A method for hydroponic lettuce management and yield prediction

Abstract

The application discloses a kind of water culture management and yield prediction method of lettuce, including seedling, strong seedling transplanting, environmental factor management, lettuce yield prediction and the like steps, seedling is raised by self-made seedling liquid, and cultivation nutrient solution is used to cultivate lettuce growth, and the factors such as temperature and humidity, illumination, carbon dioxide concentration of environmental air are monitored and coupled control simultaneously.The water culture management method of the application is simple and easy to operate, and the growth and physiology of the crop root system and stem leaf are significantly improved after lettuce planting, the chlorophyll and nitrogen content of the leaf are increased, and the yield of the lettuce is improved.The yield prediction model of the application can predict the yield condition according to the growth monitoring, and is especially suitable for intelligent lettuce cultivation, and can have certain comprehensive effect on other water culture leaf vegetables.

Description

Technical Field

[0001] This invention belongs to the fields of agricultural planting, facility horticulture cultivation, and smart agriculture, and specifically relates to a method for the management and yield prediction of hydroponic lettuce. Background Technology

[0002] With the development of science and technology in facility agriculture in recent years, the domestic and international facility vegetable industry is gradually moving towards mechanization, digitalization, and intelligentization. my country has a huge market demand for facility vegetables, and the facility vegetable industry has experienced rapid development in recent years. China's facility vegetable cultivation has entered a stage of rapid development. With increasing awareness of ecological environment and health, the development of the vegetable industry is receiving more and more attention. Currently, domestic facility vegetable cultivation has shifted from simply increasing production to diversification and high quality, with planting area expanding year by year and technology continuously improving. At the same time, some emerging business models are gradually being introduced, bringing more development opportunities to the facility vegetable industry. The production and management process of facility lettuce mainly includes four stages: precision sowing, factory seedling cultivation, field management, and harvesting and transportation. Currently, the "Internet of Things + Agriculture" model has been vigorously promoted, greatly improving agricultural productivity. Currently, various sensors are also used to monitor and control environmental factors required by crops in real time, such as using PLC controllers and HMI human-machine interfaces to achieve precise control of integrated water and fertilizer management and lettuce harvesting.

[0003] Currently, excessive nitrogen input in greenhouse vegetable systems leads to a series of problems, including soil salinization and acidification, and increased greenhouse gas emissions. This is mainly due to excessive annual nitrogen input from chemical fertilizers, organic fertilizers, and irrigation water, resulting in nitrogen surplus and nitrate nitrogen residue in the soil far exceeding the crop's needs, while also severely polluting groundwater in greenhouse vegetable production areas. Nitrogen has a significant impact on the growth, yield, and nutritional quality of leafy vegetables, especially lettuce. To a certain extent, lettuce yield increases with increasing nitrogen levels. However, after exceeding a certain peak, yield declines, and quality is greatly affected, with noticeably high nitrogen content in the leaves. Currently, shortening the lettuce growing process can bring significant yields, but the increased number of production cycles also leads to excessive nitrogen input. While hydroponics can significantly reduce soil problems, the preparation of hydroponic solutions makes it difficult to maintain nitrogen in its original form. Lettuce requires more ammonium nitrogen, but other environmental factors such as light, carbon dioxide concentration, temperature, and humidity also greatly influence nitrogen levels in hydroponic solutions, significantly impacting leaf photosynthesis, especially chlorophyll formation. Therefore, hydroponically grown lettuce requires the addition of appropriate amounts of fertilizer and nutrients to the nutrient solution, while maintaining suitable environmental factors such as temperature, humidity, light, and air carbon dioxide concentration. Researchers both domestically and internationally have shown that different nutrient ratios in hydroponic solutions can significantly impact growth rate, growth cycle, and yield. Summary of the Invention

[0004] Purpose of the invention: The technical problem to be solved by the present invention is to address the shortcomings of the existing technology by providing a facility cultivation management method to improve the yield and nitrogen content of hydroponic lettuce, solve the problem of the impact of coupled management of multiple environmental factors on the growth of hydroponic lettuce, and achieve high-yield and high-quality facility cultivation of lettuce.

[0005] To achieve the above-mentioned objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for managing and predicting the yield of hydroponically grown lettuce includes the following steps:

[0007] (1) Seedling raising

[0008] Sow lettuce seeds evenly and directly in rock wool, then place them in a container filled with seedling solution. Throughout the seedling period, keep the rock wool immersed in the seedling solution and maintain the seedling environment temperature at 25-28℃.

[0009] (2) Transplanting strong seedlings

[0010] Select strong seedlings, insert the rockwool with seedlings into the matching planting basket, and then transplant them together into the pipes or pots in the greenhouse containing hydroponic solution. Maintain an average daytime temperature of about 25-28℃ and humidity of 50-60% in the cultivation room, and a nighttime temperature of 20±1℃ and humidity of 65-70%.

[0011] (3) Environmental factor management

[0012] Temperature, humidity, light, and air carbon dioxide concentration were controlled during the following stages: the seedling establishment stage, the leaf emergence and stable growth stage, the rapid increase in plant height after the slowdown in leaf number growth, the rapid increase in plant height followed by the increase in single leaf area and biomass, and the harvestable stage.

[0013] Nutrient solution control: At the first transplanting, add coated slow-release urea fertilizer to the hydroponic solution and dilute to prepare the nutrient solution; change the hydroponic solution every 25-35 days according to the plant growth, retaining the remaining coated slow-release urea fertilizer; the hydroponic solution should have a total nitrogen ≥3 g / kg, total phosphorus ≥100 mg / kg, total potassium ≥120.0 mg / kg, pH 6.0-7.0, conductivity of 2.0 mS / cm -3.1 mS / cm, and salinity of 0.1%-0.15%;

[0014] (4) Lettuce yield forecast

[0015] Based on nutrient solution index or plant growth index, the yield of lettuce is predicted. Machine learning methods are used to construct a prediction model for chlorophyll and nitrogen content of hydroponic lettuce during the harvest period. The above environmental factors are adjusted according to the prediction results of hydroponic lettuce yield, chlorophyll and nitrogen content.

