Greenhouse environment monitoring and adjusting method and system based on internet of things

Abstract

The application provides a greenhouse environment monitoring and adjusting method and system based on the Internet of Things, which performs partition light supplement and irrigation management on the inside of the greenhouse, so that crops with poor growth are preferentially provided with cultivation care, and meanwhile, the composition of the atmosphere inside the greenhouse can be adjusted to improve the efficiency of photosynthesis of crops and ensure the reliability and sustainability of growth management of crops inside the greenhouse.

Description

Technical Field

[0001] This invention relates to the technical field of crop planting management, and in particular to a method and system for monitoring and adjusting the greenhouse environment based on the Internet of Things. Background Technology

[0002] Greenhouses are used for large-scale crop cultivation, providing stable and suitable growing conditions for crops throughout the year. Existing greenhouses are equipped with various sensors, such as temperature and humidity sensors, to monitor the internal environment in real time. This allows for adaptive adjustments to environmental parameters and regular irrigation according to predetermined patterns. However, these management methods rely on single-area temperature or humidity regulation and fixed irrigation patterns, failing to provide independent management of different areas within the greenhouse. This reduces the reliability and sustainability of crop growth management within the greenhouse. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method and system for monitoring and adjusting the greenhouse environment based on the Internet of Things (IoT). It captures and analyzes crop images inside the greenhouse to identify areas where crop growth does not meet predetermined conditions. Based on the natural light conditions of these areas, it obtains the vertical light radiation status information of the crops, thereby adjusting the illumination parameters for supplemental lighting. Based on the surface condition information of the crop stems and leaves in these areas, it adjusts the irrigation parameters for irrigation. Based on the atmospheric composition information inside the greenhouse, it determines whether the atmospheric environment inhibits normal crop growth, thereby adjusting the atmospheric environmental parameters inside the greenhouse. This allows for zoned supplemental lighting and irrigation management within the greenhouse, prioritizing cultivation care for crops with poor growth. Simultaneously, it can adjust the composition of the atmospheric environment inside the greenhouse, improving the efficiency of photosynthesis and ensuring the reliability and sustainability of crop growth management within the greenhouse.

[0004] This invention provides a method for monitoring and adjusting the environment of a greenhouse based on the Internet of Things, comprising the following steps:

[0005] Step S1: Take pictures of the inside of the greenhouse to obtain crop images, and analyze and process the crop images to obtain crop growth status information inside the greenhouse; based on the crop growth status information, determine the areas inside the greenhouse where the crop growth does not meet the predetermined conditions.

[0006] Step S2: Obtain the natural light status information of the area through the Internet of Things to determine the vertical light radiation status information of the crops in the area; adjust the irradiation parameters for supplemental lighting of the crops in the area based on the light radiation status information.

[0007] Step S3: Analyze and process the crop images of the area to determine the surface condition information of the crop stems and leaves in the area; adjust the irrigation parameters for irrigating the crops in the area based on the surface condition information of the crop stems and leaves.

[0008] Step S4: Obtain atmospheric composition information inside the greenhouse through the Internet of Things to determine whether the current atmospheric environment inhibits the normal growth of crops; adjust the atmospheric environment parameters inside the greenhouse according to the determination result.

[0009] Further, in step S1, images of the inside of the greenhouse are taken to obtain crop images, and these images are analyzed and processed to obtain crop growth status information inside the greenhouse. Based on this crop growth status information, areas inside the greenhouse where crop growth does not meet predetermined conditions are identified, including:

[0010] The interior of the greenhouse is scanned and photographed to obtain visible light images of the crops; the stem and leaf outline information of the crops is extracted from the visible light images of the crops, and the stem and leaf growth area of ​​the crops is obtained based on the stem and leaf outline information, which is used as the growth status information;

[0011] Based on the stem and leaf growth area of ​​the crops, the ratio between the stem and leaf growth area of ​​all crops in each pre-divided grid area inside the greenhouse and the area of ​​the grid area is obtained; if the ratio is less than a preset ratio threshold, the corresponding grid area is determined to belong to the area of ​​poor crop growth; otherwise, the corresponding grid area is determined not to belong to the area of ​​poor crop growth.

[0012] Further, in step S2, natural light status information of the area is acquired via the Internet of Things to determine the vertical light radiation status information of the crops in the area; based on the light radiation status information, the irradiation parameters for supplemental lighting of the crops in the area are adjusted, including:

[0013] Information on changes in natural light intensity in the vertical direction of the area with poor crop growth is obtained through the Internet of Things; the crop images corresponding to the area with poor crop growth are analyzed and processed to obtain information on changes in leaf coverage area in the vertical direction of the crop in the area with poor crop growth.

[0014] Based on the natural light intensity change information and the leaf coverage area change information, the light intensity value received by a unit area of ​​leaves at each preset distance along the vertical direction of the crop in the area with poor crop growth is obtained, and this is used as the light radiation status information.

[0015] If the light intensity value at a certain position in the vertical direction is less than or equal to the preset intensity threshold, the artificial lighting intensity at the corresponding position is increased; otherwise, the current artificial lighting intensity at the corresponding position remains unchanged.

[0016] Furthermore, step S2 also includes:

[0017] If multiple crop locations have light intensity values ​​less than or equal to a preset intensity threshold, then wide-angle artificial supplemental lighting is applied. The location points and irradiation radius of the artificial supplemental lighting are determined based on the distribution of the crop locations. The process is as follows:

[0018] Step S201: Establish a Cartesian coordinate system on the ground of the greenhouse area. Each crop location point is represented by coordinates. Using the following formula (1), determine whether it is necessary to provide wide-angle artificial lighting based on the distribution of crop location points with light intensity values ​​less than or equal to a preset intensity threshold.

