Farmland irrigation control system

Abstract

The invention discloses a farmland irrigation control system, and the system comprises an acquisition module which records a crop image of a target crop, introduces the image into a database, and obtains a humidity threshold value of the target crop and soil data of target soil; the analysis module is used for extracting a first shape feature and a color feature of the crop image, analyzing the first shape feature and the color feature to obtain the rhizome depth of the target crop, and analyzing the soil data to obtain the threshold level of the target soil; the decision-making module analyzes the depth of the rhizome to obtain a humidity monitoring range, the acquisition module detects humidity data of the humidity monitoring range, adjusts a humidity threshold value in combination with a threshold value level, generates a first difference value and makes an irrigation decision; according to the method, a double analysis strategy of appearance features and color features is adopted, the growth cycle is accurately identified, the problem of irrigation depth misalignment caused by erroneous judgment in the growth stage in a traditional method is thoroughly solved, and the effect of improving the irrigation depth matching degree is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent agricultural irrigation control, and in particular to a farmland irrigation control system. BACKGROUND

[0002] In recent years, more and more farmlands begin to introduce mechanized irrigation equipment, and irrigation has become the first choice for many agricultural greenhouses, fruit trees and cotton industries. Therefore, how to realize the automatic control of the irrigation equipment has become the core problem of agricultural intelligence and informatization.

[0003] The introduction of the intelligent irrigation system in the prior art solves the disadvantages and inconvenience of manual ball valve opening and closing, more accurately and reasonably controls the irrigation time and water quantity, and the parallelly arranged fertilization device can also realize automatic fertilization. However, the current intelligent irrigation system lacks a dynamic adjustment mechanism when collecting data, and cannot adjust the threshold value according to the different growth periods of crops, thereby causing waste of resources. SUMMARY

[0004] The technical problem solved by the present application is to solve the problem of low irrigation efficiency and resource waste caused by the fact that the traditional irrigation system does not distinguish the different soil depths of the growth stages of crops, thereby causing inaccurate detection of the actual water absorption layer humidity of crops.

[0005] To solve the above technical problems, the present application provides the following technical solutions: A farmland irrigation control system, comprising: An acquisition module records image data of target crops, pre-processes the image data, introduces a database, and obtains a humidity threshold value of the target crops and soil data of target soil; the image data includes crop images; An analysis module extracts first shape features and color features of the crop images, analyzes the first shape features and color features, obtains the root stem depth of the target crops, and analyzes the soil data to obtain the threshold grade of the target soil; A decision module is used to analyze the root stem depth to obtain a humidity monitoring range; The decision module controls the acquisition module to detect humidity data of the humidity monitoring range, adjusts the humidity threshold value in combination with the threshold grade, generates a first difference value, makes an irrigation decision, and judges whether to irrigate.

[0006] As a preferred scheme of the farmland irrigation control system described in the present application, wherein: The acquisition module records image data of target crops with a first time period as a monitoring period, pre-processes the image data, the pre-processing includes denoising processing and filtering processing, and the image data includes crop shape and crop color.

[0007] As a preferred solution of the farmland irrigation control system of the present invention, wherein: The analysis module extracts the first shape feature of the crop image after preprocessing, performs a first analysis on the first shape feature to obtain the growth cycle of the target crop, performs a second analysis on the color feature of the crop image based on the growth cycle to obtain the cycle progress of the target crop, and determines the rhizome depth based on a pre-established mapping relationship table of growth cycle, cycle progress and rhizome depth.

[0008] As a preferred solution of the farmland irrigation control system of the present invention, wherein: The analysis logic of the first shape feature includes: calling a crop database, wherein the crop database includes standard crop images in different growth periods; Any standard crop image is obtained from standard crop images in different growth cycles, a second shape feature of the any standard crop image is extracted based on the crop appearance, a first similarity between the first shape feature and the second shape feature is calculated using a cosine similarity formula, the first similarities are sorted in descending order, and the standard crop image corresponding to the first similarity with the largest value is selected as the first image, and the growth cycle to which the first image belongs is identified.