[0016] Specifically, in step (1), the seedling solution preparation process is as follows: add rooting powder to distilled water at a volume-to-weight ratio of 0.1-0.5 g / 100 ml, and simultaneously add limestone with a particle size less than 0.5 cm at a weight ratio of 10-15%. Maintain a temperature of 35-40 ℃ and a light intensity of 10000 lx-12000 lx for 2-3 days, then place it in the dark at -18 ℃ for 5-10 days. After taking it out, place it in a dark refrigerator at 4 ℃ for 5-10 days. When raising seedlings, take it out and dilute it 8-12 times, then put the rock wool with seeds into it, not completely immersing it, maintaining a liquid level of 0.5-1 cm, and keeping the rock wool completely moist. Maintain the pH of the seedling solution at 6.5-7.5, the conductivity at 1.3 mS / cm-2.4 mS / cm, and the salinity at 0.1-0.2%.

[0017] Specifically, in step (2), the hydroponic solution is kept at 1 / 2 the height of the planting basket and 1-2 cm below the lowest position of the planting hole in the pipe.

[0018] Specifically, in step (3), the temperature and humidity control methods are as follows: during the 7-day recovery period after transplanting, the temperature is controlled at 20℃-25℃ and the relative humidity is controlled at 60%-70%; during the stable growth stage after the seedlings recover, the temperature is controlled at 25℃-30℃ and the relative humidity is controlled at 70%-80%; during the rapid increase in plant height after the slowdown in the growth of the number of leaves, the temperature is controlled at 20℃-30℃ and the relative humidity is controlled at 60%-80%; during the rapid increase in plant height after the rapid increase in plant height, the temperature is controlled at 20℃-33℃ and the relative humidity is controlled at 70%-85%; after entering the harvestable stage, the temperature is controlled at 28℃-35℃ and the relative humidity is controlled at 75%-90%; when all growth stages exceed these ranges, the temperature is raised or lowered, and the humidity is increased or water vapor is removed.

[0019] Specifically, in step (3), the light control method is as follows: Light intensity is measured using a light meter during three time periods: 8:00-10:00 AM, 12:00-2:00 PM, and 4:00-6:00 PM, every half hour; during the 7-day recovery period after transplanting, the average light intensity during these three time periods should not exceed 6000 lx. If it is below 4000 lx, supplemental lighting is necessary; during the stable growth stage after recovery and leaf emergence, the average light intensity during these three time periods should not exceed 8000 lx. If it is below 6000 lx, supplemental lighting for 3-5 hours after 6:00 PM is required; during the rapid increase in plant height after the slowdown in leaf growth, the average light intensity during these three time periods should not exceed 9000 lx. If it is below 7000 lx, supplemental lighting for 3-5 hours after 6:00 PM is required; during the rapid increase in plant height and the subsequent increase in single leaf area and biomass, the average light intensity during these three time periods should not exceed 12000 lx. If it is below 8000 lx, supplemental lighting for 3-5 hours after 6:00 PM is required. For light intensity of 1000 lx, supplemental lighting for 6-8 hours is required after 18:00 in the evening. During the harvestable stage, the average light intensity for the three time periods should not exceed 8000 lx. If it is lower than 4000 lx, supplemental lighting for 1-3 hours is required after 18:00 in the evening. Purple light should be used for supplemental lighting in all stages.

[0020] Specifically, in step (3), the method for controlling the concentration of carbon dioxide in the air is as follows: CO2 monitoring is carried out in real time using CO2 monitoring equipment after transplanting; during the 7-day recovery period after transplanting, the concentration of CO2 in the air is controlled at 500 ppm-600 ppm; during the stable growth stage after the recovery period, the concentration of CO2 in the air is controlled at 500 ppm-600 ppm; during the rapid increase in plant height after the slowdown in the growth of the number of leaves, the concentration of CO2 in the air is controlled at 600 ppm-800 ppm; during the rapid increase in plant height, the concentration of CO2 in the air is controlled at 850 ppm-1000 ppm during the stage of increasing single leaf area and biomass; and after entering the harvestable stage, the concentration of CO2 in the air is controlled at 1000 ppm-1200 ppm.

[0021] Specifically, in step (3), during the first week after transplanting when the seedlings have recovered, natural ventilation is used to achieve the required CO2 concentration; starting in the second week, a CO2 growth agent is applied to increase the concentration of carbon dioxide in the air. When the CO2 concentration in the air (C air (Below the minimum value C) min When placing a CO2 gas cylinder or dry ice in the center of the lettuce planting area, with a height H from the lettuce, and the CO2 gas coverage height should be within 2H, then the coverage volume should be V (m³). 3 =s*2H; Adjust the CO2 gas cylinder release rate v to 2-5 mg / min; The required CO2 weight MCO2 and time T are:

[0022] MCO2 (mg) = [(Cmax +C min ) / 2-C air ]*V;

[0023] T (min) = MCO2 / v;

[0024] Wherein, the value of H ranges from 30cm to 60cm;

[0025] Lettuce planting area s<100m 2 ;

[0026] C min C max These represent the lowest and highest carbon dioxide concentrations in the air at different growth stages of lettuce, with values ​​ranging from the post-transplanting seedling recovery stage to the stable leaf-growing stage. min C max Both were 500 ppm and 600 ppm, respectively; during the stage of rapid plant height increase after the slowdown in leaf number growth, C min C max The concentrations were 600 ppm and 800 ppm, respectively; after a rapid increase in plant height, the plant entered a stage of increasing single leaf area and biomass. min C max The concentrations were 850 ppm and 1000 ppm, respectively; after reaching the harvestable stage, C... min C max The concentrations were 1000 ppm and 1200 ppm, respectively.

[0027] Specifically, in step (3) during nutrient solution control, the N content in the hydroponic solution comes from ammonium chloride, P from diamine, and potassium from wood ash cleaning solution. It is prepared with distilled water according to the requirements of total nitrogen, total phosphorus, and total potassium. In addition, trace elements such as Mg, Ca, S, Fe, B, Mn, Zn, Cu, and Mo are added, and vitamins B1, B2, or B6 are added at a weight-volume ratio of 1-2 mg / 10000 ml. The wood ash cleaning solution is the remaining filtrate from washing rice and wheat wood ash. After centrifugation, the supernatant is taken and added at a ratio of 1-2:10 with the hydroponic solution. The exogenously added coated slow-release urea fertilizer is a self-made 60-day slow-release urea fertilizer with a nitrogen content of not less than 45%. Its release effect should be that the initial nutrient dissolution rate is not less than 18.5%, and the cumulative nutrient release rate is not less than 50%, 75%, 85%, and 90% on the 14th, 28th, 56th, and 84th days, respectively.