[0019]

[0020] In the above formula (1), E represents the control value of artificial supplemental lighting entering the wide-angle field; L represents the diameter of the maximum circular range that can be illuminated by the artificial supplemental lighting in the wide-angle field; k represents the ratio between the unit line segment of the actual plane and the unit line segment of the established coordinate system; [X(a),Y(a)] represents the coordinates of the a-th crop position where the light intensity value is less than or equal to the preset intensity threshold; [X(b),Y(b)] represents the coordinates of the b-th crop position where the light intensity value is less than or equal to the preset intensity threshold; n represents the total number of crop positions where the light intensity value is less than or equal to the preset intensity threshold. This means taking the values ​​of a from 1 to n, and substituting the values ​​of b from 1 to n into the parentheses to get the maximum value inside the parentheses;

[0021] If E=1, then control the artificial lighting to enter the wide-angle field and proceed to the next step S201;

[0022] If E=0, then artificial lighting will not be used to illuminate the wide-angle area; instead, independent artificial lighting will be used to illuminate the corresponding positions according to their positional relationships.

[0023] Step S202: If wide-angle artificial lighting is required, the control lighting position point for artificial lighting is determined using the following formula (2) based on the distribution of the crop locations.

[0024]

[0025] In the above formula (2), (x0,y0) represents the coordinates of the controlled illumination position of artificial supplemental lighting; G(a) represents the light intensity value of the a-th crop position where the light intensity value is less than or equal to the preset intensity threshold;

[0026] Step S203: Using the following formula (3), determine the control radius of the artificial lighting based on the location of the artificial lighting and the distribution of the crop locations.

[0027]

[0028] In the above formula (3), R represents the control irradiation radius of artificial supplemental lighting; This means substituting the values ​​of 'a' from 1 to 'n' into the parentheses to obtain the maximum value within the parentheses;

[0029] If a wide-angle artificial supplementary lighting is applied, the lighting position is controlled at (x0, y0), and the lighting radius is controlled at R. This allows a single large light source to provide wide-angle artificial supplementary lighting to the greenhouse area.

[0030] Further, in step S3, the crop image of the area is analyzed and processed to determine the surface condition information of the crop stems and leaves in the area; based on the surface condition information of the crop stems and leaves, the irrigation parameters for irrigating the crops in the area are adjusted, including:

[0031] The pixel color distribution information of the crop image in the area with poor crop growth is extracted, and the area of ​​the withered area on the surface of the crop stem and leaf is obtained based on the pixel color distribution information.

[0032] If the area of ​​the withered region accounts for a greater than or equal to a preset area percentage threshold on the entire surface of the crop stems and leaves, the drip irrigation flow rate and / or drip irrigation duration for the crop will be increased.

[0033] Furthermore, in step S4, atmospheric composition information inside the greenhouse is acquired via the Internet of Things to determine whether the current atmospheric environment inhibits normal crop growth; based on the determination result, the atmospheric environmental parameters inside the greenhouse are adjusted, including:

[0034] The carbon dioxide concentration information in the indoor atmosphere of the greenhouse is obtained through the Internet of Things; based on the carbon dioxide concentration information, it is determined whether the carbon dioxide concentration in the indoor atmosphere of the greenhouse during the daytime is lower than a preset concentration threshold; if it exceeds the threshold, it is determined that the atmospheric environment during the daytime inhibits the normal growth of crops; if it does not exceed the threshold, it is determined that the atmospheric environment during the daytime does not inhibit the normal growth of crops.

[0035] When the atmospheric environment inhibits the normal growth of crops during the daytime, the amount of carbon dioxide transported into the greenhouse increases.

[0036] This invention also provides an Internet of Things-based greenhouse environment monitoring and adjustment system, comprising:

[0037] The image capture and analysis module is used to capture images of the inside of the greenhouse to obtain crop images, and to analyze and process the crop images to obtain crop growth status information inside the greenhouse; based on the crop growth status information, it determines the areas inside the greenhouse where the crop growth does not meet the predetermined conditions.

[0038] The light information collection and analysis module is used to acquire natural light status information of the area through the Internet of Things, so as to determine the light radiation status information of crops in the vertical direction in the area.

[0039] The supplemental lighting adjustment module is used to adjust the irradiation parameters for supplemental lighting operations on crops in the area based on the light radiation status information.

[0040] The crop stem and leaf state determination module is used to analyze and process crop images of the area to determine the surface state information of crop stems and leaves in the area.

[0041] The irrigation adjustment module is used to adjust the irrigation parameters for irrigating crops in the area based on the surface condition information of the crop stems and leaves.

[0042] The atmospheric composition information collection and analysis module is used to obtain atmospheric composition status information inside the greenhouse through the Internet of Things, so as to determine whether the current atmospheric environment inhibits the normal growth of crops.

[0043] The atmospheric environment adjustment module is used to adjust the atmospheric environment parameters inside the greenhouse based on the judgment results.

[0044] Furthermore, the image capture and analysis module captures images of the inside of the greenhouse to obtain crop images, and analyzes and processes these images to obtain crop growth status information inside the greenhouse. Based on this crop growth status information, it identifies areas inside the greenhouse where crop growth does not meet predetermined conditions, including:

[0045] The interior of the greenhouse is scanned and photographed to obtain visible light images of the crops; the stem and leaf outline information of the crops is extracted from the visible light images of the crops, and the stem and leaf growth area of ​​the crops is obtained based on the stem and leaf outline information, which is used as the growth status information;

[0046] Based on the stem and leaf growth area of ​​the crops, the ratio between the stem and leaf growth area of ​​all crops in each pre-divided grid area inside the greenhouse and the area of ​​the grid area is obtained; if the ratio is less than a preset ratio threshold, the corresponding grid area is determined to belong to the area of ​​poor crop growth; otherwise, the corresponding grid area is determined not to belong to the area of ​​poor crop growth.

[0047] Furthermore, the light information collection and analysis module acquires natural light status information of the area through the Internet of Things, thereby determining the vertical light radiation status information of crops in the area, including:

[0048] Information on changes in natural light intensity in the vertical direction of the area with poor crop growth is obtained through the Internet of Things; the crop images corresponding to the area with poor crop growth are analyzed and processed to obtain information on changes in leaf coverage area in the vertical direction of the crop in the area with poor crop growth.

[0049] Based on the natural light intensity change information and the leaf coverage area change information, the light intensity value received by a unit area of ​​leaves at each preset distance along the vertical direction of the crop in the area with poor crop growth is obtained, and this is used as the light radiation status information.

