CN111045467B - Intelligent agricultural machine control method based on Internet of things - Google Patents
Intelligent agricultural machine control method based on Internet of things Download PDFInfo
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- 244000144972 livestock Species 0.000 claims abstract description 31
- 238000012545 processing Methods 0.000 claims abstract description 27
- 241000607479 Yersinia pestis Species 0.000 claims abstract description 17
- 230000005540 biological transmission Effects 0.000 claims abstract description 6
- 238000012544 monitoring process Methods 0.000 claims description 16
- 238000003973 irrigation Methods 0.000 claims description 15
- 230000002262 irrigation Effects 0.000 claims description 15
- 239000000575 pesticide Substances 0.000 claims description 15
- 239000002689 soil Substances 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 12
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 10
- 238000001914 filtration Methods 0.000 claims description 10
- 235000015097 nutrients Nutrition 0.000 claims description 10
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 claims description 7
- 238000005516 engineering process Methods 0.000 claims description 7
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 5
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 5
- 239000001569 carbon dioxide Substances 0.000 claims description 5
- 201000010099 disease Diseases 0.000 claims description 5
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 5
- 230000012173 estrus Effects 0.000 claims description 5
- 239000003337 fertilizer Substances 0.000 claims description 5
- 230000002496 gastric effect Effects 0.000 claims description 5
- 230000036541 health Effects 0.000 claims description 5
- 238000010438 heat treatment Methods 0.000 claims description 5
- 238000010606 normalization Methods 0.000 claims description 5
- 235000016709 nutrition Nutrition 0.000 claims description 5
- 230000035764 nutrition Effects 0.000 claims description 5
- 229910052760 oxygen Inorganic materials 0.000 claims description 5
- 239000001301 oxygen Substances 0.000 claims description 5
- 238000005096 rolling process Methods 0.000 claims description 5
- 239000007921 spray Substances 0.000 claims description 5
- 238000005507 spraying Methods 0.000 claims description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 229910021529 ammonia Inorganic materials 0.000 claims description 2
- 238000003975 animal breeding Methods 0.000 claims description 2
- 238000009360 aquaculture Methods 0.000 claims description 2
- 244000144974 aquaculture Species 0.000 claims description 2
- 238000009395 breeding Methods 0.000 claims description 2
- 230000001488 breeding effect Effects 0.000 claims description 2
- 239000000284 extract Substances 0.000 claims description 2
- 230000037406 food intake Effects 0.000 claims description 2
- 230000010365 information processing Effects 0.000 claims description 2
- 230000008447 perception Effects 0.000 claims description 2
- 238000013024 troubleshooting Methods 0.000 claims description 2
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- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D27/00—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00
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Abstract
The invention provides an intelligent agricultural machine control method based on the Internet of things, which comprises the following steps: s1) feature modeling: the intelligent agricultural machine control method based on the Internet of things realizes modeling of different specifications of crops and livestock, omni-directional sensing of various sensors of the crops and the livestock, efficient transmission and processing of information, contrastive analysis with model library data, command sending by searching a pest model library, a crop growth model library and an information guidance model library to obtain operating parameters of agricultural machinery and eliminate faults of the agricultural machinery, and then command information is presented to an on-site controller to adjust operation of the agricultural machinery according to agricultural crop management and control commands.
Description
Technical Field
The invention relates to the technical field of agricultural control methods, in particular to an intelligent agricultural machine control method based on the Internet of things.
Background
Along with the development of science and technology, the agricultural production is greatly improved in an intelligent mode, the intelligent agricultural machine control method is greatly applied to the field of agricultural production based on the popularization of the concept of the internet of things, the traditional agricultural machine control method is old in control method and incapable of benefiting farmers, the agricultural production cultivation and agricultural machine control are two independent structures and cannot be simultaneously subjected to intelligent association of the internet of things, the work difficulty of people is increased, the agricultural production efficiency cannot be substantially improved, the agricultural production cultivation and agricultural machine control is not reasonable enough, the agricultural machine cannot be effectively utilized, and therefore the intelligent agricultural machine control method based on the internet of things can solve the problems.