[0028] Specifically, in step (4), the lettuce yield prediction model based on the nutrient solution index content is as follows:

[0029] Y=29.177+0.196EC1-333.155pH1+.219EC2+235.674pH2-0.116EC3+84.758pH3-0.131EC4+43.829 pH4-0.547N1-0.599N2+0.406N3-0.103N4(R 2 =0.747, p <0.0001);

[0030] Where Y represents the biomass of fresh leaves per lettuce plant, in g / plant; EC1, EC2, EC3, and EC4 represent the conductivity content during the 1st to 4th nutrient solution replacements, in uS / cm; pH1, pH2, pH3, and pH4 represent the pH during the 1st to 4th nutrient solution replacements; and N1, N2, N3, and N4 represent the total nitrogen content during the 1st to 4th nutrient solution replacements, in mg / L.

[0031] Specifically, in step (4), the lettuce yield prediction model based on plant growth indicators is as follows:

[0032] Y = 212.440 + 2.345H 2 -0.384L 2 -0.003A 2 -59.852H+15.236L+1.741A (R 2 =919, p <0.001)

[0033] Where Y represents the biomass of fresh leaves per lettuce plant, in g / plant; H, L, and A represent plant height, number of leaves, and maximum leaf area, respectively, in cm, leaf, and cm². 2 .

[0034] Specifically, the method for determining the prediction model for chlorophyll and nitrogen content in hydroponically grown lettuce in this invention is as follows:

[0035] S1: Taking photos

[0036] Take a photo of the lettuce; the photo resolution must be 1080p or higher, and the background must be black.

[0037] S2: Trim the image

[0038] Read the image from the specified path, along with the image's height and width, and crop an area from 10% to 80% of the original image's height vertically.

[0039] S3: Image Segmentation

[0040] Create a mask and background / foreground models. Use the GrabCut algorithm to segment image1. Initialize the segmentation by specifying a rectangular region. Find known or potential foregrounds using connected component analysis to find the largest connected component (1 for equal parts, 0 for unequal parts). Create a corresponding binary mask. Copy the original image into the connected component, setting parts not belonging to the largest connected component to 0, resulting in an image containing only leaves. Convert this image to grayscale and apply thresholding to obtain a binary image. Copy the original image, setting parts not belonging to leaves in the binary image to 0, resulting in the final segmented leaf image. Add borders at the top and bottom of the image vertically. Finally, return the processed leaf image to remove the background.

[0041] S4: Get image color values

[0042] Obtain the mean values ​​of R, G, and B of the leaves in the image, and fill the calculated data into the corresponding positions in the Excel spreadsheet;

[0043] S5: Calculate the color vegetation index

[0044] The independent variables for red, green, and blue vegetation indices are determined according to the color vegetation indices R, G, and B. First, the data in the R, G, and B columns of the Excel sheet are obtained. The R, G, B and the calculated parameters are used as independent variables X, and the indicators of leaf chlorophyll SPAD and leaf N content are used as dependent variables Y.

[0045] S6: Model Construction and Indicator Evaluation and Determination

[0046] Using the three RGB channels as independent variables and the leaf chlorophyll SPAD value and leaf N content (mg / g) as dependent variables, elastic network, kernel ridge regression, gradient boosting regression GBRT, XGBoost, and Light GBM models were constructed respectively. The optimal prediction model was determined by calculating the evaluation index R². Beneficial effects

[0047] (1) In order to shorten the hydroponic lettuce cultivation time and achieve higher yield, this invention compares the effects of different hydroponic liquid nitrogen levels, carbon dioxide concentrations, light and other factors on lettuce growth and yield through certain hydroponic experiments, and formulates reasonable hydroponic management techniques and leaf yield prediction models. This provides a theoretical basis for the rational application of nitrogen fertilizer, supplementation of carbon dioxide and light, etc. in facility hydroponic vegetables to obtain high-yield and high-quality lettuce, and also provides a reference for avoiding soil and water pollution caused by excessive application of nitrogen fertilizer.

[0048] (2) The seedling cultivation method of this invention can promote strong lettuce seedlings, and the nutrient solution cultivation and environmental management can increase the nitrogen content and yield of lettuce. In addition, the control of environmental temperature and humidity can also prevent certain diseases, pests and pathogens. This invention is simple and easy to operate, which can not only improve the yield and quality of lettuce, but also increase its commercial value and reduce operating costs. This invention is especially suitable for factory hydroponic lettuce cultivation, and can also achieve certain beneficial effects in the cultivation of other facility leafy vegetables. The operation process is simple, time-saving and labor-saving, and facility lettuce has high yield and high quality. Attached Figure Description

[0049] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0050] Figure 1 The examples show the temperature, humidity, and carbon dioxide concentration under different carbon dioxide treatments.

[0051] Figure 2 This is a comparison of the images before and after cropping in the example.

[0052] Figure 3 This is the fitting result of the image color values ​​and SPAD values ​​in the embodiment.

[0053] Figure 4 This refers to the correlation between the predicted values ​​and the actual values ​​of the SPAD value fitting model.

[0054] Figure 5 This is the fitting result of image color values ​​and leaf N content.

[0055] Figure 6 This describes the correlation between the predicted and actual values ​​of the leaf nitrogen content values ​​obtained from the fitting model. Detailed Implementation

[0056] The present invention can be better understood from the following embodiments.

[0057] This embodiment provides a facility cultivation method for hydroponic lettuce management, the specific steps of which include:

[0058] This invention relates to a hydroponic management method for lettuce, including seedling raising, transplanting methods, and environmental control management techniques. The environmental control management techniques mainly include the management of the hydroponic solution, the control of air environmental factors, and light management requirements. Harvesting can begin six weeks after transplanting.

[0059] 1. Experimental setup

[0060] The experiment was conducted in August 2021 (E 118°52′, N 32°2′) using hydroponics to grow lettuce. The lettuce variety was from the Jiangsu Academy of Agricultural Sciences. Hydroponic tubes were used for cultivation. Three treatments were set up: no CO2 additive (C0), conventional natural ventilation CO2 (C1), and the present invention using CO2 additive (C2). The CO2 fertilizer used was Quzhou Guosheng brand carbon dioxide growth agent. Under each CO2 treatment, three nitrogen levels were set in the hydroponic solution: N0 (no exogenous urea), N3 (1.95 g / L exogenous urea), and N5 (3.25 g / L exogenous urea). A total of 9 treatments were set up: C0N0, C0N3, YC0N5, C1N0, C1N3, C1N5, C2N0, C2N3, and C2N5, with each treatment repeated 6 times. Transplanting was carried out on August 13th. At the time of transplanting, lettuce seedlings with uniform growth were selected, fixed with transplanting cotton, and transplanted into hydroponic pots. Under C0 and C1 treatments, natural ventilation was provided during the first week after transplanting. Under C2 treatment, management was carried out according to Table 1. Harvesting began in the 6th week after transplanting.