[0050] The supplemental lighting adjustment module adjusts the irradiation parameters for supplemental lighting operations on crops in the area based on the light radiation status information, including:

[0051] If the light intensity value at a certain position in the vertical direction is less than or equal to the preset intensity threshold, the artificial lighting intensity at the corresponding position is increased; otherwise, the current artificial lighting intensity at the corresponding position remains unchanged.

[0052] Furthermore, the crop stem and leaf state determination module analyzes and processes the crop image of the area to determine the surface state information of the crop stems and leaves in the area, including:

[0053] The pixel color distribution information of the crop image in the area with poor crop growth is extracted, and the area of ​​the withered area on the surface of the crop stem and leaf is obtained based on the pixel color distribution information.

[0054] The irrigation adjustment module adjusts the irrigation parameters for irrigating crops in the area based on the crop stem and leaf surface condition information, including:

[0055] If the area of ​​the withered region accounts for a greater than or equal to a preset area percentage threshold on the entire surface of the crop stems and leaves, the drip irrigation flow rate and / or drip irrigation duration for the crop will be increased.

[0056] Furthermore, the atmospheric composition information collection and analysis module acquires atmospheric composition status information inside the greenhouse through the Internet of Things to determine whether the current atmospheric environment inhibits the normal growth of crops, including:

[0057] The carbon dioxide concentration information in the indoor atmosphere of the greenhouse is obtained through the Internet of Things; based on the carbon dioxide concentration information, it is determined whether the carbon dioxide concentration in the indoor atmosphere of the greenhouse during the daytime is lower than a preset concentration threshold; if it exceeds the threshold, it is determined that the atmospheric environment during the daytime inhibits the normal growth of crops; if it does not exceed the threshold, it is determined that the atmospheric environment during the daytime does not inhibit the normal growth of crops.

[0058] The atmospheric environment adjustment module adjusts the atmospheric environment parameters inside the greenhouse based on the judgment result, including:

[0059] When the atmospheric environment inhibits the normal growth of crops during the daytime, the amount of carbon dioxide transported into the greenhouse increases.

[0060] Compared to existing technologies, this IoT-based greenhouse environment monitoring and adjustment method and system captures and analyzes crop images inside the greenhouse to identify areas where crop growth does not meet predetermined conditions. Based on the natural light conditions of these areas, it obtains information on the vertical light radiation received by the crops, thereby adjusting the illumination parameters for supplemental lighting. Based on the surface condition of the crop stems and leaves in these areas, it adjusts the irrigation parameters for irrigation. Based on the atmospheric composition information inside the greenhouse, it determines whether the atmospheric environment inhibits normal crop growth, thereby adjusting the atmospheric environmental parameters. This allows for zoned supplemental lighting and irrigation management within the greenhouse, prioritizing the cultivation of weaker crops. It also adjusts the composition of the atmospheric environment inside the greenhouse, improving the efficiency of photosynthesis and ensuring the reliability and sustainability of crop growth management within the greenhouse.

[0061] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0062] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 A flowchart illustrating the IoT-based greenhouse environment monitoring and adjustment method provided by this invention.

[0065] Figure 2 This is a schematic diagram of the structure of the IoT-based greenhouse environment monitoring and adjustment system provided by the present invention. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] See Figure 1 This is a flowchart illustrating the IoT-based greenhouse environment monitoring and adjustment method provided in an embodiment of the present invention. The IoT-based greenhouse environment monitoring and adjustment method includes the following steps:

[0068] Step S1: Take pictures of the inside of the greenhouse to obtain crop images, and analyze and process the crop images to obtain crop growth status information inside the greenhouse; based on the crop growth status information, determine the areas inside the greenhouse where the crop growth does not meet the predetermined conditions.

[0069] Step S2: Obtain the natural light status information of the area through the Internet of Things to determine the vertical light radiation status information of the crops in the area; adjust the irradiation parameters for supplemental lighting of the crops in the area based on the light radiation status information.

[0070] Step S3: Analyze and process the crop images of the area to determine the surface condition information of the crop stems and leaves in the area; adjust the irrigation parameters for irrigation operations on the crops in the area based on the surface condition information of the crop stems and leaves.

[0071] Step S4: Obtain atmospheric composition information inside the greenhouse through the Internet of Things to determine whether the current atmospheric environment inhibits the normal growth of crops; adjust the atmospheric environment parameters inside the greenhouse according to the result of the determination.

[0072] The beneficial effects of the above technical solution are as follows: This IoT-based greenhouse environment monitoring and adjustment method captures and analyzes crop images inside the greenhouse to identify areas where crop growth does not meet predetermined conditions; based on the natural light status information of these areas, it obtains the vertical light radiation status information of the crops, thereby adjusting the irradiation parameters for supplemental lighting operations; based on the surface status information of the crop stems and leaves in these areas, it adjusts the irrigation parameters for irrigation operations; based on the atmospheric composition information inside the greenhouse, it determines whether the atmospheric environment inhibits normal crop growth, thereby adjusting the atmospheric environment parameters inside the greenhouse. This allows for zoned supplemental lighting and irrigation management within the greenhouse, prioritizing cultivation care for crops with poor growth. Simultaneously, it adjusts the composition of the atmospheric environment inside the greenhouse, improving the efficiency of photosynthesis and ensuring the reliability and sustainability of crop growth management within the greenhouse.

[0073] Preferably, in step S1, images of the inside of the greenhouse are taken to obtain crop images, and these images are analyzed and processed to obtain information on the crop growth status inside the greenhouse. Based on this crop growth status information, areas inside the greenhouse where the crop growth does not meet predetermined conditions are identified, including:

[0074] The interior of the greenhouse is scanned and photographed to obtain visible light images of the crop; the outline information of the crop's stems and leaves is extracted from the visible light images, and the growth area of ​​the crop's stems and leaves is obtained based on the outline information, which is used as the growth status information of the crop.

[0075] Based on the stem and leaf growth area of ​​the crops, the ratio between the stem and leaf growth area of ​​all crops in each pre-divided grid area inside the greenhouse and the area of ​​the grid area is obtained; if the ratio is less than the preset ratio threshold, the corresponding grid area is determined to belong to the area of ​​poor crop growth; otherwise, the corresponding grid area is determined not to belong to the area of ​​poor crop growth.