Disclosure of Invention
The invention aims to overcome the technical problems of the prior art and provides an intelligent agricultural machine control method based on the Internet of things, which can be used for monitoring various sensors of crops and livestock by modeling the crops and the livestock with different specifications, transmitting monitoring data to a remote host through a GPRS transmission module under the support of a wireless network, integrating and displaying the remote host on a display, extracting characteristic information of the crops and the livestock and comparing and analyzing the characteristic information with model library data, sending an instruction by searching a pest model library, a crop growth model library and an information guidance model library to obtain operating parameters of the agricultural machine and eliminate faults of the agricultural machine, then processing and judging the collected information by a system host, transmitting the instruction to a field controller through the GPRS network, presenting instruction information to the field controller to adjust the operation of the agricultural machine according to agricultural crop control instructions, the administrator can also remotely access the service work website to monitor the crop information, the agricultural machinery is more reasonably and effectively utilized, and the problems in the background technology can be effectively solved.
In order to achieve the purpose, the invention provides an intelligent agricultural machine control method based on the Internet of things, which comprises the following steps: the method comprises the following steps:
s1) feature modeling: acquiring external characteristic specifications of different crops and livestock as sample pictures through a machine consisting of a camera and a DSP video processing system, performing normalization processing pretreatment operation of centralization and size standardization on each picture, performing filtering processing on an original image, realizing image denoising through median filtering to reduce the noise of the original image, performing graying on the filtered image, processing each image into a size of 32cm multiplied by 32cm, ensuring that the dimension of an image characteristic vector is the same as the number of random units of an input layer, and ensuring that the number of output units of an output layer can be the same as the number of categories of a data sample to be classified to obtain the characteristic vector of the image so as to obtain a characteristic growth model;
s2) agricultural information perception: monitoring various characteristics of crops through crop growth environment sensing equipment, breeding environment sensing equipment and animal identification and physiological sensing equipment, and positioning position information of a crop or animal breeding area through a GPS positioning module;
s3) agricultural information transmission: under the support of a wireless network, information is transmitted to a remote terminal host through a GPRS module every 5-30 minutes, and the system host is comprehensively processed and displayed on a display;
s4) obtaining the operation parameters of the agricultural machine: acquiring agricultural machine operation parameter signals including speed, steering angle and operation pump pressure signals through various sensors, acquiring a machine operation picture through a camera, and positioning monitored position information through a GPS positioning module;
s5) troubleshooting of agricultural machinery: the system host machine identifies and calculates the uploaded operation parameter signals, judges whether the current agricultural machine has a mechanical fault or not, sends alarm information containing fault types if the current agricultural machine has the mechanical fault, and sends normal use information of the machine if the current agricultural machine does not have the mechanical fault;
s6) agricultural information processing: the system host processes and judges the collected information, transmits an instruction to the field control machine through a GPRS network, and an administrator can also remotely access a service work website to monitor crop information, extracts crop and livestock characteristic information and model base data by using an image processing technology to compare and analyze, and records ingestion, exercise amount, disease occurrence rate and the like according to the acquired living habits and health states of livestock;
s7) agricultural machine operation: according to an agricultural crop control instruction, instruction information is presented to an on-site controller to adjust the operation of agricultural machinery, a fan, a water pump, a curtain drawing machine, a film rolling device, a window opening machine, heating, irrigation and other equipment can be adjusted to adjust the on-site planting environment, a system host can also calculate nutrition, fertilizer, moisture and the like required by crop growth, the on-site controller is adjusted to allocate nutrient solution according to technical parameters, irrigation operation is performed through sprinkling irrigation equipment, the system host guides the use of pesticides by searching a pest model library, a crop growth model library and an information guidance model library, the on-site controller adjusts the pesticide spraying equipment to spray the pesticide, real-time monitoring and control of pests are achieved, a feedlot is helped to master the estrus of livestock, and varieties are screened.
As a preferred technical scheme of the invention: the crop growth environment sensing equipment comprises a soil moisture sensor, a soil pH value sensor, a soil nutrient sensor, a carbon dioxide concentration sensor and image acquisition equipment.
As a preferred technical scheme of the invention: the aquaculture environment sensing equipment comprises an ammonia sensor and a water-soluble oxygen sensor.
As a preferred technical scheme of the invention: the animal identification and physiological sensing equipment comprises an ear tag and a gastric electronic tag.