[0061] First, evenly sow lettuce seeds directly into rock wool, then place them in a container filled with a homemade seedling solution. Throughout the seedling period, ensure the rock wool is fully saturated with the solution. The homemade seedling solution is prepared by adding rooting powder to distilled water at a volume ratio of 100ml:0.1g, along with limestone particles smaller than 0.5cm at 10%-15% of their weight. Then, maintain the solution at 35℃-40℃ and a light intensity of 10000-12000 lux for 3 days. Next, place it in the dark at -18℃ for 1 week. After removing it from the light, place it in a dark refrigerator at 4℃ for 1 week. When ready for seedling cultivation, dilute the solution approximately 10 times. Then, place the seeded rock wool in the solution, partially submerging it (maintaining a 1cm liquid level), ensuring the rock wool is completely moistened. Maintain the seedling solution at pH 7.0, conductivity of 2.0 mS / cm, and salinity of approximately 0.15%. Maintain the seedling environment temperature at 25℃.

[0062] Select robust seedlings for transplanting. Insert the rockwool seedlings into the matching planting basket, then transplant them together into a container filled with hydroponic solution inside the greenhouse. The hydroponic solution should cover half the height of the planting basket and be 2cm below the lowest point of the planting holes. Maintain an average daytime temperature of approximately 25-28℃ and humidity of 55%-60% in the cultivation room. At night, maintain a temperature of 20℃ and humidity of 65%-70%. Add 1.95g of coated slow-release urea fertilizer per liter of homemade hydroponic solution, and then dilute with 500ml of distilled water per liter to prepare the nutrient solution. Replace the homemade hydroponic solution every 30 days, retaining the remaining coated slow-release urea fertilizer. The nutrient solution should circulate from 8:00 AM to 4:00 PM. The homemade hydroponic solution contains ≥3 g / kg total nitrogen, ≥100 mg / kg total phosphorus, ≥120.0 mg / kg total potassium, pH 6.0-7.0, conductivity of 2.0 mS / cm-3.1 mS / cm, and salinity of approximately 0.1%-0.15%. The specific method is as follows: the nitrogen in the stock hydroponic solution comes from ammonium chloride, phosphorus from diamine, and potassium from wood ash washing liquid. It is prepared with distilled water according to the required total nitrogen, total phosphorus, and total potassium levels. Additionally, certain trace elements such as MgO, CaO, S, Fe, B, Mn, Zn, Cu, and Mo are added, along with vitamins B1, B2, and B6, at a weight-to-volume ratio of 1 mg: 10000 ml. The wood ash washing liquid is obtained by centrifuging the remaining filtrate from washing rice and wheat wood ash, collecting the supernatant, and adding it to the hydroponic solution at a ratio of 1:10. In addition, the exogenously added coated slow-release urea fertilizer is a self-made 60-day slow-release urea fertilizer with a nitrogen content of 45%. According to national standards, its release effect is as follows: initial nutrient dissolution rate is 18.5%, and cumulative nutrient release rates on days 3, 5, 7, 14, 28, 42, 56, 84, and 112 are 25.89%, 28.48%, 30.50%, 53.10%, 78.10%, 83.94%, 87.83%, 92.59%, and 97.22%, respectively.

[0063] Under C2 treatment, environmental factors, including temperature, humidity, CO2 concentration, and light, are controlled according to the needs of the lettuce at each growth stage. Specifically, after transplanting, the seedlings are allowed to recover for 7 days, with the temperature controlled at 20℃-25℃, relative humidity at 60%-70%, and CO2 concentration at 500 ppm-600 ppm. After recovery, the lettuce enters a stable leaf-growing stage (approximately 5 days), with the temperature controlled at 25℃-30℃, relative humidity at 70%-80%, and CO2 concentration at 500 ppm-600 ppm. Finally, after the leaf number growth slows down, the plant height rapidly increases (approximately 5 days), with the temperature controlled at 20℃-30℃, relative humidity at 60%-80%, and CO2 concentration at 600 ppm-800 ppm. After rapid growth in plant height, the plant enters a stage of increasing single-leaf area and biomass (approximately 7 days). During this period, the temperature should be controlled at 20℃-33℃, relative humidity at 70%-85%, and air CO2 concentration at 850 ppm-1000 ppm. This is followed by the harvestable stage, where the temperature should be controlled at 28℃-35℃, relative humidity at 75%-90%, and air CO2 concentration at 1000 ppm-1200 ppm. During any growth stage exceeding these ranges, heating or cooling, humidification or removal of water vapor, and increasing or removing excess CO2 are necessary. Light intensity should be measured every half hour using a light meter during the morning (8:00-10:00), midday (12:00-14:00), and evening (16:00-18:00) periods. After transplanting, during the 7-day recovery period, the average light intensity over these three time periods should not exceed 6000 lx. If it falls below 4000 lx, supplemental lighting is required. After the seedlings have established themselves, the plant enters a stable leaf-emerging growth stage (approximately 5 days). During this stage, the average light intensity should not exceed 8000 lx across three time periods. If it falls below 6000 lx, supplemental lighting for 4 hours after 6 PM is required. Following the slowdown in leaf number growth, the plant enters a rapid height increase stage (approximately 5 days). During this stage, the average light intensity should not exceed 9000 lx across three time periods. If it falls below 7000 lx, supplemental lighting for 4 hours after 6 PM is required. After the rapid height increase, the plant enters a stage of increasing single-leaf area and biomass (approximately 7 days). During this stage, the average light intensity should not exceed 12000 lx across three time periods. If it falls below 8000 lx, supplemental lighting for 6-8 hours after 6 PM is required. Finally, the plant enters the harvestable stage. During this stage, the average light intensity should not exceed 8000 lx across three time periods. If it falls below 4000 lx, supplemental lighting for 2 hours after 6 PM is required. All supplemental lighting is provided using purple light.

[0064] Other treatments should follow standard facility management for lettuce, without supplemental lighting. If the above requirements are not met, internal and external shade nets and evaporative cooling fans must be turned on for cooling and humidification, or fans must be turned on for exhaust and dehumidification.