[0009] As a preferred solution of the farmland irrigation control system of the present invention, wherein: The analysis logic of the color feature includes: Calling a crop database, wherein the crop database includes a range of color depths of crops in each growth period; The crop image is converted into a grayscale image, and the grayscale value of each pixel in the grayscale image is obtained; an interval pixel sampling method is used, with each interval pixel as the center, and the grayscale difference between itself and the adjacent pixel is calculated respectively, and the adjacent pixel with the maximum grayscale difference and the center pixel are stored in a temporary data array, and compared with a preset segmentation threshold, and the grayscale value whose maximum grayscale difference is less than the preset segmentation threshold is obtained, and the grayscale value is piled according to the value of the grayscale value, and a corresponding number of storage arrays are set, wherein the screening condition of the storage array is that the grayscale value falls within the grayscale value range of the corresponding pile; when the maximum grayscale difference is greater than the preset segmentation threshold, the coordinate information of the adjacent pixel with the maximum grayscale difference and the center pixel is respectively recorded in the corresponding storage array according to the corresponding grayscale value; based on the coordinate information, the proportion of the number of pixels in each storage array occupied by the grayscale image is calculated respectively, and each storage array is sorted in descending order according to the proportion, and the storage array with the largest proportion is recorded as the crop grayscale value array; The color depth range of the crop corresponding to the growth cycle is converted into a standard gray value range, a position information of the most concentrated gray value range in the standard gray value range is obtained based on the gray value array of the crop, a corresponding progress percentage is generated, and the progress of the target crop is determined.

[0010] As a preferred scheme of the farmland irrigation control system provided by the application, wherein: The analysis logic of the threshold level comprises: A soil database is introduced, and the soil database comprises a water retention rate corresponding to each soil category; According to the soil data, the soil category of the target soil is obtained, and based on the soil category, the corresponding water retention rate is obtained; According to the different water retention rates, the corresponding threshold levels of each soil category are generated; The water retention rates are arranged in descending order, the first threshold, the second threshold and the third threshold are sequentially set, and the corresponding humidity threshold is selected from the first threshold, the second threshold and the third threshold according to the threshold level of each soil category.

[0011] As a preferred scheme of the farmland irrigation control system provided by the application, wherein: Further comprising a prediction module: The prediction module accesses a meteorological data system to obtain local weather data in a second time period, and the weather data comprises rainfall data and wind data; According to the wind data, the values of the first threshold, the second threshold and the third threshold in the threshold level are adjusted to realize dynamic adjustment of the humidity threshold; and according to the rainfall data and the first difference, a waterlogging risk value is analyzed and generated, and a decision is made on whether to irrigate based on the waterlogging risk value.

[0012] As a preferred scheme of the farmland irrigation control system provided by the application, wherein: The execution logic of the dynamic adjustment of the humidity threshold comprises: The wind data comprises a wind speed level, the wind speed level is compared with a preset level threshold, and when the wind speed level exceeds the preset level threshold, the values of the first threshold, the second threshold and the third threshold are reduced by a preset proportion.

[0013] As a preferred scheme of the farmland irrigation control system provided by the application, wherein: The analysis logic of the waterlogging risk value comprises: The rainfall probability and the rainfall level in the rainfall data are obtained; The product of the rainfall probability and the first difference is calculated and compared with the preset rainfall threshold. When the product of the rainfall probability and the first difference exceeds the preset rainfall threshold, an irrigation decision is made to determine whether to irrigate. When the product of the rainfall probability and the first difference is less than the preset rainfall threshold, a suspension irrigation instruction is generated.

[0014] A farmland irrigation control method, which is applied to the above-mentioned farmland irrigation control system, comprises the following steps: Step S100: the acquisition module records the image data of the target crop and obtains the moisture threshold of the target crop and the soil data of the target soil; Step S200: The analysis module analyzes the first shape feature and the color feature to obtain the root depth of the target crop, and analyzes the soil data to obtain the threshold level of the target soil; In step S300, the decision module obtains the humidity monitoring range based on the root depth, controls the acquisition module to detect the humidity data in the humidity monitoring range, adjusts the humidity threshold in combination with the threshold level, generates a first difference, makes an irrigation decision, and determines whether to irrigate.

