Method and device for measuring nitrogen content in rice leaves based on deep learning

Abstract

The present invention relates to the field of agricultural technology, and more particularly to a method and device for measuring the nitrogen content of rice leaves based on deep learning. The method comprises the following steps: S1, sample collection: collecting a number of rice plants as samples and immersing the sample roots in a nutrient solution; S2, sample transport: adjusting transport parameters according to the current environment and transporting the samples to a laboratory; recording the activity index of the samples during transport; S3, sample processing: washing the samples after transport to the laboratory with clean water and removing dried and damaged rice leaves; S4, sample detection: detecting the treated rice leaves using a spectrometer to obtain spectral data of the rice leaves; S5, intelligent analysis: analyzing the spectral data of the rice leaves based on a deep learning model to obtain the current nitrogen content of the rice leaves; and compensating the current nitrogen content of the rice leaves based on the transport parameters and activity indexes using a compensation model. The present invention can optimize the sample transport environment and improve detection accuracy.

Description

Technical Field

[0001] The present invention relates to the field of agricultural technology, and in particular to a method and device for measuring the nitrogen content of rice leaves based on deep learning. Background Art

[0002] Nitrogen is the nutrient most closely linked to crop photosynthesis, yield, and quality. It is also the mineral element most required and applied by crops. As one of the world's major food crops, nitrogen nutrition assessment in rice is crucial for monitoring rice growth and enabling precise field management. Therefore, rapid and accurate assessment of crop nitrogen status and efficient, targeted fertilization are crucial requirements for modern agricultural production.

[0003] Existing methods for diagnosing rice leaf nitrogen content primarily rely on laboratory spectral testing of plant tissues. Based on the detected spectral data, deep learning models are then used to intelligently analyze the nitrogen content of rice leaves, allowing rapid determination of the nitrogen content. However, because samples must be collected from the field and then transported to the laboratory, the activity and nitrogen content of the samples change during transport. Furthermore, this inadequate protection of the samples during transport further reduces the accuracy of the test results.

[0004] In summary, addressing the existing problem of insufficient sample protection during transport, which reduces the accuracy of test results, has become a pressing challenge in the field. Therefore, it is necessary to propose a method and device for measuring nitrogen content in rice leaves based on deep learning that can enhance sample protection during transport and improve the accuracy of test results. Summary of the Invention

[0005] To solve the above problems, the present invention provides a method and device for measuring the nitrogen content of rice leaves based on deep learning. Through the design of the outer shell and top cover, the sample can be sealed and transported, and the transportation environment of the sample can be adjusted at the same time to protect the activity of the sample and reduce the change in its carbon content. Then, through the design of the deep learning model and the compensation model, the actual carbon content of the sample after compensation can be effectively calculated, thereby improving the detection accuracy.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: A method for measuring the nitrogen content of rice leaves based on deep learning comprises the following steps:

[0007] S1, sample collection: Collect several rice plants as samples and soak the sample roots in nutrient solution.

[0008] S2, sample transport: adjust the transport parameters of the transport environment according to the current environment, and transport the sample to the laboratory; record the activity indicators of the sample during the transport process.

[0009] S3, sample processing: After being transported to the laboratory, the samples were washed with clean water and the dried and damaged rice leaves were removed.

[0010] S4, sample detection: performing spectral detection on the treated rice leaves using a spectrometer to obtain spectral data of the rice leaves.

[0011] S5, intelligent analysis: Analyze the spectral data of rice leaves based on the deep learning model to obtain the current nitrogen content of the rice leaves; compensate the current nitrogen content of the rice leaves according to the transport parameters and activity indicators based on the compensation model to obtain the actual nitrogen content of the rice leaves.

[0012] Furthermore, in S2, the transport parameters include the temperature, humidity and light of the transport environment; the activity indicators include the photosynthetic rate, transpiration rate and stomatal conductance during the sample transport process; in S5, the deep learning module is trained based on the spectral data of several rice leaves; and the compensation model is trained based on the linear change data of several transport parameters and activity indicators.