[0065] To increase the concentration of carbon dioxide in the air, CO2 enhancers should be applied according to the actual situation, and the concentration should be monitored in real time using CO2 monitoring equipment. When the CO2 concentration in the air (C...) air (Lower than the lowest value C in the table) min At that time, the CO2 solid growth agent was placed in the lettuce planting area (area s < 100m²). 2 In the middle, the height is H (30cm-60cm) away from the lettuce. The CO2 gas coverage height should be around 2H, then the coverage volume should be V (m³). 3 The CO2 release rate v from the gas cylinder is adjusted to 2-5 mg / min. The required CO2 weight MCO2 and time T are then:

[0066] MCO2 (mg) = [(C max +C min ) / 2-C air ]*V;

[0067] T (min) = MCO2 / v.

[0068] Wherein, the value of H ranges from 30cm to 60cm;

[0069] Lettuce planting area s<100m 2 ;

[0070] C min C max These represent the lowest and highest carbon dioxide concentrations in the air at different growth stages of lettuce, with values ​​ranging from the post-transplanting seedling recovery stage to the stable leaf-growing stage. min C max Both were 500 ppm and 600 ppm, respectively; during the stage of rapid plant height increase after the slowdown in leaf number growth, C min C max The concentrations were 600 ppm and 800 ppm, respectively; after a rapid increase in plant height, the plant entered a stage of increasing single leaf area and biomass. min C max The concentrations were 850 ppm and 1000 ppm, respectively; after reaching the harvestable stage, C... min C max The concentrations were 1000 ppm and 1200 ppm, respectively.

[0071] 2. Data Collection and Analysis

[0072] CO2 concentration, temperature, and humidity were monitored in real time on a mobile phone using a CO2 monitor (Beijing Green Scale Technology Co., Ltd.), and light intensity was measured using a light meter. Crop plant height and leaf number were measured. After harvest, plant height, leaf number, root length, leaf fresh weight (yield), and root fresh weight were measured with a ruler. After drying, the root dry weight and leaf dry weight were measured. After planting, chlorophyll meter was used to measure chlorophyll content, leaf surface temperature, leaf nitrogen content, and leaf surface humidity. Light intensity was measured with an illuminometer each time chlorophyll content was measured. Measurements were taken from uniformly growing, disease-free, and pest-free leaves. Nitrogen content in plant leaves was measured at harvest. This embodiment displays average values ​​for various indicators for selected dates, as shown in Tables 2 and 3. Figure 1 See the image.

[0073] 3. Results

[0074] (1) Environmental monitoring status

[0075] A. Average light intensity

[0076] Table 1. Average light intensity (lux) inside the experimental greenhouse.

[0077]

[0078] Note: Since C0, C1, and C2 are located adjacent to each other in the same greenhouse, the light intensity is the same.

[0079] B. Temperature, humidity, and air carbon dioxide concentration

[0080] Figure 1 It shows the temperature, humidity, and carbon dioxide concentration under different carbon dioxide treatments.

[0081] C. Hydroponic solution indicators (harvest period)

[0082] Table 2. Average values ​​of the hydroponic solution before replacement at different stages of the entire process.

[0083]

[0084] Note: EC1, EC2, EC3, and EC4 represent the conductivity values ​​during the 1st to 4th nutrient solution replacements, in μS / cm; pH1, pH2, pH3, and pH4 represent the pH values ​​during the 1st to 4th nutrient solution replacements; N1, N2, N3, and N4 represent the total nitrogen values ​​during the 1st to 4th nutrient solution replacements, in mg / L.

[0085] (2) Growth and yield

[0086] Table 3. Growth and yield at harvest (average values)

[0087]

[0088] Note: Y represents the biomass of fresh leaves per lettuce plant, in g / plant. H, L, and A represent the plant height, number of leaves, and maximum leaf area at harvest time, respectively, in cm, leaf, and cm². 2 R 2 R represents the goodness of fit of the model. 2 =0.864 indicates a high degree of fit. p <0.001 indicates that the prediction model has extremely high accuracy and the model indicators are highly significantly correlated.

[0089] (3) Leaf chlorophyll SPAD value and nitrogen content (harvest period)

[0090] Table 4. Average values ​​of the hydroponic solution before replacement at different stages of the entire process.

[0091]

[0092] Note: SPAD1, SPAD2, SPAD3, SPAD4, SPAD5, and SPAD6 are the average values ​​of leaf chlorophyll SPAD from week 1 to week 6 (dimensionless, no unit); LN1, LN2, LN3, LN4, LN5, and LN6 are the average values ​​of leaf nitrogen content from week 1 to week 6 (unit: mg / g).

[0093] (4) Production model construction

[0094] Lettuce yield prediction model based on nutrient solution index content:

[0095] Y=29.177+0.196EC1-333.155pH1+.219EC2+235.674 pH2-0.116EC3+84.758 pH3-0.131EC4+43.829 pH4-0.547N1-0.599N2+0.406N3-0.103N4(R 2 =0.747, p <0.0001)

[0096] Where Y represents the biomass of fresh leaves per lettuce plant, in g / plant. EC1, EC2, EC3, and EC4 represent the conductivity content during the 1st to 4th nutrient solution replacements, in uS / cm; pH1, pH2, pH3, and pH4 represent the pH during the 1st to 4th nutrient solution replacements; and N1, N2, N3, and N4 represent the total nitrogen content during the 1st to 4th nutrient solution replacements, in mg / L. R 2 R represents the goodness of fit of the model. 2 =0.747 indicates a high degree of fit. p<0.0001 indicates that the prediction model has extremely high accuracy and the model indicators are highly significantly correlated.

[0097] Lettuce yield prediction model based on plant growth indicators:

[0098] Y = 212.440 + 2.345H 2 -0.384L 2 -0.003A 2 -59.852H+15.236L+1.741A (R 2 =919, p <0.001)

[0099] Where Y represents the biomass of fresh leaves per lettuce plant, in g / plant. H, L, and A represent the plant height, number of leaves, and maximum leaf area at harvest time, respectively, in cm, leaf, and cm². 2 R 2 R represents the goodness of fit of the model. 2 =0.864 indicates a high degree of fit. p <0.001 indicates that the prediction model has extremely high accuracy and the model indicators are highly significantly correlated.

[0100] (5) Machine learning was used to construct a prediction model for chlorophyll SPAD value and nitrogen content of leaves at harvest time.

[0101] The above prediction of chlorophyll and nitrogen content in hydroponically grown lettuce involves constructing a predictive model for chlorophyll SPAD value and leaf nitrogen content using machine learning methods. The specific steps are as follows:

[0102] Step 1: Take a photo

[0103] Take a photo of the lettuce; the photo resolution must be 1080p or higher, and the background must be black.

