1-2 weeks before nursery, and artificial control measures are removed to adapt seedlings to natural environment.

[0112] As an embodiment of the present application, the feedback optimization system.

[0113] Unmanned aerial vehicles and satellite remote sensing are used to track seedling survival rate and growth conditions after afforestation.

[0114] A database of afforestation effects is established to record the correspondence between seedling parameters and afforestation effects.

[0115] Data mining techniques are used to analyze key factors affecting survival rate.

[0116] Machine learning algorithms are used to establish seedling parameter optimization models.

[0117] Based on the feedback of afforestation effects, the seedling program is continuously optimized.

[0118] As an embodiment of the present application, the tree species adaptability evaluation uses the analytic hierarchy process.

[0119] An evaluation index system including temperature adaptability, water adaptability, soil adaptability, light adaptability and stress resistance is established.

[0120] According to the main limiting factors of the target site, the weights of each index are assigned.

[0121] The comprehensive adaptability scores of each candidate tree species are calculated.

[0122] The tree species with the highest adaptability score is recommended as the afforestation tree species.

[0123] Embodiment 2, as shown in the following. Figure 1 A forestry seedling method includes the following steps.

[0124] Step S1: Satellite remote sensing, unmanned aerial vehicles and ground sensors are used to collect environmental data of the target afforestation site.

[0125] Step S2: Multi-source data fusion processing is carried out to extract soil, climate, water, light and biological community characteristics.

[0126] Step S3: Tree species adaptability is evaluated, seedling environmental parameters are calculated, and customized seedling programs are generated.

[0127] Step S4: According to the seedling program, the temperature, humidity, light and water and fertilizer supply of the greenhouse are regulated.

[0128] Step S5: Seedling morphology and physiological indicators are monitored, and growth records are established.

[0129] Step S6: Environmental acclimation and stress resistance training are carried out in the later stage of seedling.

[0130] Step S7: Track the afforestation effect and feedback the optimized seedling raising scheme.

[0131] Example 3: Customized seedling raising of Pinus tabulaeformis in arid and semi-arid regions

[0132] I. Project Overview: Afforestation site: barren mountain afforestation area in Guyuan City, Ningxia Hui Autonomous Region; afforestation area: 500 hectares; afforestation tree species: Pinus tabulaeformis; seedling quantity: 1 million. Site conditions: loess hilly area, elevation 1800-2200 meters, slope 15-35 degrees, loess soil, annual precipitation 400-450 mm, annual evaporation 1500-1800 mm, arid and semi-arid climate. The traditional Pinus tabulaeformis afforestation survival rate in this area is only 60-70%, the main problem is that the seedlings lack drought resistance, and a large number of seedlings die after transplanting due to water stress.

[0133] II. Environmental perception of target area

[0134] Satellite remote sensing monitoring: Landsat-8 multispectral satellite data and Gaofen-2 satellite data are used. Multi-temporal images are obtained, with growth season from May to September and non-growth season from November to March.

[0135] Extracted information: topographic parameters, slope 15-35 degrees, slope direction mainly sunny and semi-sunny, elevation 1800-2200 meters; vegetation coverage 15-25%, mainly sparse shrubs and herbs; soil type is loess soil; normalized difference vegetation index 0.15-0.30.

[0136] Unmanned aerial vehicle patrol monitoring: multi-rotor unmanned aerial vehicle is used, flight height 100 meters, ground resolution 2.7 cm. Five representative sample areas are selected for detailed monitoring.

[0137] Data acquisition: high-resolution orthophoto, multispectral image, thermal infrared image, digital elevation model.

[0138] Identified information: vegetation type; soil surface water content 5-8%; micro-topographic features.

[0139] Ground sensor monitoring

[0140] Twenty sensor nodes are laid out with a node spacing of about 500 meters to form a grid monitoring network. Each node is equipped with soil temperature and humidity sensors, soil nutrient sensors, small weather stations, and light sensors. The sampling interval is 10 minutes, and the monitoring time is from March 2024 to March 2025.

[0141] Monitoring data: soil: loess soil, organic matter 0.8-1.2%, total nitrogen 0.05-0.08%, pH 7.8-8.2.

[0142] Climate: annual mean temperature 7.2℃, maximum 34.5℃, minimum -24.8℃, annual precipitation 432mm, annual evaporation 1680mm, frost-free period 150 days; moisture: soil moisture content 8-14% in growing season, 12-18% in non-growing season; light: annual total radiation 5800 MJ / m², sunshine duration 2600 hours; biology: sparse vegetation, low species diversity, indicating a harsh environment.

[0143] Overall evaluation: the target site belongs to arid and semi-arid difficult site, the main limiting factors are severe water deficit and soil infertility.

[0144] III. Analysis of environmental data.