[0013] The formula for calculating photosynthetic rate is as follows:

[0014] V O2 =(Vx-Vt) / M (1).

[0015] Among them, V O2 represents the oxygen generation rate, Vx represents the oxygen release amount, Vt represents the initial oxygen concentration), and M represents the leaf area.

[0016] The transpiration rate calculation formula is as follows:

[0017] Tr = Ox / (Mt×T) (2).

[0018] Among them, Tr represents transpiration rate, Ox represents transpiration water loss, Mt represents unit leaf area, and T represents time.

[0019] The formula for calculating stomatal conductance is as follows:

[0020] gs = E / (1.6×Vpd) (2).

[0021] Where gs is the stomatal conductance, E is the transpiration rate, Vpd is the vapor pressure deficit at the leaf-air interface, and 1.6 is an empirical constant.

[0022] The technical principles of the above solution are as follows:

[0023] Several rice plants were collected as samples, and the roots of all samples were immersed in nutrient solution. At the same time, the temperature, humidity and light of the transportation environment were adjusted according to the suitable temperature, humidity and light of the samples.

[0024] The photosynthetic rate, transpiration rate and stomatal conductance of the samples during transport were recorded in real time during the transport process.

[0025] After the samples were transported to the laboratory, they were washed with clean water and dried and damaged rice leaves were removed. After the treatment, the rice leaves were spectrally detected using a spectrometer to obtain spectral data of the rice leaves.

[0026] The spectral data of rice leaves were analyzed using a deep learning model to obtain the current nitrogen content of the rice leaves. Then, based on the compensation model, the current nitrogen content of the rice leaves was compensated according to the temperature, humidity and light of the transportation environment, as well as the photosynthetic rate, transpiration rate and stomatal conductance of the rice during transportation, to obtain the actual nitrogen content of the rice leaves.

[0027] The above scheme has the following beneficial effects:

[0028] 1. The present invention analyzes the spectral data of rice leaves through a deep learning model, and can quickly and accurately estimate the nitrogen content of rice leaves. Compared with the existing technology, the present invention further considers the temperature, humidity and light during the transportation process as well as the photosynthetic rate, transpiration rate and stomatal conductance of rice through a compensation model, and further corrects the estimated nitrogen content, thereby improving the accuracy of detection.

[0029] 2. During the transportation process, the present invention adjusts the temperature, humidity and light of the transportation environment to a range suitable for rice, thereby improving the activity of rice during transportation and reducing the loss of its carbon content, thereby optimizing the rice transportation environment, improving the quality of samples transported to the laboratory, and further improving the accuracy of its carbon content detection results.

[0030] 3. By extensively training the deep learning model and the compensation model, the present invention enables intelligent and automated analysis of samples, thereby quickly and accurately obtaining the carbon content analysis results of the samples. Compared with manual analysis, the present invention has higher analysis efficiency.

[0031] Furthermore, a device for measuring the nitrogen content of rice leaves based on deep learning includes a controller, an outer shell and a top cover, wherein a plurality of clamping blocks are slidably fitted on the outer shell; a plurality of rotating rods are rotatably fitted on the outer wall of the outer shell, and the rotating rods all penetrate the side wall of the outer shell, and the rotating rods are respectively fixedly connected to an external gear and an internal gear at one end outside the outer shell and at one end inside the outer shell, and the external gears are meshed with racks, and the upper parts of the racks are fixedly connected to the top cover, and a plurality of limiting holes are opened on the top cover, and a plurality of limiting rods are fixedly connected to the top of the outer shell, and the limiting rods are slidably fitted with the limiting holes adjacent to them; a placement slot is opened in the middle of the outer shell; the bottom of the outer shell is fixedly connected to a nutrient box; and nutrient solution is stored in the nutrient box.