Compared with the prior art, the invention has the beneficial effects that: the monitoring system can be used for monitoring various sensors of crops and livestock by modeling the crops and the livestock with different specifications, transmitting monitoring data to a remote host through a GPRS (general packet radio service) transmission module under the support of a wireless network, integrating and displaying the monitoring data on a display by the remote host, extracting characteristic information of the crops and the livestock and comparing and analyzing the characteristic information with model library data, sending an instruction by searching a pest model library, a crop growth model library and an information guidance model library, acquiring agricultural machine operation parameter signals including speed, steering angle and operation pump pressure signals through various sensors in advance before a field control machine regulates and controls the operation of the machine, acquiring a machine operation picture through a camera, positioning the monitored position information through a GPS (global positioning system) positioning module, identifying and calculating the uploaded operation parameter signals, judging whether the current agricultural machine has mechanical faults or not, and if the current agricultural machine has the mechanical faults, if the agricultural crop management and control instruction does not exist, the system host machine sends out normal use information of the machine, after the normal use is judged, the system host machine processes and judges the collected information, the instruction is transmitted to a field control machine through a GPRS network, the instruction information is presented to the field control machine according to the agricultural crop management and control instruction to adjust the operation of the agricultural machine, and an administrator can also remotely access a service work website to monitor the crop information.
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Fig. 1 is a step diagram of an intelligent agricultural machine control method based on the internet of things.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides the following technical solutions:
the first embodiment is as follows: the method comprises the steps of collecting external characteristic specifications of different crops and livestock as sample pictures through a machine consisting of a camera and a DSP video processing system, carrying out normalization processing pretreatment operation of centralization and size standardization on each picture, carrying out filtering processing on an original image, realizing image denoising through median filtering to reduce the noise of the original image, carrying out graying on the filtered image, processing each image into a size of 32cm multiplied by 32cm, ensuring that the dimension of an image characteristic vector is the same as the random unit number of an input layer, obtaining the characteristic vector of the image by ensuring that the output unit number of the output layer is the same as the category number of a data sample to be classified to obtain a characteristic growth model, and obtaining the characteristic growth model through a soil moisture sensor, a soil PH value sensor, a soil nutrient sensor, a carbon dioxide concentration sensor, an image collecting device, an ammonia gas sensor, a water-soluble oxygen sensor, The ear tag and the gastric electronic tag monitor various characteristics of crops, position information of a crop or an animal culture area is positioned through the GPS positioning module, information is transmitted to the remote terminal host computer every 5 minutes through the GPRS module under the support of a wireless network, the system host computer comprehensively processes and displays the information on a display, operating parameter signals of agricultural machinery including speed, steering angle and operating pump pressure signals are obtained through various sensors, a mechanical operating picture is collected through a camera, the position information monitored is positioned through the GPS positioning module, the system host computer identifies and calculates the uploaded operating parameter signals, whether mechanical faults exist in the current agricultural machinery is judged, if the mechanical faults exist, alarm information containing fault types is sent, if the mechanical normal use information does not exist, the system host computer processes and judges the collected information, the instructions are transmitted to an on-site controller through a GPRS network, an administrator can remotely visit a service work website to monitor crop information, characteristic information of crops and livestock is extracted by using an image processing technology and is contrastively analyzed with model base data, feeding, exercise amount, disease incidence and the like are recorded according to the acquired living habits and health states of the livestock, instruction information is presented to the on-site controller to adjust the operation of agricultural machinery according to agricultural crop control instructions, a fan, a water pump, a curtain pulling machine, a film rolling machine, a window opening machine, heating, irrigation and other equipment can be adjusted to adjust the on-site planting environment, a system host can also calculate nutrition, fertilizer, moisture and the like required by crop growth, the on-site controller is adjusted to configure nutrient solution according to technical parameters, irrigation operation is carried out through sprinkling irrigation equipment, and the system host searches for a pest model base, a crop growth model base, a model base, The information guides the model base to guide the use of pesticides, the pesticide spraying equipment is adjusted to spray the pesticides through the field control machine, real-time monitoring and control of pests are achieved, the livestock estrus control in a feeding farm is helped, and varieties are screened.