[0104] Step 2: Trim the image

[0105] The specific steps for cropping an image are as follows:

[0106] 1) Use image = cv2.imread(path) to read an image from a specified path. path is the input parameter of the function, which represents the path of the image file.

[0107] 2) Use height1, width1 = image.shape[:2] to get the height and width of the image.

[0108] 3) Using `image1 = image[int(0.1*height1):int(0.8*height1),:]`, a region of 10% to 80% of the height is cropped vertically from the original image to reduce the area to be processed. The comparison of the above experimental images before and after cropping is as follows. Figure 2 As shown.

[0109] Step 3: Segmenting the image

[0110] 1) Create a mask and background / foreground models. Use the GrabCut algorithm to segment image1, initializing the segmentation by specifying a rectangular region (based on graph cut). Then use np.where to find known or potential foreground elements.

[0111] 2) Use cv2.connectedComponents to return num_labels (the number of labeled connected components) and labeled_mask (an image of the same size as the input image). Use connected component analysis to find the largest connected component: np.where(labeled_mask == largest_component_label, 1, 0). If they are equal, the value is 1; if they are not equal, the value is 0. That is, 1 is foreground and 0 is background. Create the corresponding binary mask largest_component_mask.

[0112] 3) By copying the original image1 to largest, and setting the parts that do not belong to the largest connected component to 0, we obtain an image that only contains the leaves.

[0113] 4) Convert largest to a grayscale image gray_largest, and apply Otsu’s thresholding method to obtain a binary image thresholded.

[0114] 5) segmented_leaf = image1.copy(): Copy the original image.

[0115] 6) segmented_leaf[thresholded == 0] = 0: Set the region in the binary image that does not belong to the leaf to 0 in the copied image to obtain the final segmented leaf image.

[0116] 7) segmented_leaf = cv2.copyMakeBorder(segmented_leaf, int(0.1*height1), int(0.2*height1), 0,0, cv2.BORDER_CONSTANT, value=(0, 0, 0)): Adds borders to the top and bottom of the image in the vertical direction to retain more information.

[0117] 8) Returns the processed leaf image segmented_leaf.

[0118] 9) Use rembg.remove in rembg to remove the background of img.

[0119] Step 4: Obtain the image color values

[0120] Run main.py to obtain the mean values ​​of R, G, and B for plants in an image and fill the calculated data into the corresponding positions in an Excel spreadsheet. This can convert low-resolution images into high-resolution images for recognition.

[0121] Step 5: Calculate the color vegetation index

[0122] The independent variables for red, green, and blue vegetation colors are determined based on the color vegetation indices R, G, and B. The `predict_N.py` file is run to retrieve the data from the R, G, and B columns of the Excel spreadsheet. These R, G, and B values, along with the calculated parameters, are used as the independent variable X. The indicators chlorophyll SPAD (a dimensionless, unitless index) and leaf nitrogen content (mg / g) are used as the dependent variable Y for training the following fitted model. R... 2 As an evaluation indicator.

[0123] Step 6: Model Construction and Indicator Evaluation Determination

[0124] XGBoost and Light GBM models were constructed with RGB three channel values ​​as independent variables and chlorophyll SPAD value or leaf N content as dependent variables, respectively. The evaluation index R² was calculated to evaluate the quality of the models.

[0125] 1) Using the three RGB channels as independent variables and the leaf chlorophyll SPAD value and leaf N content (mg / g) as dependent variables, construct fitting models such as elastic network, kernel ridge regression, gradient boosting regression GBRT, XGBoost, and Light GBM respectively. The code is shown below, and the evaluation index R² is calculated to evaluate the quality of the model.

[0126] #ElasticNetwork

[0127] ENet = ElasticNet(alpha=0.01,l1_ratio=0.9,random_state=3)

[0128] ENet.fit(X,Y)

[0129] Y_predict1 = np.round(ENet.predict(X),decimals=4)

[0130] R1 = np.round(r2_score(Y,Y_predict1),4)

[0131] #Return of Nuclear Ridge

[0132] KRR=KernelRidge(alpha=0.05,kernel='polynomial',degree=2,coef0=0.5)

[0133] KRR.fit(X,Y)

[0134] Y_predict2 = np.round(KRR.predict(X),decimals=4)

[0135] R2 = np.round(r2_score(Y,Y_predict2),4)

[0136] #Gradient Boosting Regression GBRT

[0137] GBoost=GradientBoostingRegressor(n_estimators=100,learning_rate=0.03,max_depth=3,max_features='sqrt',loss='huber',random_state=5)

[0138] GBoost.fit(X, Y)

[0139] Y_predict3 = np.round(GBoost.predict(X), decimals=4)

[0140] R3 = np.round(r2_score(Y,Y_predict3),4)

[0141] # Random Forest Regression

[0142] # RF = RandomForestRegressor(n_estimators=100, random_state=5)

[0143] # RF.fit(X, Y)

[0144] # Y_predict3 = np.round(RF.predict(X), decimals=4)

[0145] # R3 = np.round(r2_score(Y,Y_predict3),4)

[0146] # Extreme Gradient Boosting XGBoost

[0147] model_xgb=xgb.XGBRegressor(colsample_bytree=0.5,gamma=0.05,learning_rate=0.05,max_depth=4,min_child_weight=1.8, n_estimators=3000,reg_alpha=0.4,reg_lambda=0.8,subsample=0.5,random_state=7,nthread=-1)

[0148] model_xgb.fit(X, Y)

[0149] Y_predict4 = np.round(model_xgb.predict(X), decimals=4)

[0150] R4 = np.round(r2_score(Y,Y_predict4),4)

[0151] #Lightgbm

[0152] model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=3,learning_rate=0.1,n_estimators=2200,max_bin=55,bagging_fraction=0.8, bagging_frep=5,feature_fraction=0.2,feature_fraction_seed=4,bagging_seed=9,min_data_in_leaf=4, min_sum_hessian_in_leaf=4,verbose=-1)

[0153] model_lgb.fit(X, Y)

[0154] Y_predict5 = np.round(model_lgb.predict(X), decimals=4)

[0155] R5 = np.round(r2_score(Y,Y_predict5),4)

[0156] In the above example, X represents the parameter calculated from the color value extracted by main.py, Y represents the leaf chlorophyll SPAD or leaf N content (mg / g) index, fit(X, Y) means calling the true value of the column containing Y in Excel, and Y_predict is the fitted prediction model.