[0145] Data preprocessing: satellite images: radiometric calibration, atmospheric correction, geometric correction; UAV images: stitching, registration, orthorectification; ground data: outlier identification, missing value interpolation, noise filtering.

[0146] Multi-source data fusion: pixel-level fusion: fusion of UAV high-resolution images and satellite multispectral images; feature-level fusion: integration of macro, meso, and micro features; decision-level fusion: comprehensive site quality evaluation based on three data sources

[0147] Environmental feature extraction: soil: loamy soil, nutrient-poor, weakly alkaline, moderate water retention capacity; climate: cool climate, severe cold in winter, warm in summer, short frost-free period; moisture: low precipitation, strong evaporation, severe water deficit; light: adequate light, not a limiting factor; biology: sparse vegetation, drought-tolerant species dominant.

[0148] IV. Seedling raising scheme generation.

[0149] Tree species adaptability evaluation: establish evaluation index system (drought tolerance, cold tolerance, soil adaptability, light adaptability, growth characteristics), assign weights according to main limiting factors (drought tolerance 0.40, cold tolerance 0.25, soil adaptability 0.20, light adaptability 0.05, growth characteristics 0.10).

[0150] Candidate tree species score: Pinus tabulaeformis: 8.45 points; Platycladus orientalis: 7.40 points; Pinus sylvestris var. mongolica: 9.20 points.

[0151] Recommendation conclusion: Pinus sylvestris var. mongolica has the highest overall adaptability, but considering local tradition and seedling source, Pinus tabulaeformis is selected, and the drought resistance characteristics of Pinus sylvestris var. mongolica are referred to guide seedling raising.

[0152] Seedling raising parameter calculation.

[0153] Temperature: 18-22℃ during the day, 10-14℃ at night; Humidity: 70-80% during seedling stage, 60-70% during growth stage, 50-60% during acclimation stage; Light: intensity 600-800 μmol / (m²·s), photoperiod 12-14 hours; Water: soil water content 18-22% during seedling stage, 15-18% during growth stage, 12-15% during acclimation stage; Nutrients: N:P:K ratio 2:1:1, total amount 30% lower than conventional seedling raising.

[0154] Growth model prediction: predicted height 25-30 cm, ground diameter 0.4-0.5 cm, root-shoot ratio 0.8 after 8 months. Predicted survival rate 92% after transplanting the customized seedlings for the first year, height growth 18 cm in the second year.

[0155] Seedling raising scheme: tree species: Pinus tabulaeformis Carr.; period: 8 months; temperature, humidity, light, water, and fertilizer control curve: set for three stages of seedling stage, growth stage, and acclimation stage; acclimation scheme: temperature acclimation: start reducing temperature by 1℃ per week in September, humidity acclimation: reduce humidity by 5% per week, water control training: extend irrigation interval for 3 times, seedling hardening treatment: 2 weeks before leaving the nursery.

[0156] V. Greenhouse environment control.

[0157] Greenhouse facility: sunlight greenhouse, span 8 meters, length 50 meters, ridge height 3.5 meters, steel frame structure, covered with PVC transparent film. Equipped with air source heat pump, floor heating pipeline, exhaust fan, sunshade net, ventilation window, atomizing humidifier, LED plant growth lamp, drip irrigation system, and fertilizer tank.

[0158] Temperature control: PID closed-loop control, temperature sampling interval 1 minute, control period 5 minutes. Heating mode starts heat pump and floor heating, closes ventilation window; cooling mode opens ventilation window for natural ventilation or starts exhaust fan for forced ventilation, and unfolds sunshade net. Actual effect: temperature control accuracy ±1℃, day-night temperature difference 8℃.

[0159] Humidity control: humidification mode starts atomizing humidifier, dehumidification mode ventilates to reduce humidity. Humidity sampling interval 5 minutes, cooperates with temperature control to avoid dewing. Actual effect: humidity control accuracy ±5%RH.

[0160] Light control: uses full-spectrum LED light for supplementary lighting. Sunshade net shading rate 50%. Photoperiod is controlled by timer. Actual effect: light intensity 600-800 μmol / (m²·s), good uniformity.

[0161] Water and fertilizer supply: drip irrigation system, dripper flow rate 2 L / h, spacing 20 cm. Irrigation system is determined according to soil moisture sensor data, seedling stage every 2 days, growth period every 3-4 days, acclimation period every 5-7 days. Fertilization uses water-soluble compound fertilizer, concentration 0.1-0.2%, every 10 days, stop fertilizing for the first 2 weeks of acclimation period. Actual effect: soil moisture content control accuracy ± 2%, irrigation uniformity > 90%.

[0162] Six, seedling growth monitoring.