The system can send out instructions by searching a pest model library, a crop growth model library and an information guidance model library, before a field control machine regulates and controls the operation of the machine, agricultural machine operation parameter signals including speed, steering angle and operation pump pressure signals are obtained through various sensors in advance, a machine operation picture is collected through a camera, the position information monitored is positioned through a GPS positioning module, the uploaded operation parameter signals are identified and calculated, whether the current agricultural machine has a mechanical fault is judged, if the current agricultural machine has the mechanical fault, alarm information containing fault types is sent out, if the current machine does not have the mechanical normal use information, after the normal use is judged, a system host machine processes and judges the collected information, the instructions are transmitted to the field control machine through a GPRS network, and the instruction information is presented to the field control machine to regulate the operation of the agricultural machine according to the agricultural crop control instructions, the administrator can also remotely access the service work website to monitor the crop information.
Example two: the method comprises the steps of collecting external characteristic specifications of different crops and livestock as sample pictures through a machine consisting of a camera and a DSP video processing system, carrying out normalization processing pretreatment operation of centralization and size standardization on each picture, carrying out filtering processing on an original image, realizing image denoising through median filtering to reduce the noise of the original image, carrying out graying on the filtered image, processing each image into a size of 32cm multiplied by 32cm, ensuring that the dimension of an image characteristic vector is the same as the random unit number of an input layer, obtaining the characteristic vector of the image by ensuring that the output unit number of the output layer is the same as the category number of a data sample to be classified to obtain a characteristic growth model, and obtaining the characteristic growth model through a soil moisture sensor, a soil PH value sensor, a soil nutrient sensor, a carbon dioxide concentration sensor, an image collecting device, an ammonia gas sensor, a water-soluble oxygen sensor, The ear tag and the gastric electronic tag monitor various characteristics of crops, position information of a crop or an animal culture area is positioned through the GPS positioning module, information is transmitted to the remote terminal host computer every 15 minutes through the GPRS module under the support of a wireless network, the system host computer comprehensively processes and displays the information on a display, operating parameter signals of agricultural machinery including speed, steering angle and operating pump pressure signals are obtained through various sensors, a mechanical operating picture is collected through a camera, the position information monitored is positioned through the GPS positioning module, the system host computer identifies and calculates the uploaded operating parameter signals, whether mechanical faults exist in the current agricultural machinery is judged, if the mechanical faults exist, alarm information containing fault types is sent, if the mechanical normal use information does not exist, the system host computer processes and judges the collected information, the instructions are transmitted to an on-site controller through a GPRS network, an administrator can remotely visit a service work website to monitor crop information, characteristic information of crops and livestock is extracted by using an image processing technology and is contrastively analyzed with model base data, feeding, exercise amount, disease incidence and the like are recorded according to the acquired living habits and health states of the livestock, instruction information is presented to the on-site controller to adjust the operation of agricultural machinery according to agricultural crop control instructions, a fan, a water pump, a curtain pulling machine, a film rolling machine, a window opening machine, heating, irrigation and other equipment can be adjusted to adjust the on-site planting environment, a system host can also calculate nutrition, fertilizer, moisture and the like required by crop growth, the on-site controller is adjusted to configure nutrient solution according to technical parameters, irrigation operation is carried out through sprinkling irrigation equipment, and the system host searches for a pest model base, a crop growth model base, a model base, The information guides the model base to guide the use of pesticides, the pesticide spraying equipment is adjusted to spray the pesticides through the field control machine, real-time monitoring and control of pests are achieved, the livestock estrus control in a feeding farm is helped, and varieties are screened.
The system can send out instructions by searching a pest model library, a crop growth model library and an information guidance model library, before a field control machine regulates and controls the operation of the machine, agricultural machine operation parameter signals including speed, steering angle and operation pump pressure signals are obtained through various sensors in advance, a machine operation picture is collected through a camera, the position information monitored is positioned through a GPS positioning module, the uploaded operation parameter signals are identified and calculated, whether the current agricultural machine has a mechanical fault is judged, if the current agricultural machine has the mechanical fault, alarm information containing fault types is sent out, if the current machine does not have the mechanical normal use information, after the normal use is judged, a system host machine processes and judges the collected information, the instructions are transmitted to the field control machine through a GPRS network, and the instruction information is presented to the field control machine to regulate the operation of the agricultural machine according to the agricultural crop control instructions, the administrator can also remotely access the service work website to monitor the crop information.