[0032] A toothed disc rotates inside the outer shell, and the internal gears are engaged with the toothed disc. A spiral convex strip is fixedly connected to the top of the toothed disc, and a concave strip is fixedly connected to the bottom of the clamping block, and the concave strips are slidably matched with the convex strips; a driving member is fixedly connected inside the outer shell, and the output shaft of the driving member is coaxially fixedly connected to the adjacent internal gear; the controller is used to control the opening and closing of the driving member.

[0033] Beneficial effects: During the transportation process, the operator can place the collected samples into the placement tank and the sample roots into the nutrient box.

[0034] When placing the plants, use clean water to clean the surface of the leaves, and rotate the plants during this process so that the plants can be cleaned evenly, and use a sponge to wipe the leaves; this can effectively avoid the problem that if there are hydrophobic attachments on the surface of the leaves during the water cleaning process, it will be difficult for water to clean them. The key is to use clean water to clean the leaves and wipe them with a sponge to improve the cleaning effect of the plants and improve the consistency of subsequent data.

[0035] The operator controls the output shaft of the driving member to rotate clockwise through the controller. The driving member drives the adjacent internal gear to rotate, and the internal gear drives the gear plate to rotate, and the gear plate drives the remaining internal gears to rotate; the internal gear drives the rotating rod to rotate, and the rotating rod drives the outer gear to rotate, and the outer gear will drive the rack meshing with it to move upward.

[0036] When the rack moves upward, the rack will drive the top cover to move upward, so that the outer shell and the top cover move away from each other; at the same time, due to the rotation of the toothed disc, the convex strips will also rotate accordingly. Since the convex strips are spiral-shaped, the concave strips and the convex strips slide together. Therefore, due to the limiting effect of the convex strip shape and the thrust of the rotation of the convex strips, the concave strips will drive the clamping block to move to the outside of the outer shell, thereby leaving the placement slot; at this time, the operator can put the collected sample into the placement slot, so that the sample roots are placed in the nutrient box.

[0037] After the sample is placed, the operator controls the output shaft of the driving part to rotate counterclockwise through the controller. At this time, the clamping blocks move toward the sample together to clamp the sample. At the same time, the outer shell and the top cover move closer to each other, so that the entire device is closed and a closed environment is maintained, thereby protecting the sample.

[0038] Furthermore, one end of the clamping block close to the placement slot is fixedly connected to an airbag.

[0039] Beneficial Effects: During the clamping process, the airbag can adapt to samples of different sizes and sizes, making it easier for operators to transport large quantities of samples and samples with different root diameters. It also prevents mechanical damage to the plants during the clamping process, improving sample quality.

[0040] Furthermore, the top cover is hollow inside and a protective layer is fixedly connected to the inner wall. A photosynthetic rate meter, a transpiration rate meter, a plurality of heating wires and a plurality of lighting lamps are fixedly connected inside the top cover.

[0041] Beneficial Effects: During transport, the protective layer significantly reduces mechanical damage to the sample caused by collisions with the interior of the device, improving sample quality. The photosynthetic rate meter and transpiration rate meter are used to measure the sample's photosynthetic rate and transpiration rate, respectively. The heating wire is used to adjust the temperature during transport, and the lighting is used to adjust the lighting during transport.

[0042] Furthermore, a one-way valve is fixedly connected to the side wall of the nutrient tank, and the controller is used to control the opening and closing of the one-way valve.

[0043] Beneficial effects: During the transportation process, the operator can open the one-way valve through the controller to allow air to enter the nutrient box to optimize the transportation environment of the sample.

[0044] Furthermore, the clamping blocks are all stepped.

[0045] Beneficial effects: The stepped design can reduce the material consumption and weight of the clamping block, thereby reducing the cost and shipping weight of the device while maintaining the clamping area of the clamping block.

[0046] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of a method for measuring nitrogen content in rice leaves based on deep learning in an embodiment of the present invention.

[0048] Figure 2 This is an axonometric diagram of a device for measuring nitrogen content in rice leaves based on deep learning in an embodiment of the present invention.