[0015] Beneficial effects of the present invention: First, the farmland irrigation control system provided by the present invention accurately identifies the growth cycle by adopting a dual analysis strategy of appearance characteristics and color characteristics, completely solving the problem of inaccurate irrigation depth caused by misjudgment of growth stages in traditional methods, and plays a role in improving the matching degree of irrigation depth.

[0016] Second, the present invention provides a farmland irrigation control system that establishes a dynamic grading model of soil water retention rate and three-level thresholds (first threshold, second threshold, third threshold), automatically matching threshold levels according to different soil types (such as sandy soil with low water retention and clay with high water retention), and can effectively avoid root hypoxia damage.

[0017] Third, the present invention provides a farmland irrigation control system that uses a wind speed-threshold linkage mechanism (automatically lowering each level's threshold to reduce evaporation when the wind speed exceeds a preset level) and combines rainfall probability with humidity difference (first difference) to construct an overflow risk quantification model, thereby reducing over-irrigation events caused by heavy rain. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flow chart of a farmland irrigation control system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments.

[0020] In recent years, more and more farmlands have introduced mechanized irrigation equipment, and irrigation has become the first choice for many agricultural greenhouses, fruit trees and cotton industries. Therefore, how to realize the automatic control of the irrigation equipment has become the core problem of agricultural intelligence and informatization.

[0021] The introduction of the intelligent irrigation system in the prior art has solved the disadvantages and inconvenience of manual ball valve opening and closed, and more accurately and reasonably controls the irrigation time and water quantity. Meanwhile, the automatically fertilizing device arranged in parallel can also realize automatic fertilizing. However, the current intelligent irrigation system lacks a dynamic adjustment mechanism when collecting data, and cannot adjust the threshold value according to the different growth periods of crops, thereby causing waste of resources.

[0022] Embodiment one, refer to Figure 1 The present application provides a farmland irrigation control system, comprising: a collection module, recording image data of target crops, pre-processing the image data, introducing a database, obtaining humidity threshold value of target crops and soil data of target soil; the image data includes crop image; an analysis module, extracting first shape feature and color feature of the crop image, analyzing the first shape feature and color feature to obtain root depth of the target crop, analyzing the soil data to obtain threshold grade of the target soil; a decision module for analyzing the root depth to obtain humidity monitoring range; the decision module controls the collection module to detect humidity data of the humidity monitoring range, adjusts the humidity threshold value combined with the threshold grade, generates a first difference, makes irrigation decision, and judges whether to irrigate.

[0023] The farmland irrigation control system comprises the following execution steps: Step S100, the collection module records the image data of the target crops, and obtains the humidity threshold value of the target crops and the soil data of the target soil.

[0024] Step S110, the collection module records the image data of the target crops with a first time period as a monitoring period, and pre-processes the image data. The pre-processing includes denoising processing and filtering processing. The image data includes crop shape and crop color. The first time period is usually one month.

[0025] In step S200, the analysis module analyzes the first shape and color features to determine the root depth of the target crop, and then analyzes the soil data to determine the target soil threshold level. By obtaining the root depth of the target crop, the current root area of ​​the target crop can be monitored for moisture, ensuring that the target crop is properly irrigated. Due to the thickness of the wall, the bottom of the root of the crop is the thinnest part and is the main part of the crop that absorbs water.

[0026] In step S210, the analysis module extracts the first shape feature of the crop image after preprocessing, performs a first analysis on the first shape feature to obtain the growth cycle of the target crop, performs a second analysis on the color feature of the crop image based on the growth cycle to obtain the cycle progress of the target crop, and determines the rhizome depth based on a pre-established mapping relationship table of growth cycle, cycle progress and rhizome depth.