[0076] The beneficial effects of the above technical solution are as follows: In practical work, distributed cameras can be installed inside the greenhouse. These distributed cameras are connected to the Internet of Things (IoT). The distributed cameras scan and photograph the inside of the greenhouse, and collect the captured visible light images of crops through the IoT. The visible light images of crops are then analyzed and processed to obtain the ratio between the stem and leaf growth area of ​​all crops in each pre-divided grid area inside the greenhouse and the area of ​​the grid area. The larger the ratio, the more vigorous the crop growth in the corresponding grid area. When the ratio is less than a preset ratio threshold, it indicates that the crop growth status in the corresponding grid area is poor. This allows for the differentiation of different grid areas inside the greenhouse.

[0077] Preferably, in step S2, the natural light status information of the area is acquired through the Internet of Things to determine the vertical light radiation status information of the crops in the area; based on the light radiation status information, the irradiation parameters for supplemental lighting of the crops in the area are adjusted, including:

[0078] The Internet of Things (IoT) is used to obtain information on the vertical changes in natural light intensity in areas where crop growth is poor; the crop images corresponding to these areas are analyzed and processed to obtain information on the vertical changes in leaf coverage area of ​​the crops in these areas.

[0079] Based on the information on changes in natural light intensity and changes in leaf coverage area, the light intensity value received by a unit area of ​​leaves at each preset distance along the vertical direction of the crop in the area with poor crop growth is obtained, and this is used as the information on the state of light radiation.

[0080] If the light intensity value at a certain position in the vertical direction is less than or equal to the preset intensity threshold, the artificial lighting intensity at the corresponding position is increased; otherwise, the current artificial lighting intensity at the corresponding position remains unchanged.

[0081] The beneficial effects of the above technical solution are as follows: In practical work, several light sensors can be sequentially and spaced along the vertical direction in each grid area inside the greenhouse to form a distributed light sensor, which is connected to the Internet of Things (IoT). The distributed light sensor detects the natural light intensity in the vertical direction for each grid area. Since crops are usually placed vertically for cultivation inside the greenhouse, they will block natural light, causing changes in the natural light intensity along the vertical direction in each grid area. Generally speaking, the lower the position along the vertical direction, the lower the corresponding natural light intensity. Thus, the lower the position along the vertical direction, the less natural light intensity it receives. Through the above method, the changes in the light radiation status information of crops at different positions along the vertical direction in areas with poor crop growth can be quantitatively judged. This allows for increasing the artificial lighting intensity in areas with excessively low vertical light intensity, thereby ensuring the uniformity of light for crops in areas with poor growth.

[0082] Preferably, step S2 further includes:

[0083] If multiple crop locations have light intensity values ​​less than or equal to a preset intensity threshold, then wide-angle artificial supplemental lighting is applied. The location and radius of the artificial supplemental lighting are determined based on the distribution of the crop locations. The process is as follows:

[0084] Step S201: Establish a Cartesian coordinate system on the ground of the greenhouse area. Each crop location point is represented by coordinates. Using the following formula (1), determine whether it is necessary to provide wide-angle artificial lighting based on the distribution of crop location points with light intensity values ​​less than or equal to a preset intensity threshold.

[0085]

[0086] In the above formula (1), E represents the control value of artificial supplemental lighting entering the wide-angle field; L represents the diameter of the maximum circular range that can be illuminated by the artificial supplemental lighting of the wide-angle field; k represents the ratio between the unit line segment of the actual plane and the unit line segment of the established coordinate system; [X(a),Y(a)] represents the coordinates of the a-th crop position where the light intensity value is less than or equal to the preset intensity threshold; [X(b),Y(b)] represents the coordinates of the b-th crop position where the light intensity value is less than or equal to the preset intensity threshold; n represents the total number of crop positions where the light intensity value is less than or equal to the preset intensity threshold. This means taking the values ​​of a from 1 to n, and substituting the values ​​of b from 1 to n into the parentheses to get the maximum value inside the parentheses;

[0087] If E=1, then control the artificial lighting to enter the wide-angle field and proceed to the next step S201;

[0088] If E=0, then artificial lighting will not be used to illuminate the wide-angle area; instead, independent artificial lighting will be used to illuminate the corresponding positions according to their positional relationships.

[0089] Step S202: If wide-angle artificial lighting is required, use the following formula (2) to determine the control lighting position point based on the distribution of the crop location.

[0090]

[0091] In the above formula (2), (x0,y0) represents the coordinates of the controlled illumination position of artificial supplemental lighting; G(a) represents the light intensity value of the a-th crop position where the light intensity value is less than or equal to the preset intensity threshold;

[0092] Step S203: Using the formula (3) below, determine the control radius of the artificial lighting based on the location of the artificial lighting and the distribution of the crop's location.

[0093]

[0094] In the above formula (3), R represents the control irradiation radius of artificial supplemental lighting; This means substituting the values ​​of 'a' from 1 to 'n' into the parentheses to obtain the maximum value within the parentheses;

[0095] If a wide-angle artificial supplemental lighting is used, the lighting position is controlled at (x0, y0), and the lighting radius is controlled at R. This allows for wide-angle artificial supplemental lighting of the greenhouse area using a single large light source.

[0096] The beneficial effects of the above technical solution are as follows: Using the above formula (1), based on the distribution of crop locations where the light intensity value is less than or equal to the preset intensity threshold, it is determined whether it is necessary to enter the wide-angle artificial supplementary lighting. Thus, when the light intensity value corresponding to multiple crop locations is less than or equal to the preset intensity threshold, wide-angle artificial supplementary lighting is carried out. First, it is convenient to control the wide-angle artificial supplementary lighting device. Second, using one lighting device to achieve all supplementary lighting reflects the high efficiency of the system. Then, using the above formula (2), based on the distribution of the crop locations, the control lighting location point for artificial supplementary lighting is determined, thus ensuring that the dense areas of artificial supplementary lighting are concentrated in areas with low light intensity values. Finally, using the above formula (3), based on the location point of the artificial supplementary lighting and the distribution of the crop locations, the control lighting radius for artificial supplementary lighting is determined, ensuring that each location point that needs supplementary lighting can be illuminated by the wide-angle artificial supplementary lighting, thus ensuring the reliability of the system.