[0163] Morphological monitoring: fixed installation of industrial cameras and depth cameras, automatic shooting every Saturday at 10:00 am. Identify individual seedlings by image segmentation, measure height, ground diameter, and leaf area. Measurement accuracy: height ± 2 mm, ground diameter ± 0.5 mm.

[0164] Physiological monitoring: measure chlorophyll content every two weeks, measure photosynthetic rate every month, destructive sampling of 30 plants every month to determine biomass and root-shoot ratio.

[0165] Growth file: establish an electronic file for each seedling, record number, sowing date, growth data, environmental conditions, management measures, and abnormal events. Data analysis: establish growth curve, analyze influencing factors, and predict time to reach nursery standard.

[0166] Seven, acclimation regulation.

[0167] Environmental gradient acclimation (9-10 months, 8 weeks): temperature acclimation: initial 18-20℃ / 10-12℃, mid-term 16-18℃ / 8-10℃, late 14-16℃ / 6-8℃, last week 12-14℃ / 4-6℃, decrease by about 2℃ per week; humidity acclimation: initial 60-65%, mid-term 55-60%, late 50-55%, decrease by 5% per week; light acclimation: gradually reduce shading, initial shading rate 40%, mid-term 20%, late no shading, photoperiod from 14 hours to 12 hours.

[0168] Stress resistance training: drought resistance training: 3 times of water control training, first time 5 days, second time 6 days, third time 7 days, resume irrigation after mild wilting; cold resistance training: low temperature exercise in mid-October, minimum temperature at night drops to 2-4℃, lasts for 2 weeks; nutrient regulation: stop fertilizing in mid-September to promote stem lignification.

[0169] Seedling training: completely remove artificial control measures, temperature, humidity, and light change naturally, irrigation frequency reduces to every 7-10 days to prevent excessive drought. Actual effect: greenhouse temperature during the day 10-18℃, night 2-8℃, basically consistent with natural temperature.

[0170] Field inspection: seedling height: 27±3 cm; ground diameter: 0.45±0.05 cm; root system: lateral roots developed, effective root system >20; root-shoot ratio: 0.75-0.85; quality pass rate: 98%.

[0171] Eight, afforestation effect tracking.

[0172] Afforestation implementation: afforestation in late March to early April 2023, hole digging 40cm×40cm×40cm, hole spacing 2m×2m, 2500 plants per hectare. Set up control test: test area uses customized seedlings 100 mu, control area uses conventional seedlings 100 mu, similar site conditions, completely the same afforestation method.

[0173] Effect monitoring: unmanned aerial vehicle patrol: once a month in the first year, once a quarter in the second year.

[0174] Ground investigation: 10 fixed sample plots each, 100 sample trees marked in each sample plot, survival rate, seedling height, ground diameter, health status investigated.

[0175] Nine, feedback optimization: data mining: establish afforestation effect database, containing 1000 records (different batches, regions). Use correlation analysis to identify key factors affecting survival rate: domestication temperature control, water control training frequency, root-shoot ratio are the top three factors. Establish survival rate prediction model: survival rate = 0.65 + 0.18×domestication temperature drop + 0.12×water control frequency + 0.25×root-shoot ratio.

[0176] Knowledge update: supplement the adaptability data of Pinus tabulaeformis in arid and semiarid areas to the database, update the optimal seedling raising parameters, optimize the growth model parameters, and improve the prediction accuracy.

Claims

1. A forestry seedling raising device, characterized in that, include.

2. Target site environmental perception system, used to collect environmental data of the target afforestation area, including satellite remote sensing monitoring unit, UAV patrol monitoring unit and ground sensor monitoring unit; An environmental data analysis system is used to process and analyze multi-source environmental data, extracting soil characteristics, climate features, water conditions, light conditions, and biological community characteristics of the target location. The seedling cultivation plan generation system is used to generate customized seedling cultivation plans based on the environmental characteristics of the target site, including tree species selection, environmental parameter setting, and acclimatization strategies. Greenhouse environmental control system is used to regulate the greenhouse environment according to the seedling program, including temperature, humidity, light and water and fertilizer supply; A seedling growth monitoring system is used to monitor the growth process of seedlings and record morphological and physiological indicators. The acclimatization and regulation system is used to gradually adjust environmental parameters in the later stages of seedling cultivation to make them closer to the conditions of the target site, thereby improving the adaptability of seedlings; A feedback optimization system is used to track afforestation results and optimize seedling cultivation plans.

3. The forestry seedling raising device according to claim 1, characterized in that: The target location environment perception system The satellite remote sensing monitoring unit acquires multispectral remote sensing images with a spatial resolution better than 10 meters and extracts topographic, vegetation and soil information. The UAV cruise monitoring unit flies at an altitude of 50-150 meters and collects visible light images, multispectral images and thermal infrared images with centimeter-level resolution. The ground sensor monitoring unit deploys sensor nodes in the target afforestation area, with a node spacing of 50-100 meters, to monitor soil temperature and humidity, soil nutrients, meteorological parameters and light intensity, with a data acquisition frequency of once every 15-30 minutes.