[0049] Figure 3 This is an axonometric view of the top cover in an embodiment of the present invention.

[0050] Figure 4 It is a front view of the toothed disc in the embodiment of the present invention.

[0051] Figure 5 2 is a top view of the toothed disc in an embodiment of the present invention.

[0052] Figure 6 It is a front view of the clamping block in an embodiment of the present invention.

[0053] The figure marks in the drawings of the specification include: 1. shell; 2. clamping block; 201. air bag; 202. concave strip; 3. rotating rod; 301. internal gear; 4. external gear; 5. rack; 6. top cover; 7. limit rod; 8. toothed disc; 801. convex strip; 9. nutrition box. DETAILED DESCRIPTION

[0054] The following is further described in detail through specific implementation methods:

[0055] Example 1:

[0056] As attached Figure 1 As shown: A method for measuring nitrogen content in rice leaves based on deep learning, comprising the following steps:

[0057] S1, sample collection: Collect several rice plants as samples and soak the sample roots in nutrient solution.

[0058] S2, sample transport: adjust the transport parameters of the transport environment according to the current environment, and transport the sample to the laboratory; record the activity indicators of the sample during the transport process.

[0059] S3, sample processing: After being transported to the laboratory, the samples were washed with clean water and the dried and damaged rice leaves were removed.

[0060] S4, sample detection: performing spectral detection on the treated rice leaves using a spectrometer to obtain spectral data of the rice leaves.

[0061] S5, Intelligent Analysis: Analyze rice leaf spectral data using a deep learning model to determine the current nitrogen content. The deep learning module is trained using spectral data from several rice leaves. A compensation model is used to compensate for the current nitrogen content of the rice leaves based on transport parameters and activity indicators to determine the actual nitrogen content. The compensation model is trained based on linear variation data from several transport parameters and activity indicators. Extensive training of the deep learning and compensation models enables intelligent and automated sample analysis, resulting in rapid and accurate carbon content analysis. This approach offers higher efficiency than manual analysis.

[0062] Among them, the transport parameters include the temperature, humidity and light of the transport environment, and the activity indicators include the photosynthetic rate, transpiration rate and stomatal conductance during sample transport.

[0063] The formula for calculating photosynthetic rate is as follows:

[0064] V O2 =(Vx-Vt) / M (1).

[0065] Among them, V O2represents the oxygen generation rate, Vx represents the oxygen release amount, Vt represents the initial oxygen concentration), and M represents the leaf area.

[0066] The transpiration rate calculation formula is as follows:

[0067] Tr = Ox / (Mt×T) (2).

[0068] Among them, Tr represents transpiration rate, Ox represents transpiration water loss, Mt represents unit leaf area, and T represents time.

[0069] The formula for calculating stomatal conductance is as follows:

[0070] gs = E / (1.6×Vpd) (2).

[0071] Where gs is the stomatal conductance, E is the transpiration rate, Vpd is the vapor pressure deficit at the leaf-air interface, and 1.6 is an empirical constant.

[0072] The specific implementation process is as follows: First, the operator collects several rice samples in the field, soaks the roots of all samples in nutrient solution, and adjusts the temperature, humidity and light of the transportation environment according to the appropriate temperature, humidity and light of the samples, thereby increasing the activity of the rice during transportation and reducing the loss of its carbon content. In turn, the transportation environment of the rice is optimized, the quality of the samples transported to the laboratory is improved, and the accuracy of the carbon content test results is further improved.

[0073] During the transportation process, the operator records the photosynthetic rate and transpiration rate of the samples in real time, and then uses the formula to calculate the photosynthetic rate, transpiration rate and stomatal conductance of the samples during transportation. This can provide good assistance for the subsequent deep learning model and compensation model nitrogen content analysis, and improve the accuracy of the analysis.