[0097] Preferably, in step S3, the crop image of the area is analyzed and processed to determine the surface condition information of the crop stems and leaves in the area; based on the surface condition information of the crop stems and leaves, the irrigation parameters for irrigation operations in the area are adjusted, including:

[0098] The pixel color distribution information of the crop image in the area with poor crop growth is extracted, and the area of ​​the withered area on the surface of the crop stem and leaf is obtained based on the pixel color distribution information.

[0099] If the area of ​​the withered region accounts for a greater than or equal to the area percentage of the entire crop stem and leaf surface, the drip irrigation flow rate and / or drip irrigation duration for the crop will be increased.

[0100] The beneficial effects of the above technical solution are as follows: In practical work, crop images of areas with poor crop growth can be extracted from crop images captured by distributed cameras. When crops wither due to water shortage, the pixel color of the withered area corresponding to the stem and leaves in the crop image will be different from that of the non-withered area. By performing pixel color distribution recognition processing on the crop image, the area of ​​the withered area on the surface of the crop stem and leaves can be accurately identified. This allows for the control of the drip irrigation equipment installed inside the greenhouse to adjust the drip irrigation flow rate and / or drip irrigation duration for the corresponding crops, thereby providing timely water replenishment.

[0101] Preferably, in step S4, atmospheric composition information inside the greenhouse is acquired via the Internet of Things to determine whether the current atmospheric environment inhibits normal crop growth; based on the result of this determination, the atmospheric environmental parameters inside the greenhouse are adjusted, including:

[0102] The system acquires carbon dioxide concentration information in the atmosphere inside the greenhouse through the Internet of Things; based on this carbon dioxide concentration information, it determines whether the carbon dioxide concentration in the atmosphere inside the greenhouse during the daytime is lower than a preset concentration threshold; if it exceeds the threshold, it is determined that the atmosphere during the daytime inhibits the normal growth of crops; if it does not exceed the threshold, it is determined that the atmosphere during the daytime does not inhibit the normal growth of crops.

[0103] When the atmospheric environment inhibits the normal growth of crops during the daytime, the amount of carbon dioxide transported into the greenhouse increases.

[0104] The beneficial effects of the above technical solution are as follows: In practical work, carbon dioxide sensors can be installed at different locations inside the greenhouse to form a distributed carbon dioxide sensor network, which is connected to the Internet of Things (IoT). The distributed carbon dioxide sensors detect the carbon dioxide concentration at different locations inside the greenhouse. During photosynthesis, the concentration of carbon dioxide in the environment affects the efficiency of crop photosynthesis. Generally speaking, the lower the concentration of carbon dioxide in the environment, the lower the photosynthetic efficiency of the crop, meaning that crop growth is inhibited. If the carbon dioxide concentration inside the greenhouse is determined to be too low during the daytime, the carbon dioxide delivery equipment will be instructed to deliver an appropriate amount of carbon dioxide into the greenhouse, thereby improving crop growth efficiency.

[0105] See Figure 2 This is a schematic flowchart of an IoT-based greenhouse environment monitoring and adjustment system provided in an embodiment of the present invention. The IoT-based greenhouse environment monitoring and adjustment system includes:

[0106] The image capture and analysis module is used to capture images of the inside of the greenhouse to obtain crop images, and to analyze and process these images to obtain information on the crop growth status inside the greenhouse. Based on this information, it identifies areas inside the greenhouse where the crop growth does not meet predetermined conditions.

[0107] The light information collection and analysis module is used to acquire natural light status information of the area through the Internet of Things, so as to determine the light radiation status information of crops in the vertical direction in the area.

[0108] The supplemental lighting adjustment module is used to adjust the irradiation parameters for supplemental lighting operations on crops in the area based on the information on the received light radiation status.

[0109] The crop stem and leaf condition determination module is used to analyze and process crop images of the area to determine the surface condition information of crop stems and leaves in the area.

[0110] The irrigation adjustment module is used to adjust the irrigation parameters for irrigating crops in the area based on the surface condition information of the crop stems and leaves.

[0111] The atmospheric composition information collection and analysis module is used to obtain atmospheric composition status information inside the greenhouse through the Internet of Things, so as to determine whether the current atmospheric environment inhibits the normal growth of crops.

[0112] The atmospheric environment adjustment module is used to adjust the atmospheric environment parameters inside the greenhouse based on the judgment result.

[0113] The beneficial effects of the above technical solution are as follows: This IoT-based greenhouse environment monitoring and adjustment system captures and analyzes crop images inside the greenhouse to identify areas where crop growth does not meet predetermined conditions; based on the natural light status information of these areas, it obtains the vertical light radiation status information of the crops, thereby adjusting the irradiation parameters for supplemental lighting operations; based on the surface status information of the crop stems and leaves in these areas, it adjusts the irrigation parameters for irrigation operations; based on the atmospheric composition information inside the greenhouse, it determines whether the atmospheric environment inhibits normal crop growth, thereby adjusting the atmospheric environment parameters inside the greenhouse. This system enables zoned supplemental lighting and irrigation management within the greenhouse, prioritizing cultivation care for crops with poor growth. Simultaneously, it adjusts the composition of the atmospheric environment inside the greenhouse, improving the efficiency of photosynthesis and ensuring the reliability and sustainability of crop growth management within the greenhouse.

[0114] Preferably, the image capture and analysis module captures images of the inside of the greenhouse to obtain crop images, and analyzes and processes these images to obtain information on the crop growth status inside the greenhouse. Based on this crop growth status information, it identifies areas inside the greenhouse where the crop growth does not meet predetermined conditions, including:

[0115] The interior of the greenhouse is scanned and photographed to obtain visible light images of the crop; the outline information of the crop's stems and leaves is extracted from the visible light images, and the growth area of ​​the crop's stems and leaves is obtained based on the outline information, which is used as the growth status information of the crop.