4. The forestry seedling raising device according to claim 1, characterized in that: The environmental data analysis system employs a multi-source data fusion algorithm. Spatiotemporal registration and format standardization of satellite remote sensing imagery, UAV imagery, and ground sensor data; A weighted fusion method is used to integrate data from different sources and scales; Environmental characteristic parameters were extracted, including soil texture, soil nutrients, average annual temperature, precipitation, evapotranspiration capacity, solar radiation, and dominant species.

5. The forestry seedling raising device according to claim 1, characterized in that: The seedling cultivation plan generation system, Establish a database of tree species ecological adaptability, including the temperature adaptation range, water requirements, soil requirements, and stress resistance characteristics of candidate tree species; A comprehensive evaluation method was used to calculate the adaptability index of tree species to the target environment; Calculate the seedling temperature, humidity, light, and nutrient supply parameters based on the environmental conditions of the target location; Establish a seedling growth model to predict the survival rate and growth performance after afforestation.

6. The forestry seedling raising device according to claim 1, characterized in that: In the greenhouse environment control system Temperature control uses heat pump technology to achieve heating and cooling, with a temperature control accuracy of ±1℃, and implements day and night temperature difference control. Humidity control employs atomized humidification and ventilation dehumidification, with a humidity control accuracy of ±5%RH. Light control uses LED plant growth lights for supplemental lighting and shade nets for shading. The light intensity is adjustable from 100 to 1000 micromoles per square meter per second, and the light cycle can be set from 8 to 16 hours. Water and fertilizer are supplied using a drip irrigation system and integrated water and fertilizer technology, with the supply amount adjusted in stages according to the growth stage of the seedlings.

7. The forestry seedling raising device according to claim 1, characterized in that: The seedling growth monitoring system Machine vision technology is used to automatically measure seedling height, ground diameter, and leaf area, with a monitoring frequency of 1-2 times per week. Physiological indicators were measured using a chlorophyll meter and a photosynthesis meter. Establish seedling growth records to record the morphological indicators, physiological indicators, and environmental conditions of each seedling; Machine learning algorithms are used to analyze growth patterns and predict the time when the plants will reach the standard for sale.

8. The forestry seedling raising device according to claim 1, characterized in that: The acclimatization and regulation system implements acclimatization treatment in the later stages of seedling growth, with the acclimatization period lasting 4-8 weeks before transplanting. Gradually adjust the greenhouse temperature to bring it closer to the average temperature of the target area, decreasing it by 1-2℃ each week; Gradually reduce air humidity and irrigation frequency; Drought resistance training is conducted through water control, and irrigation intervals are extended until the soil moisture content drops to a moderately dry level before irrigation. Cold-resistance training is conducted through low-temperature treatment; Hardening-off treatment should be carried out 1-2 weeks before the seedlings are removed to allow them to adapt to the natural environment.

9. The forestry seedling raising device according to claim 1, characterized in that: The feedback optimization system, Drones and satellite remote sensing were used to track the survival rate and growth status of seedlings after afforestation. Establish a database of afforestation results to record the correspondence between seedling parameters and afforestation results; Data mining techniques were used to analyze the key factors affecting survival rate; A seedling parameter optimization model was established using machine learning algorithms; The seedling cultivation plan is continuously optimized based on feedback on afforestation results.

10. The forestry seedling raising device according to claim 4, characterized in that: The tree species adaptability assessment was conducted using the analytic hierarchy process (AHP). Establish an evaluation index system that includes temperature adaptability, water adaptability, soil adaptability, light adaptability, and stress resistance; Weights of each indicator are assigned based on the main limiting factors of the target location; Calculate the overall adaptability score for each candidate tree species; The tree species with the highest adaptability score are recommended as afforestation species.

11. A forestry seedling cultivation method, applied to the seedling cultivation system of claim 1, characterized in that, It includes the following steps.

12. Step S1: Collect environmental data of the target afforestation site using satellite remote sensing, drones, and ground sensors; Step S2: Perform fusion processing on multi-source data to extract soil, climate, water, light and biological community characteristics; Step S3: Assess tree species adaptability, calculate seedling environment parameters, and generate customized seedling plans; Step S4: Adjust the temperature, humidity, light, and water and fertilizer supply in the greenhouse according to the seedling cultivation plan; Step S5: Monitor seedling morphology and physiological indicators, and establish growth records; Step S6: Implement environmental acclimatization and stress resistance training in the later stages of seedling cultivation; Step S7: Track the afforestation effect and provide feedback to optimize the seedling cultivation plan.

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