[0074] When the samples were transported to the laboratory, the temperature, humidity, and lighting conditions were adjusted to the appropriate range for the samples to maintain environmental consistency. The samples were then rinsed with clean water and any dried or damaged rice leaves removed. After this treatment, the rice leaves were spectrally analyzed using a spectrometer to obtain spectral data.

[0075] Finally, the deep learning model was used to analyze the spectral data of rice leaves to obtain the current nitrogen content of the rice leaves. Then, based on the compensation model, the current nitrogen content of the rice leaves was compensated according to the temperature, humidity and light of the transportation environment, as well as the photosynthetic rate, transpiration rate and stomatal conductance of the rice during transportation to obtain the actual nitrogen content of the rice leaves.

[0076] The present invention analyzes the spectral data of rice leaves through a deep learning model, and can quickly and accurately estimate the nitrogen content of rice leaves. Compared with the existing technology, the present invention further considers the temperature, humidity and light during the transportation process as well as the photosynthetic rate, transpiration rate and stomatal conductance of rice through a compensation model, and further corrects the estimated nitrogen content, thereby improving the accuracy of detection.

[0077] Example 2:

[0078] As attached Figure 2-Figure 6 As shown: Different from the above embodiment, the device for measuring the nitrogen content of rice leaves based on deep learning includes a controller, a shell 1 and a top cover 6; Figure 2 As shown, a plurality of clamping blocks 2 are slidably fitted on the outer shell 1; a plurality of rotating rods 3 are rotatably fitted on the outer wall of the outer shell 1, and the rotating rods 3 all penetrate the side wall of the outer shell 1. The rotating rods 3 are located at one end outside the outer shell 1 and one end inside the outer shell 1 and are respectively bolted to an external gear 4 and an internal gear 301, and the external gear 4 is meshed with a rack 5.

[0079] like Figure 3 As shown, the upper part of the rack 5 is fixedly connected with the top cover 6 by bolts, a number of limit holes are opened on the top cover 6, a number of limit rods 7 are welded on the top of the shell 1, and the limit rods 7 are slidably matched with the limit holes adjacent to them; a placement groove is opened in the middle of the shell 1; the bottom of the shell 1 is fixedly connected with a nutrient box 9 by bolts; the nutrient solution is stored in the nutrient box 9.

[0080] like Figure 4 and Figure 5 As shown, the housing 1 is rotatably fitted with a toothed disc 8, the internal gears 301 are all meshed with the toothed disc 8, and a spiral convex strip 801 is welded on the top of the toothed disc 8; Figure 6 As shown, the bottom of the clamping block 2 is welded with concave strips 202, and the concave strips 202 are slidably matched with the convex strips 801; a driving component is fixedly connected with bolts in the housing 1. In this embodiment, the driving component is a servo motor, and the output shaft of the servo motor is fixedly connected with the adjacent internal gear 301 by coaxial bolts; the controller is used to control the opening and closing of the servo motor.

[0081] The specific implementation process is as follows: During the transportation process, the operator can put the collected samples into the placement tank and put the sample roots into the nutrient box 9.

[0082] by Figure 3 and Figure 4 For example, after the sample collection is completed, the operator controls the servo motor output shaft to rotate clockwise through the controller, and the servo motor drives the adjacent internal gear 301 to rotate. Since the internal gear 301 is engaged with the gear plate 8, the internal gear 301 will drive the gear plate 8 to rotate, and the gear plate 8 will drive the remaining internal gears 301 to rotate.

[0083] Since the inner gear 301 and the rotating rod 3 are fixedly connected by bolts, the inner gear 301 drives the rotating rod 3 to rotate, and the rotating rod 3 drives the outer gear 4 to rotate. Since the outer gear 4 is engaged with the adjacent rack 5, the outer gear 4 drives the meshing rack 5 to move upward.

[0084] by Figure 2 and Figure 3 For example, when all the racks 5 move upward together, since the racks 5 and the top cover 6 are fixedly connected by bolts, the racks 5 will drive the top cover 6 to move upward, thereby causing the housing 1 and the top cover 6 to move away from each other.