Example three: the method comprises the steps of collecting external characteristic specifications of different crops and livestock as sample pictures through a machine consisting of a camera and a DSP video processing system, carrying out normalization processing pretreatment operation of centralization and size standardization on each picture, carrying out filtering processing on an original image, realizing image denoising through median filtering to reduce the noise of the original image, carrying out graying on the filtered image, processing each image into a size of 32cm multiplied by 32cm, ensuring that the dimension of an image characteristic vector is the same as the random unit number of an input layer, obtaining the characteristic vector of the image by ensuring that the output unit number of the output layer is the same as the category number of a data sample to be classified to obtain a characteristic growth model, and obtaining the characteristic growth model through a soil moisture sensor, a soil PH value sensor, a soil nutrient sensor, a carbon dioxide concentration sensor, an image collecting device, an ammonia gas sensor, a water-soluble oxygen sensor, The ear tag and the gastric electronic tag monitor various characteristics of crops, position information of a crop or an animal culture area is positioned through the GPS positioning module, information is transmitted to the remote terminal host computer every 30 minutes through the GPRS module under the support of a wireless network, the system host computer comprehensively processes and displays the information on a display, operating parameter signals of agricultural machinery including speed, steering angle and operating pump pressure signals are obtained through various sensors, a mechanical operating picture is collected through a camera, the position information monitored is positioned through the GPS positioning module, the system host computer identifies and calculates the uploaded operating parameter signals, whether mechanical faults exist in the current agricultural machinery is judged, if the mechanical faults exist, alarm information containing fault types is sent, if the mechanical normal use information does not exist, the system host computer processes and judges the collected information, the instructions are transmitted to an on-site controller through a GPRS network, an administrator can remotely visit a service work website to monitor crop information, characteristic information of crops and livestock is extracted by using an image processing technology and is contrastively analyzed with model base data, feeding, exercise amount, disease incidence and the like are recorded according to the acquired living habits and health states of the livestock, instruction information is presented to the on-site controller to adjust the operation of agricultural machinery according to agricultural crop control instructions, a fan, a water pump, a curtain pulling machine, a film rolling machine, a window opening machine, heating, irrigation and other equipment can be adjusted to adjust the on-site planting environment, a system host can also calculate nutrition, fertilizer, moisture and the like required by crop growth, the on-site controller is adjusted to configure nutrient solution according to technical parameters, irrigation operation is carried out through sprinkling irrigation equipment, and the system host searches for a pest model base, a crop growth model base, a model base, The information guides the model base to guide the use of pesticides, the pesticide spraying equipment is adjusted to spray the pesticides through the field control machine, real-time monitoring and control of pests are achieved, the livestock estrus control in a feeding farm is helped, and varieties are screened.
The system can send out instructions by searching a pest model library, a crop growth model library and an information guidance model library, before a field control machine regulates and controls the operation of the machine, agricultural machine operation parameter signals including speed, steering angle and operation pump pressure signals are obtained through various sensors in advance, a machine operation picture is collected through a camera, the position information monitored is positioned through a GPS positioning module, the uploaded operation parameter signals are identified and calculated, whether the current agricultural machine has a mechanical fault is judged, if the current agricultural machine has the mechanical fault, alarm information containing fault types is sent out, if the current machine does not have the mechanical normal use information, after the normal use is judged, a system host machine processes and judges the collected information, the instructions are transmitted to the field control machine through a GPRS network, and the instruction information is presented to the field control machine to regulate the operation of the agricultural machine according to the agricultural crop control instructions, the administrator can also remotely access the service work website to monitor the crop information.