[0116] Based on the stem and leaf growth area of ​​the crops, the ratio between the stem and leaf growth area of ​​all crops in each pre-divided grid area inside the greenhouse and the area of ​​the grid area is obtained; if the ratio is less than the preset ratio threshold, the corresponding grid area is determined to belong to the area of ​​poor crop growth; otherwise, the corresponding grid area is determined not to belong to the area of ​​poor crop growth.

[0117] The beneficial effects of the above technical solution are as follows: In practical work, distributed cameras can be installed inside the greenhouse. These distributed cameras are connected to the Internet of Things (IoT). The distributed cameras scan and photograph the inside of the greenhouse, and collect the captured visible light images of crops through the IoT. The visible light images of crops are then analyzed and processed to obtain the ratio between the stem and leaf growth area of ​​all crops in each pre-divided grid area inside the greenhouse and the area of ​​the grid area. The larger the ratio, the more vigorous the crop growth in the corresponding grid area. When the ratio is less than a preset ratio threshold, it indicates that the crop growth status in the corresponding grid area is poor. This allows for the differentiation of different grid areas inside the greenhouse.

[0118] Preferably, the light information collection and analysis module acquires natural light status information of the area through the Internet of Things, thereby determining the vertical light radiation status information of crops in the area, including:

[0119] The Internet of Things (IoT) is used to obtain information on the vertical changes in natural light intensity in areas where crop growth is poor; the crop images corresponding to these areas are analyzed and processed to obtain information on the vertical changes in leaf coverage area of ​​the crops in these areas.

[0120] Based on the information on changes in natural light intensity and changes in leaf coverage area, the light intensity value received by a unit area of ​​leaves at each preset distance along the vertical direction of the crop in the area with poor crop growth is obtained, and this is used as the information on the state of light radiation.

[0121] The supplemental lighting adjustment module adjusts the irradiation parameters for supplemental lighting operations on crops in the area based on the light radiation status information, including:

[0122] If the light intensity value at a certain position in the vertical direction is less than or equal to the preset intensity threshold, the artificial lighting intensity at the corresponding position is increased; otherwise, the current artificial lighting intensity at the corresponding position remains unchanged.

[0123] The beneficial effects of the above technical solution are as follows: In practical work, several light sensors can be sequentially and spaced along the vertical direction in each grid area inside the greenhouse to form a distributed light sensor, which is connected to the Internet of Things (IoT). The distributed light sensor detects the natural light intensity in the vertical direction for each grid area. Since crops are usually placed vertically for cultivation inside the greenhouse, they will block natural light, causing changes in the natural light intensity along the vertical direction in each grid area. Generally speaking, the lower the position along the vertical direction, the lower the corresponding natural light intensity. Thus, the lower the position along the vertical direction, the less natural light intensity it receives. Through the above method, the changes in the light radiation status information of crops at different positions along the vertical direction in areas with poor crop growth can be quantitatively judged. This allows for increasing the artificial lighting intensity in areas with excessively low vertical light intensity, thereby ensuring the uniformity of light for crops in areas with poor growth.

[0124] Preferably, the crop stem and leaf condition determination module analyzes and processes the crop image of the area to determine the surface condition information of the crop stems and leaves in the area, including:

[0125] The pixel color distribution information of the crop image in the area with poor crop growth is extracted, and the area of ​​the withered area on the surface of the crop stem and leaf is obtained based on the pixel color distribution information.

[0126] The irrigation adjustment module adjusts the irrigation parameters for irrigating crops in the area based on the surface condition information of the crop stems and leaves, including:

[0127] If the area of ​​the withered region accounts for a greater than or equal to the area percentage of the entire crop stem and leaf surface, the drip irrigation flow rate and / or drip irrigation duration for the crop will be increased.

[0128] The beneficial effects of the above technical solution are as follows: In practical work, crop images of areas with poor crop growth can be extracted from crop images captured by distributed cameras. When crops wither due to water shortage, the pixel color of the withered area corresponding to the stem and leaves in the crop image will be different from that of the non-withered area. By performing pixel color distribution recognition processing on the crop image, the area of ​​the withered area on the surface of the crop stem and leaves can be accurately identified. This allows for the control of the drip irrigation equipment installed inside the greenhouse to adjust the drip irrigation flow rate and / or drip irrigation duration for the corresponding crops, thereby providing timely water replenishment.

[0129] Preferably, the atmospheric composition information collection and analysis module acquires atmospheric composition status information inside the greenhouse through the Internet of Things to determine whether the current atmospheric environment inhibits the normal growth of crops, including:

[0130] The system acquires carbon dioxide concentration information in the atmosphere inside the greenhouse through the Internet of Things; based on this carbon dioxide concentration information, it determines whether the carbon dioxide concentration in the atmosphere inside the greenhouse during the daytime is lower than a preset concentration threshold; if it exceeds the threshold, it is determined that the atmosphere during the daytime inhibits the normal growth of crops; if it does not exceed the threshold, it is determined that the atmosphere during the daytime does not inhibit the normal growth of crops.

[0131] Based on the judgment result, the atmospheric environment adjustment module adjusts the atmospheric environment parameters inside the greenhouse, including:

[0132] When the atmospheric environment inhibits the normal growth of crops during the daytime, the amount of carbon dioxide transported into the greenhouse increases.

[0133] The beneficial effects of the above technical solution are as follows: In practical work, carbon dioxide sensors can be installed at different locations inside the greenhouse to form a distributed carbon dioxide sensor network, which is connected to the Internet of Things (IoT). The distributed carbon dioxide sensors detect the carbon dioxide concentration at different locations inside the greenhouse. During photosynthesis, the concentration of carbon dioxide in the environment affects the efficiency of crop photosynthesis. Generally speaking, the lower the concentration of carbon dioxide in the environment, the lower the photosynthetic efficiency of the crop, meaning that crop growth is inhibited. If the carbon dioxide concentration inside the greenhouse is determined to be too low during the daytime, the carbon dioxide delivery equipment will be instructed to deliver an appropriate amount of carbon dioxide into the greenhouse, thereby improving crop growth efficiency.