[0157] A. Fit between actual and predicted values ​​in the chlorophyll SPAD model

[0158] Run main.py to obtain the mean values ​​of R, G, and B for the plants in the image and then fill the calculated data into the corresponding positions in an Excel spreadsheet. See below. Figure 3 , Figure 4 As shown

[0159] The scatter plots of the true and predicted values ​​for Elastic Network, Kernel Ridge Regression, Gradient Boosting Regression (GBRT), XGBoost, and Light GBM are shown in the figure below. From these plots, we can see that after training and fitting, the R-values ​​of Elastic Network... 2 The values ​​are 0.5165, 0.8992, 0.9979, 0.9916, and 0.9923, respectively.

[0160] B. Fit between actual and predicted values ​​in the leaf nitrogen content model.

[0161] Run main.py to obtain the mean values ​​of R, G, and B for the plants in the image and then fill the calculated data into the corresponding positions in an Excel spreadsheet. See below. Figure 5 , Figure 6 As shown.

[0162] The scatter plots of the actual and predicted values ​​for Elastic Network, Kernel Ridge Regression, Gradient Boosting Regression (GBRT), XGBoost, and Light GBM are shown in the figure below. From these plots, we can see that after training and fitting, the R-values ​​of the Elastic Network... 2 The values ​​are 0.2455, 0.2546, 0.7153, 0.7705, and 0.7783, respectively.

[0163] The current growth index parameters, chlorophyll SPAD value, and leaf nitrogen content are measured. The yield, chlorophyll SPAD value, and leaf nitrogen content of the lettuce are calculated using a yield, chlorophyll SPAD value, and nitrogen prediction model, thereby obtaining the cultivation condition parameters that need to be adjusted.

[0164] This invention provides a method for the management and yield prediction of hydroponic lettuce. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A method of hydroponic lettuce management and yield prediction, characterized in that, Comprising the following steps: (1) Seedling The lettuce seeds are evenly and directly scattered in the rock wool, and then placed in a container containing a seedling liquid. The rock wool is immersed in the seedling liquid throughout the seedling period, and the seedling environment temperature is maintained at 25-28°C; (2) Transplanting of strong seedlings Select strong seedlings, insert the rock wool with seedlings into a matching planting basket first, and then transplant them together into a pipe or pot device containing water culture solution in a greenhouse. The water culture solution is maintained at 1 / 2 height of the planting basket, 1-2 cm lower than the lowest position of the pipe planting hole. The average temperature in the cultivation room is maintained at 25-28°C during the day, and the humidity is maintained at 50-60%. The temperature at night is 20±1°C, and the humidity is 65-70%; (3) Environmental factor management The temperature, humidity, light, and air carbon dioxide concentration are controlled during the following stages: the post-transplanting acclimation stage, the leaf emergence and stable growth stage, the stage of rapid increase in plant height after the number of leaves increases slowly, the stage of increase in single leaf area and biomass after the rapid increase in plant height, and the harvestable stage; Nutrient solution control: when the seedlings are transplanted for the first time, coated slow-release urea fertilizer is added to the water culture solution to prepare a nutrient solution. The water culture solution is replaced every 25-35 days according to the plant growth, and the remaining coated slow-release urea fertilizer is retained. The total nitrogen content of the water culture solution is ≥3 g / kg, the total phosphorus content is ≥100 mg / kg, the total potassium content is ≥120.0 mg / kg, the pH is 6.0-7.0, the electrical conductivity is 2.0 mS / cm-3.1 mS / cm, and the salinity is 0.1%-0.15%; (4) Lettuce yield prediction Based on the nutrient solution index content or based on the plant growth index, the yield of lettuce is predicted, and a machine learning method is used to construct a water culture lettuce chlorophyll and nitrogen content prediction model at the harvest stage. Based on the prediction results of the yield, chlorophyll, and nitrogen content of the water culture lettuce, the above environmental factors are adjusted; The determination method of the water culture lettuce chlorophyll and nitrogen content prediction model is as follows: S1: Taking a photo Take a photo of the lettuce. The resolution of the photo must be 1080p or higher, and the background must be black. S2: Trimming the picture Read the image from the specified path, and the height and width of the image. Crop the area from 10% to 80% of the height in the vertical direction of the original image. S3: Splitting the picture Create a mask and background, foreground model, and use the GrabCut algorithm to split image1. Initialize the split by specifying a rectangular region, and find the known foreground or possible foreground. Use connected component analysis to find the largest connected component, which is equal to 1 and not equal to 0. 1 is the foreground and 0 is the background. Create a corresponding binary mask. Then copy the original image to the connected component, and set the part that does not belong to the largest connected component to 0 to get an image containing only leaves. Then convert it to a grayscale image and apply thresholding to get a binary image. Copy the original image and set the area in the binary image that does not belong to the leaves to 0 in the image to get the final split leaf image. Add a border at the top and bottom of the image in the vertical direction. Finally, return the processed leaf image to remove the background of the image. S4: Obtain the color value of the picture Obtain the average of R, G, B of the leaves in the picture, and fill the calculated data into the corresponding position of the excel table; S5: Calculate the color vegetation index According to the color vegetation index R, G, B, determine the red, green and blue color vegetation index independent variables; first obtain the data in the R, G, B columns in the excel table, and take the above R, G, B and the calculated parameters as independent variables X, and take the leaf chlorophyll SPAD and leaf N content as dependent variables Y respectively; S6: Model construction and index evaluation determination Take the RGB three channel values as independent variables, and the leaf chlorophyll SPAD value and leaf N content (mg / g) as dependent variables to construct elastic network, kernel ridge regression, gradient boosting regression GBRT, XGBoost and Light GBM model respectively, and determine the best prediction model by calculating the evaluation index R²; In step (4), the lettuce yield prediction model based on the nutrient solution index content is: Y=29.177+0.196EC1-333.155pH1+.219EC2+235.674pH2-0.116EC3+84.758pH3-0.131EC4+43.829 pH4-0.547N1-0.599N2+0.406N3-0.103N4 (R 2 =0.747, p <0.0001); Wherein, Y is the fresh leaf biomass of single lettuce, unit is g / plant; EC1, EC2, EC3, EC4 are the conductivity contents when the nutrient solution is replaced for the first time to the fourth time, unit is uS / cm; pH1, pH2, pH3, pH4 are the acid-base degrees when the nutrient solution is replaced for the first time to the fourth time; N1, N2, N3, N4 are the total nitrogen contents when the nutrient solution is replaced for the first time to the fourth time, unit is mg / L; In step (4), the lettuce yield prediction model based on the plant growth index is: Y = 212.440 + 2.345H 2 -0.384L 2 -0.003A 2 -59.852H + 15.236L + 1.741A (R 2 = 919, p <0.001) Wherein, Y is the fresh leaf biomass of single lettuce plant, unit is g / plant; H, L, A represent the plant height, leaf number, maximum leaf area respectively, unit is cm, piece, cm 2 .