[0027] Step S211 calls a crop database containing images of standard crops in different growth cycles. A standard crop image is obtained from the standard crop images in different growth cycles. A second shape feature of the standard crop is extracted based on the crop's appearance. A first similarity between the first and second shape features is calculated using the cosine similarity formula. The first similarities are sorted in descending order. The standard crop image with the highest first similarity is selected as the first image, and the growth cycle to which the first image belongs is identified. The first and second shape features are extracted using machine vision. This allows for automatic determination of the current target crop's growth status through machine vision. Plants in different growth states have distinct shape features, which can be automatically identified using sophisticated machine vision.

[0028] Step S212, calling a crop database, the crop database includes the color depth range of each crop growth period; The crop image is converted into a grayscale image, and the grayscale value of each pixel in the grayscale image is obtained; the interval pixel sampling method is used, and the grayscale value of the pixel with the coordinates (1,1), (1,3), (1,n), (3,1), (3,3) is taken as the center, and then the grayscale difference between itself and the adjacent pixel is calculated respectively, and the adjacent pixel with the maximum grayscale difference and the center pixel are stored in a temporary data array, and compared with the preset segmentation threshold, the grayscale value with the maximum grayscale difference less than the preset segmentation threshold is obtained, and the grayscale value is calculated according to the grayscale value. The values ​​are stacked and a corresponding number of storage arrays are set, where the screening condition of the storage array is that the grayscale value falls within the grayscale value range of the corresponding stack; when the maximum grayscale difference is greater than the preset segmentation threshold, the coordinate information of the adjacent pixels with the maximum grayscale difference and the central pixel is recorded in the corresponding storage array according to the corresponding grayscale value; based on the coordinate information, the proportion of the number of pixels in each storage array occupied by the grayscale image is calculated, and each storage array is sorted in descending order according to the proportion, and the storage array with the largest proportion is recorded as the crop grayscale value array; The color depth range of the crop corresponding to the growth cycle is converted into a standard grayscale value range. Based on the position information of the most concentrated grayscale value range in the crop grayscale value array within the standard grayscale value range, the corresponding progress percentage is generated to determine the progress of the cycle to which the target crop belongs.

[0029] By doing so, the present application can automatically identify the approximate stage of the current growth state by recognizing color features, thereby improving recognition effectiveness and efficiency. For example, at the end of the germination period, the color of the crop becomes significantly darker and remains dark green, which is clearly different from the light green color of the crop that has just entered the germination period.

[0030] Step S220 introduces a soil database, which includes water retention rates corresponding to various soil types. The soil type of the target soil is obtained based on the soil data, and the corresponding water retention rate is obtained based on the soil type. A threshold level corresponding to each soil type is generated based on the water retention rate. The water retention rates are sorted in descending order, and a first threshold, a second threshold, and a third threshold are set accordingly. Based on the threshold level corresponding to each soil type, a corresponding humidity threshold is selected from the first threshold, the second threshold, and the third threshold. Furthermore, based on the differences in water retention rates between different soils, such as sandy soil and black soil, the present application can automatically match threshold levels based on different soil types (e.g., sandy soil has low water retention, clay soil has high water retention), effectively preventing excessive soil moisture and hypoxic damage to crop roots caused by overwatering.

[0031] In step S300, the decision module obtains the humidity monitoring range based on the root depth, controls the acquisition module to detect the humidity data in the humidity monitoring range, adjusts the humidity threshold in combination with the threshold level, generates a first difference, makes an irrigation decision, and determines whether to irrigate.

[0032] In step S400, the prediction module accesses the meteorological data system and obtains local weather data, including rainfall and wind data, for a second time period. Based on the wind data, the prediction module adjusts the values ​​of the first, second, and third thresholds within the threshold level to achieve dynamic adjustment of the humidity threshold. Based on the rainfall data and the first difference, the prediction module analyzes and generates a waterlogging risk value, and makes a decision on whether to irrigate based on the waterlogging risk value. The second time period is set in weeks, based on the frequency of weather changes, and is typically set to one week.