[0134] As can be seen from the above embodiments, the IoT-based greenhouse environment monitoring and adjustment method and system captures and analyzes crop images inside the greenhouse to identify areas where crop growth does not meet predetermined conditions; based on the natural light status information of these areas, it obtains the vertical light radiation status information of the crops, thereby adjusting the irradiation parameters for supplemental lighting operations; based on the surface status information of the crop stems and leaves in these areas, it adjusts the irrigation parameters for irrigation operations; based on the atmospheric composition information inside the greenhouse, it determines whether the atmospheric environment inhibits normal crop growth, thereby adjusting the atmospheric environment parameters inside the greenhouse. This allows for zoned supplemental lighting and irrigation management within the greenhouse, prioritizing cultivation care for crops with poor growth, while also adjusting the composition of the atmospheric environment inside the greenhouse to improve the efficiency of crop photosynthesis, ensuring the reliability and sustainability of crop growth management within the greenhouse.

[0135] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for monitoring and adjusting the environment of a greenhouse based on the Internet of Things, characterized in that, Includes the following steps: Step S1: Take pictures of the inside of the greenhouse to obtain crop images, and analyze and process the crop images to obtain crop growth status information inside the greenhouse; based on the crop growth status information, determine the areas inside the greenhouse where the crop growth does not meet the predetermined conditions. Step S2: Obtain the natural light status information of the area through the Internet of Things, thereby determining the light radiation status information of the crops in the vertical direction in the area; Based on the aforementioned light radiation status information, the irradiation parameters for supplemental lighting operations on crops in the aforementioned area are adjusted, including: The Internet of Things (IoT) is used to acquire information on the vertical changes in natural light intensity in areas with poor crop growth. Crop images corresponding to these areas are analyzed to obtain information on the vertical changes in leaf coverage area. Based on the natural light intensity and leaf coverage information, the light intensity value received per unit area of ​​leaves at preset intervals along the vertical direction is obtained, serving as the light radiation status information. If the light intensity value at a certain vertical position is less than or equal to a preset intensity threshold, the artificial supplementary lighting intensity at that position is increased; otherwise, the current artificial supplementary lighting intensity at that position remains unchanged. Step S3: Analyze and process the crop images of the area to determine the surface condition information of the crop stems and leaves in the area; adjust the irrigation parameters for irrigating the crops in the area based on the surface condition information of the crop stems and leaves. Step S4: Obtain atmospheric composition information inside the greenhouse through the Internet of Things to determine whether the current atmospheric environment inhibits the normal growth of crops; adjust the atmospheric environment parameters inside the greenhouse according to the determination result.

2. The method for monitoring and adjusting the greenhouse environment based on the Internet of Things as described in claim 1, characterized in that: In step S1, images of the inside of the greenhouse are taken to obtain crop images, and these images are analyzed to obtain crop growth status information inside the greenhouse. Based on this crop growth status information, areas inside the greenhouse where crop growth does not meet predetermined conditions are identified, including: The interior of the greenhouse is scanned and photographed to obtain visible light images of the crops; the stem and leaf outline information of the crops is extracted from the visible light images of the crops, and the stem and leaf growth area of ​​the crops is obtained based on the stem and leaf outline information, which is used as the crop growth status information; Based on the stem and leaf growth area of ​​the crops, the ratio between the stem and leaf growth area of ​​all crops in each pre-divided grid area inside the greenhouse and the area of ​​the grid area is obtained; if the ratio is less than a preset ratio threshold, the corresponding grid area is determined to belong to the area of ​​poor crop growth; otherwise, the corresponding grid area is determined not to belong to the area of ​​poor crop growth.

3. The method for monitoring and adjusting the greenhouse environment based on the Internet of Things as described in claim 1, characterized in that: Step S2 further includes: If multiple crop locations have light intensity values ​​less than or equal to a preset intensity threshold, then wide-angle artificial supplemental lighting is applied. The location points and irradiation radius of the artificial supplemental lighting are determined based on the distribution of the crop locations. The process is as follows: Step S201: Establish a Cartesian coordinate system on the ground of the greenhouse area. Each crop location point is represented by coordinates. Using the following formula (1), determine whether it is necessary to provide wide-angle artificial lighting based on the distribution of crop location points with light intensity values ​​less than or equal to a preset intensity threshold. In the above formula (1), E represents the control value of artificial supplemental lighting entering the wide-angle field; L represents the diameter of the maximum circular range that can be illuminated by the artificial supplemental lighting in the wide-angle field; k represents the ratio between the unit line segment of the actual plane and the unit line segment of the established coordinate system; [X(a),Y(a)] represents the coordinates of the a-th crop position where the light intensity value is less than or equal to the preset intensity threshold; [X(b),Y(b)] represents the coordinates of the b-th crop position where the light intensity value is less than or equal to the preset intensity threshold; n represents the total number of crop positions where the light intensity value is less than or equal to the preset intensity threshold. This means taking the values ​​of a from 1 to n, and substituting the values ​​of b from 1 to n into the parentheses to get the maximum value inside the parentheses; If E=1, then control the artificial lighting to enter the wide-angle field and proceed to step S201 below; if E=0, then control the artificial lighting not to enter the wide-angle field and still provide independent artificial lighting to the corresponding positions according to the corresponding positional relationship. Step S202: If wide-angle artificial lighting is required, the control lighting position point for artificial lighting is determined using the following formula (2) based on the distribution of the crop locations. In the above formula (2), (x0,y0) represents the coordinates of the controlled illumination position of artificial supplemental lighting; G(a) represents the light intensity value of the a-th crop position where the light intensity value is less than or equal to the preset intensity threshold; Step S203: Using the following formula (3), determine the control radius of the artificial lighting based on the location of the artificial lighting and the distribution of the crop locations. In the above formula (3), R represents the control irradiation radius of artificial supplemental lighting; This means substituting the values ​​of 'a' from 1 to 'n' into the parentheses to obtain the maximum value within the parentheses; If a wide-angle artificial supplementary lighting is applied, the lighting position is controlled at (x0, y0), and the lighting radius is controlled at R. This allows a single large light source to provide wide-angle artificial supplementary lighting to the greenhouse area.