2. The hydroponic lettuce management and yield prediction method according to claim 1, wherein, In step (1), the seedling solution preparation process is: according to the volume weight ratio of 0.1-05g / 100 ml, add rooting powder in distilled water, at the same time, add 10-15% limestone with particle size less than 0.5 cm in weight ratio in the liquid, keep the temperature at 35-40 ℃, and place under the light intensity of 10000lx-12000 lx for 2-3 days, then place under light-18 ℃ for 5-10 days, and then place in the refrigerator under light 4 ℃ for 5-10 days; When seedling, take out and dilute 8-12 times, then put the rock wool with seeds in it, not completely immersed, keep the liquid level at 0.5-1 cm, and the rock wool can be kept completely wet; Keep the pH of the seedling solution at 6.5-7.5, the conductivity can be 1.3 mS / cm-2.4 mS / cm, and the salinity is 0.1-0.2%.

3. The hydroponic lettuce management and yield prediction method of claim 1, wherein, In step (3), the temperature and humidity control method is as follows: in the 7 days after transplanting, the temperature is controlled at 20-25℃ and the relative humidity is controlled at 60-70%; after the seedling stage, the temperature is controlled at 25-30℃ and the relative humidity is controlled at 70-80%; after the leaf number growth slows down and the plant height rapidly increases, the temperature is controlled at 20-30℃ and the relative humidity is controlled at 60-80%; after the plant height rapidly increases and the single leaf area and biomass increase, the temperature is controlled at 20-33℃ and the relative humidity is controlled at 70-85%; after entering the harvest stage, the temperature is controlled at 28-35℃ and the relative humidity is controlled at 75-90%; when the temperature exceeds the above range in all growth stages, the temperature is increased or decreased, and the humidity is increased or removed.

4. The hydroponic lettuce management and yield prediction method of claim 1, wherein, In step (3), the light control method is as follows: the light intensity is measured at 8:00-10:00 in the morning, 12:00-14:00 at noon, and 16:00-18:00 at night every half hour; in the 7 days after transplanting, the average light intensity in the three time periods is not higher than 6000 lx, and if it is lower than 4000 lx, light supplement is necessary; after the seedling stage, the average light intensity in the three time periods is not higher than 8000 lx, and if it is lower than 6000 lx, light supplement is necessary for 3-5 hours after 18:00 at night; after the leaf number growth slows down and the plant height rapidly increases, the average light intensity in the three time periods is not higher than 9000 lx, and if it is lower than 7000 lx, light supplement is necessary for 3-5 hours after 18:00 at night; after the plant height rapidly increases and the single leaf area and biomass increase, the average light intensity in the three time periods is not higher than 12000 lx, and if it is lower than 8000 lx, light supplement is necessary for 6-8 hours after 18:00 at night; in the harvest stage, the average light intensity in the three time periods is not higher than 8000 lx, and if it is lower than 4000 lx, light supplement is necessary for 1-3 hours after 18:00 at night; in each stage, purple light is used for light supplement.

5. The hydroponic lettuce management and yield prediction method of claim 1, wherein, In step (3), the air CO2 concentration control method is as follows: after transplanting, real-time monitoring is carried out by using a CO2 monitoring device; in the 7 days after transplanting, the air CO2 concentration is controlled at 500-600 ppm; after the seedling stage, the air CO2 concentration is controlled at 500-600 ppm; after the leaf number growth slows down and the plant height rapidly increases, the air CO2 concentration is controlled at 600-800 ppm; after the plant height rapidly increases and the single leaf area and biomass increase, the air CO2 concentration is controlled at 850-1000 ppm; after entering the harvest stage, the air CO2 concentration is controlled at 1000-1200 ppm.

6. The hydroponic lettuce management and yield prediction method according to claim 5, wherein, In step (3), during the first week after transplanting, natural ventilation is used to reach the CO2 concentration requirement; from the second week, CO2 growth agent is used to increase the CO2 concentration in the air. When the CO2 concentration (C air ) in the air is lower than the minimum value C min , a CO2 gas bottle or dry ice is placed in the middle of the lettuce planting area, with a height H from the lettuce. The CO2 gas coverage height should be within 2H, and the coverage volume should be V (m 3 )=s*2H. The CO2 gas bottle release rate v is adjusted to 2-5 mg / min. The required CO2 weight MCO2 and time T are: MCO2 (mg) = [(C max + C min ) / 2 - C air ] * V; T (min) = MCO2 / v; wherein H is in the range of 30-60 cm. Lettuce growing area area s < 100 m 2 ; C min , C max are the minimum and maximum CO2 concentrations in the air for different growth stages of lettuce, respectively, and the value range is that the CO2 concentration in the air C min , C max are 500 ppm and 600 ppm, respectively; in the stage of rapid increase of plant height after the growth of leaf number slows down, C min , C max are 600 ppm and 800 ppm, respectively; in the stage of increase of single leaf area and biomass after the rapid increase of plant height, C min , C max are 850 ppm and 1000 ppm, respectively; after entering the harvestable stage, C min , C max are 1000 ppm and 1200 ppm, respectively.

7. The hydroponic lettuce management and yield prediction method of claim 1, wherein, Step (3) in the process of nutrient solution control, the N content in the original solution of hydroponic solution comes from ammonium chloride, P comes from diamine, potassium comes from straw ash cleaning solution, according to the total nitrogen, total phosphorus, total potassium requirements and distilled water configuration, in addition to the addition of Mg, Ca, S, Fe, B, Mn, Zn, Cu, Mo trace elements, and according to the weight volume ratio of 1-2 mg / 10000 ml to add vitamin B1, B2 or B6; The straw ash cleaning solution is the remaining filtrate of cleaning rice and wheat straw ash, after centrifugation, the supernatant is taken, and is added according to the ratio of 1-2:10 with the hydroponic night; The exogenous added coating slow-release urea fertilizer is self-made 60-day slow-release urea fertilizer, which contains not less than 45% of nitrogen, and its release effect should be that the initial nutrient dissolution rate is not less than 18.5%, and the cumulative nutrient release rate is not less than 50%, 75%, 85% and 90% on the 14th, 28th, 56th and 84th day.

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