[0033] In step S410, the wind data, including wind speed levels, is compared with preset thresholds. When the wind speed level exceeds the threshold, the first, second, and third thresholds are reduced by a predetermined ratio. This allows for faster evaporation of soil moisture when wind speeds are high, allowing for pre-watering of the soil. This effectively prevents larger than usual soil moisture fluctuations due to air movement, potentially leading to soil dehydration during the automatic check cycle.

[0034] Step S420, obtain the rainfall probability and rainfall level in the rainfall data; calculate the product of the rainfall probability and the first difference, and compare it with the preset rainfall threshold. When the product of the rainfall probability and the first difference exceeds the preset rainfall threshold, make an irrigation decision to determine whether to irrigate; when the product of the rainfall probability and the first difference is less than the preset rainfall threshold, generate a pause irrigation instruction; thereby, the present application can effectively avoid the problem of over-irrigation of crops due to rain after irrigation.

[0035] In a second embodiment, the present application provides another method for identifying the progress of a crop cycle, which includes the following steps: Step a, color feature extraction: First, color space conversion is performed to convert the preprocessed image from RGB color space to a color space more suitable for color analysis, such as HSV or LAB.

[0036] Next, color features are extracted to extract the color features of the region of interest (ROI) in the image, such as calculating the average hue, saturation and brightness of the ROI.

[0037] Step b, compare with the standard image: First, load the standard image, load the pre-defined standard image, which represents the color characteristics of crops at different maturity stages.

[0038] Then, the color features of the standard image are extracted, and the same preprocessing and color feature extraction operations are performed on the standard image.

[0039] Finally, the color feature difference is calculated to calculate the difference between the color features of the real-time image and the color features of the standard image.

[0040] Step c, determine maturity: Based on the size of the color feature difference, the real-time image is judged to which standard image (standard images are collected at the initial and final stages of the cycle, and maturity is judged at the stage when the real-time image is in the middle color feature range) it is closest to, thereby determining the maturity stage of the crop.

[0041] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0042] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A farmland irrigation control system, characterized in that: include: an acquisition module, recording image data of target crops, preprocessing the image data, importing the image data into a database, and obtaining a moisture threshold of the target crops and soil data of the target soil; the image data includes crop images; an analysis module that extracts first shape features and color features of the crop image, analyzes the first shape features and color features to obtain a root depth of the target crop, and analyzes soil data to obtain a threshold level of the target soil; Decision module, used to analyze the root depth to obtain the humidity monitoring range; The decision module controls the acquisition module to detect humidity data within the humidity monitoring range, adjusts the humidity threshold in combination with the threshold level, and generates a first difference to make an irrigation decision and determine whether to irrigate.

2. The farmland irrigation control system according to claim 1, characterized in that: The acquisition module uses the first time period as a monitoring cycle to record image data of the target crop and preprocess the image data, wherein the preprocessing includes denoising and filtering. The image data includes crop shape and crop color.

3. The farmland irrigation control system according to claim 1, characterized in that: The analysis module extracts the first shape feature of the crop image after preprocessing, performs a first analysis on the first shape feature to obtain the growth cycle of the target crop, performs a second analysis on the color feature of the crop image based on the growth cycle to obtain the cycle progress of the target crop, and determines the rhizome depth based on a pre-established mapping relationship table of growth cycle, cycle progress and rhizome depth.

4. The farmland irrigation control system according to claim 3, characterized in that: The analysis logic of the first shape feature includes: calling a crop database, wherein the crop database includes standard crop images in different growth periods; Any standard crop image is obtained from standard crop images in different growth cycles, a second shape feature of the any standard crop image is extracted based on the crop appearance, a first similarity between the first shape feature and the second shape feature is calculated using a cosine similarity formula, the first similarities are sorted in descending order, and the standard crop image corresponding to the first similarity with the largest value is selected as the first image, and the growth cycle to which the first image belongs is identified.