4. The method for monitoring and adjusting the greenhouse environment based on the Internet of Things as described in claim 1, characterized in that: In step S3, the crop image of the region is analyzed and processed to determine the surface condition information of the crop stems and leaves in the region; Based on the crop stem and leaf surface condition information, adjust the irrigation parameters for irrigating the crops in the area, including: The pixel color distribution information of the crop image in the area with poor crop growth is extracted, and the area of ​​the withered area on the surface of the crop stem and leaf is obtained based on the pixel color distribution information. If the area of ​​the withered region accounts for a greater than or equal to a preset area percentage threshold on the entire surface of the crop stems and leaves, the drip irrigation flow rate and / or drip irrigation duration for the crop will be increased.

5. The method for monitoring and adjusting the greenhouse environment based on the Internet of Things as described in claim 4, characterized in that: In step S4, atmospheric composition information inside the greenhouse is obtained through the Internet of Things to determine whether the current atmospheric environment inhibits the normal growth of crops. Based on the results of the judgment, adjust the atmospheric environmental parameters inside the greenhouse, including: Information on carbon dioxide concentration in the atmosphere inside a greenhouse is obtained through the Internet of Things. Based on the carbon dioxide concentration information, determine whether the carbon dioxide concentration in the indoor atmosphere of the greenhouse during the daytime is lower than a preset concentration threshold; if it exceeds the threshold, it is determined that the atmospheric environment during the daytime inhibits the normal growth of crops; if it does not exceed the threshold, it is determined that the atmospheric environment during the daytime does not inhibit the normal growth of crops. When the atmospheric environment inhibits the normal growth of crops during the daytime, the amount of carbon dioxide transported into the greenhouse increases.

6. A greenhouse environment monitoring and adjustment system based on the Internet of Things, characterized in that, include: The image capture and analysis module is used to capture images of the inside of the greenhouse to obtain crop images, and to analyze and process the crop images to obtain crop growth status information inside the greenhouse; based on the crop growth status information, it determines the areas inside the greenhouse where the crop growth does not meet the predetermined conditions. The light information collection and analysis module is used to acquire natural light status information of the area through the Internet of Things (IoT) to determine the vertical light radiation status information of crops in the area. This includes: acquiring natural light intensity variation information in the vertical direction of areas with poor crop growth through the IoT; analyzing and processing crop images corresponding to areas with poor crop growth to obtain leaf coverage area variation information in the vertical direction of the crops in areas with poor crop growth; and, based on the natural light intensity variation information and the leaf coverage area variation information, obtaining the light intensity value received per unit area of ​​leaves at each preset distance along the vertical direction of the crops in areas with poor crop growth, using this as the light radiation status information. The supplemental lighting adjustment module is used to adjust the irradiation parameters for supplemental lighting operations on crops in the area based on the light radiation status information, including: if the light intensity value corresponding to a certain position in the vertical direction is less than or equal to a preset intensity threshold, then increase the artificial supplemental lighting intensity at the corresponding position; otherwise, keep the current artificial supplemental lighting intensity at the corresponding position unchanged. The crop stem and leaf state determination module is used to analyze and process crop images of the area to determine the surface state information of crop stems and leaves in the area. The irrigation adjustment module is used to adjust the irrigation parameters for irrigating crops in the area based on the surface condition information of the crop stems and leaves. The atmospheric composition information collection and analysis module is used to obtain atmospheric composition status information inside the greenhouse through the Internet of Things, so as to determine whether the current atmospheric environment inhibits the normal growth of crops. The atmospheric environment adjustment module is used to adjust the atmospheric environment parameters inside the greenhouse based on the judgment results.

7. The greenhouse environment monitoring and adjustment system based on the Internet of Things as described in claim 6, characterized in that: The image capture and analysis module captures images of the inside of the greenhouse to obtain crop images, and analyzes and processes these images to obtain crop growth status information inside the greenhouse. Based on this crop growth status information, it identifies areas inside the greenhouse where crop growth does not meet predetermined conditions, including: The interior of the greenhouse is scanned and photographed to obtain visible light images of the crops; the stem and leaf outline information of the crops is extracted from the visible light images of the crops, and the stem and leaf growth area of ​​the crops is obtained based on the stem and leaf outline information, which is used as the crop growth status information; Based on the stem and leaf growth area of ​​the crops, the ratio between the stem and leaf growth area of ​​all crops in each pre-divided grid area inside the greenhouse and the area of ​​the grid area is obtained; if the ratio is less than a preset ratio threshold, the corresponding grid area is determined to belong to the area of ​​poor crop growth; otherwise, the corresponding grid area is determined not to belong to the area of ​​poor crop growth.

8. The greenhouse environment monitoring and adjustment system based on the Internet of Things as described in claim 7, characterized in that: The crop stem and leaf condition determination module analyzes and processes the crop image of the area to determine the surface condition information of the crop stems and leaves in the area, including: The pixel color distribution information of the crop image in the area with poor crop growth is extracted, and the area of ​​the withered area on the surface of the crop stem and leaf is obtained based on the pixel color distribution information. The irrigation adjustment module adjusts the irrigation parameters for irrigating crops in the area based on the crop stem and leaf surface condition information, including: If the area of ​​the withered region accounts for a greater than or equal to a preset area percentage threshold on the entire surface of the crop stems and leaves, the drip irrigation flow rate and / or drip irrigation duration for the crop will be increased. The atmospheric composition information collection and analysis module acquires atmospheric composition status information inside the greenhouse through the Internet of Things to determine whether the current atmospheric environment inhibits the normal growth of crops, including: The carbon dioxide concentration information in the indoor atmosphere of the greenhouse is obtained through the Internet of Things; based on the carbon dioxide concentration information, it is determined whether the carbon dioxide concentration in the indoor atmosphere of the greenhouse during the daytime is lower than a preset concentration threshold; if it exceeds the threshold, it is determined that the atmospheric environment during the daytime inhibits the normal growth of crops; if it does not exceed the threshold, it is determined that the atmospheric environment during the daytime does not inhibit the normal growth of crops. The atmospheric environment adjustment module adjusts the atmospheric environment parameters inside the greenhouse based on the judgment result, including: When the atmospheric environment inhibits the normal growth of crops during the daytime, the amount of carbon dioxide transported into the greenhouse increases.

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