[0085] At the same time, Figure 5 and Figure 6 For example, due to the rotation of the toothed disc 8, the ridge 801 will also rotate accordingly. Since the ridge 801 is spiral, the concave 202 slides with the ridge 801. Therefore, since the shapes of the ridge 801 and the concave 202 fit each other, a limiting effect will be produced. At the same time, with the thrust of the rotation of the ridge 801, the concave 202 will drive the clamping block 2 to move to the outside of the shell 1, thereby leaving the placement slot; at this time, the operator can put the collected sample into the placement slot, so that the sample root system is placed in the nutrient box 9.

[0086] After the sample is placed, the operator controls the servo motor output shaft to rotate counterclockwise through the controller. At this time, the clamping blocks 2 move toward the sample together to clamp the sample. At the same time, the housing 1 and the top cover 6 move closer to each other, so that the entire device is closed and a closed environment is maintained, thereby protecting the sample.

[0087] by Figure 2 and Figure 3 For example, during this process, the limit rod 7 will slide in the limit hole. Due to the limiting effect of the limit rod 7, the top cover 6 will not rotate, thereby keeping the rack 5 and the adjacent outer gear 4 engaged; and due to the self-locking ability of the servo motor, the outer gear 4 will only rotate when the controller is started, so that the distance between the top cover 6 and the outer shell 1 can only be increased by the controller for regulation, and will not be affected by gravity, thereby ensuring the stable operation of the device.

[0088] Example 3:

[0089] As attached Figure 1 and Figure 6 As shown: Different from the above embodiment, an air bag 201 is fixedly bonded to one end of the clamping block 2 close to the placement groove.

[0090] The specific implementation process is as follows: During the clamping process, the airbag 201 can adapt to samples of different volumes and sizes, making it easier for operators to transport large quantities of samples and samples with different root diameters. It can also avoid mechanical damage to the plants during the clamping process, thereby improving the quality of the samples.

[0091] Example 4:

[0092] As attached Figure 3 As shown: Different from the above embodiment, the top cover 6 is hollow inside and a protective layer is fixedly bonded to the inner wall. A photosynthetic rate meter, a transpiration rate meter, several heating wires and several lighting lamps are fixedly connected to the top cover 6 by screws.

[0093] The specific implementation process is as follows: During transport, the protective layer significantly reduces mechanical damage to the sample caused by collisions with the interior of the device, thereby improving sample quality. A photosynthetic rate meter and a transpiration rate meter are used to measure the sample's photosynthetic rate and transpiration rate, respectively. A heating filament is used to regulate temperature during transport, and a lighting lamp is used to adjust the lighting during transport.

[0094] Example 5:

[0095] As attached Figure 2 As shown: the side wall of the nutrient box 9 is fixed with bolts and connected with a one-way valve, and the controller is used to control the opening and closing of the one-way valve.

[0096] The specific implementation process is as follows: During the transportation process, the operator can open the one-way valve through the controller to let air into the nutrient box 9, thereby optimizing the transportation environment of the sample.

[0097] Example 6:

[0098] As attached Figure 6 As shown: the clamping blocks 2 are all stepped.

[0099] The specific implementation process is as follows: the stepped design can reduce the consumption of materials for making the clamping block 2, while reducing the weight of the clamping block 2, thereby reducing the cost and shipping weight of the device. While reducing the volume and gravity of the clamping block 2, it will not affect the clamping area of the clamping block 2, ensuring the stability of the clamping block 2 when clamping the sample.