The invention has the advantages that: the system can be used for modeling different specifications of crops and livestock, monitoring various sensors of the crops and the livestock, transmitting monitoring data to a remote host through a GPRS (general packet radio service) transmission module under the support of a wireless network, integrating and displaying the monitoring data on a display by the remote host, extracting characteristic information of the crops and the livestock and carrying out comparative analysis on the characteristic information and model library data, sending an instruction by searching a pest model library, a crop growth model library and an information guidance model library, acquiring operating parameter signals of the agricultural machinery including speed, steering angle and pressure signals of an operating pump through various sensors in advance before a field control machine regulates and controls the operation of the machinery, acquiring operation pictures of the machinery through a camera, positioning the monitored position information through a GPS (global positioning system) positioning module, identifying and calculating the uploaded operating parameter signals, and judging whether the current agricultural machinery has mechanical faults or not, if the agricultural crop management and control instruction exists, the instruction information is presented to the field control machine to adjust the operation of the agricultural machinery, and an administrator can also remotely access a service work website to monitor the crop information.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (1)
1. An intelligent agricultural machine control method based on the Internet of things is characterized by comprising the following steps:
s1) feature modeling: acquiring external characteristic specifications of different crops and livestock as sample pictures through a machine consisting of a camera and a DSP video processing system, performing normalization processing pretreatment operation of centralization and size standardization on each picture, performing filtering processing on an original image, realizing image denoising through median filtering to reduce the noise of the original image, performing graying on the filtered image, processing each image into a size of 32cm multiplied by 32cm, ensuring that the dimension of an image characteristic vector is the same as the number of random units of an input layer, and ensuring that the number of output units of an output layer can be the same as the number of categories of a data sample to be classified to obtain the characteristic vector of the image so as to obtain a characteristic growth model;
s2) agricultural information perception: monitoring various characteristics of crops through crop growth environment sensing equipment, breeding environment sensing equipment and animal identification and physiological sensing equipment, and positioning position information of a crop or animal breeding area through a GPS positioning module;
s3) agricultural information transmission: under the support of a wireless network, information is transmitted to a remote terminal host through a GPRS module every 5-30 minutes, and the system host is comprehensively processed and displayed on a display;
s4) obtaining the operation parameters of the agricultural machine: acquiring agricultural machine operation parameter signals including speed, steering angle and operation pump pressure signals through various sensors, acquiring a machine operation picture through a camera, and positioning monitored position information through a GPS positioning module;
s5) troubleshooting of agricultural machinery: the system host machine identifies and calculates the uploaded operation parameter signals, judges whether the current agricultural machine has a mechanical fault or not, sends alarm information containing fault types if the current agricultural machine has the mechanical fault, and sends normal use information of the machine if the current agricultural machine does not have the mechanical fault;
s6) agricultural information processing: the system host processes and judges the collected information, transmits an instruction to the field control machine through a GPRS network, and an administrator remotely accesses a service work website to monitor crop information, extracts crop and livestock characteristic information and model base data by using an image processing technology to compare and analyze, and records the ingestion, the amount of exercise and the disease occurrence rate according to the acquired living habits and health states of livestock;
s7) agricultural machine operation: according to an agricultural crop control instruction, instruction information is presented to a field controller to adjust the operation of agricultural machinery, a fan, a water pump, a curtain drawing machine, a film rolling device, a window opening machine and heating and irrigation equipment are adjusted to adjust the field planting environment, a system host calculates nutrition, fertilizer and moisture required by crop growth, the field controller is adjusted to configure nutrient solution according to technical parameters, irrigation operation is performed through sprinkling irrigation equipment, the system host guides the use of pesticides by searching a pest model library, a crop growth model library and an information guidance model library, pesticide spraying equipment is adjusted to spray the pesticides through the field controller, real-time monitoring and control on pests are achieved, a feedlot is helped to master the estrus period of livestock, and varieties are screened; the crop growth environment sensing equipment comprises a soil moisture sensor, a soil pH value sensor, a soil nutrient sensor, a carbon dioxide concentration sensor and image acquisition equipment; the aquaculture environment sensing equipment comprises an ammonia sensor and a water-soluble oxygen sensor; the animal identification and physiological sensing equipment comprises an ear tag and a gastric electronic tag.
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| CN111480554A (en) * | 2020-06-04 | 2020-08-04 | 宁夏大学 | Water, fertilizer, gas and hot chemical integrated intelligent irrigation system for farmland irrigation area |
| CN113597941A (en) * | 2021-09-03 | 2021-11-05 | 新疆农业科学院农业机械化研究所 | Greenhouse intelligent environment regulation and control system and device |
| CN115185216A (en) * | 2022-07-18 | 2022-10-14 | 江苏东久机械有限公司 | Agricultural machinery intelligence control unit |
| CN117854012B (en) * | 2024-03-07 | 2024-05-14 | 成都智慧城市信息技术有限公司 | Crop environment monitoring method and system based on big data |
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