5. The farmland irrigation control system according to claim 3, characterized in that: The analysis logic of the color feature includes: Calling a crop database, wherein the crop database includes a range of color depths of crops in each growth period; The crop image is converted into a grayscale image, and the grayscale value of each pixel in the grayscale image is obtained; an interval pixel sampling method is used, with each interval pixel as the center, and the grayscale difference between itself and the adjacent pixel is calculated respectively, and the adjacent pixel with the maximum grayscale difference and the center pixel are stored in a temporary data array, and compared with a preset segmentation threshold, and the grayscale value whose maximum grayscale difference is less than the preset segmentation threshold is obtained, and the grayscale value is piled according to the value of the grayscale value, and a corresponding number of storage arrays are set, wherein the screening condition of the storage array is that the grayscale value falls within the grayscale value range of the corresponding pile; when the maximum grayscale difference is greater than the preset segmentation threshold, the coordinate information of the adjacent pixel with the maximum grayscale difference and the center pixel is respectively recorded in the corresponding storage array according to the corresponding grayscale value; based on the coordinate information, the proportion of the number of pixels in each storage array occupied by the grayscale image is calculated respectively, and each storage array is sorted in descending order according to the proportion, and the storage array with the largest proportion is recorded as the crop grayscale value array; The color depth range of the crop's corresponding growth cycle is converted into a standard grayscale value range. Based on the position information of the most concentrated grayscale value range in the crop grayscale value array within the standard grayscale value range, the corresponding progress percentage is generated to determine the cycle progress of the target crop.

6. The farmland irrigation control system according to claim 1, characterized in that: The analysis logic of the threshold level includes: A soil database is introduced, wherein the soil database includes water retention rates corresponding to various soil types; Obtain the soil type of the target soil according to the soil data, and obtain the corresponding water retention rate based on the soil type; According to the different water retention rates, the corresponding threshold levels for each soil type are generated; The water retention rates are arranged in descending order, the first threshold, the second threshold, and the third threshold are set accordingly, and the corresponding humidity threshold is selected from the first threshold, the second threshold, and the third threshold according to the threshold level corresponding to each soil type.

7. The farmland irrigation control system according to claim 6, characterized in that: Also includes prediction module: The prediction module accesses the meteorological data system and obtains local weather data in a second time period, wherein the weather data includes rainfall data and wind data; According to the wind data, the values ​​of the first threshold, the second threshold and the third threshold within the threshold level are adjusted to achieve dynamic adjustment of the humidity threshold; According to the rainfall data and the first difference, a waterlogging risk value is analyzed and generated, and a decision on whether to irrigate is made based on the waterlogging risk value.

8. The farmland irrigation control system according to claim 7, characterized in that: The execution logic for dynamically adjusting the humidity threshold includes: The wind data includes wind speed level, which is compared with a preset level threshold. When the wind speed level exceeds the preset level threshold, the values ​​of the first threshold, the second threshold and the third threshold are reduced according to a preset ratio.

9. The farmland irrigation control system according to claim 7, characterized in that: The analysis logic of the waterlogging risk value includes: Get the rainfall probability and rainfall level in rainfall data; The product of the rainfall probability and the first difference is calculated and compared with the preset rainfall threshold. When the product of the rainfall probability and the first difference exceeds the preset rainfall threshold, an irrigation decision is made to determine whether to irrigate. When the product of the rainfall probability and the first difference is less than the preset rainfall threshold, a suspension irrigation instruction is generated.

10. A farmland irrigation control method, applied to the farmland irrigation control system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step S100: the acquisition module records the image data of the target crop and obtains the moisture threshold of the target crop and the soil data of the target soil; Step S200: The analysis module analyzes the first shape feature and the color feature to obtain the root depth of the target crop, and analyzes the soil data to obtain the threshold level of the target soil; In step S300, the decision module obtains the humidity monitoring range based on the root depth, controls the acquisition module to detect the humidity data in the humidity monitoring range, adjusts the humidity threshold in combination with the threshold level, generates a first difference, makes an irrigation decision, and determines whether to irrigate.

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