[0100] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A method for measuring nitrogen content in rice leaves based on deep learning, characterized in that: The following steps are involved: S1, sample collection: Collect several rice plants as samples and immerse the sample roots in nutrient solution; S2, sample transport: the transport device adjusts the transport parameters of the transport environment according to the current environment and transports the sample to the laboratory; records the activity index of the sample during the transport process; the transport parameters include the temperature, humidity and light of the transport environment; the activity index includes the photosynthetic rate, transpiration rate and stomatal conductance during the sample transport process; the transport device includes a controller, a shell (1) and a top cover (6), and a plurality of clamping blocks (2) are slidably fitted on the shell (1); the top cover (6) is hollow inside and a protective layer is fixedly connected to the inner wall; a photosynthetic rate meter, a transpiration rate meter, a plurality of heating wires and a plurality of lighting lamps are fixedly connected inside the top cover (6); S3, sample processing: After being transported to the laboratory, the samples were washed with clean water and dried and damaged rice leaves were removed; S4, sample detection: performing spectral detection on the treated rice leaves using a spectrometer to obtain spectral data of the rice leaves; S5, intelligent analysis: Analyze the spectral data of rice leaves based on the deep learning model to obtain the current nitrogen content of rice leaves; Based on the compensation model, the current nitrogen content of rice leaves is compensated according to the transport parameters and activity indicators to obtain the actual nitrogen content of rice leaves.

2. The method for measuring nitrogen content in rice leaves based on deep learning according to claim 1, characterized in that: In S5, the deep learning module is trained based on the spectral data of several rice leaves; the compensation model is trained based on the linear change data of several transport parameters and activity indicators; The formula for calculating photosynthetic rate is as follows: In O2 =(Vx-Vt) / M (1); Among them, V O2 represents the oxygen generation rate, Vx represents the oxygen release amount, Vt represents the initial oxygen concentration, and M represents the leaf area; The transpiration rate calculation formula is as follows: Tr =Ox / (Mt×T) (2); Where Tr represents transpiration rate, Ox represents transpiration water loss, Mt represents unit leaf area, and T represents time; The formula for calculating stomatal conductance is as follows: gs=E / (1.6×Vpd) (2); Where gs is the stomatal conductance, E is the transpiration rate, Vpd is the vapor pressure deficit at the leaf-air interface, and 1.6 is an empirical constant.

3. The method for measuring nitrogen content in rice leaves based on deep learning according to claim 2, characterized in that: The outer wall of the housing (1) is rotatably engaged with a plurality of rotating rods (3), and the rotating rods (3) all penetrate the side wall of the housing (1). The rotating rods (3) are respectively fixedly connected to an external gear (4) and an internal gear (301) at one end outside the housing (1) and at one end inside the housing (1). The external gears (4) are meshed with racks (5), and the upper parts of the racks (5) are fixedly connected to the top cover (6); A plurality of limiting holes are formed on the top cover (6), and a plurality of limiting rods (7) are fixedly connected to the top of the housing (1), and the limiting rods (7) are all slidably engaged with the limiting holes adjacent thereto; A placement slot is provided in the middle of the housing (1); a nutrient box (9) is fixedly connected to the bottom of the housing (1); and a nutrient solution is stored in the nutrient box (9); A toothed disc (8) is rotatably fitted in the housing (1), and the internal gears (301) are all meshed with the toothed disc (8). A spiral convex strip (801) is fixedly connected to the top of the toothed disc (8), and a concave strip (202) is fixedly connected to the bottom of the clamping block (2), and the concave strip (202) is slidably fitted with the convex strip (801). A driving member is fixedly connected to the housing (1), and an output shaft of the driving member is coaxially fixedly connected to an adjacent internal gear (301); and a controller is used to control the opening and closing of the driving member.

4. The method for measuring nitrogen content in rice leaves based on deep learning according to claim 3, characterized in that: The clamping block (2) is fixedly connected to an air bag (201) at one end close to the placement groove.

5. The method for measuring nitrogen content in rice leaves based on deep learning according to claim 4, characterized in that: A one-way valve is fixedly connected to the side wall of the nutrient box (9), and the controller is used to control the opening and closing of the one-way valve.

6. The method for measuring nitrogen content in rice leaves based on deep learning according to claim 5, characterized in that: The clamping blocks (2) are all stepped.

Images