An ecological agriculture low-cost industrialized production system convenient to operate

Abstract

The application discloses an ecological agricultural low-cost industrialized production system convenient to operate, relates to the technical field of industrialized production, and comprises a data acquisition module, a data processing module, a modeling and prediction module, a decision control module, a resource utilization module and an application module. The data acquisition module is used for monitoring the environmental conditions of a field by using a sensor to obtain an original environmental data set. The data processing module is used for pre-processing the original environmental data set by using an edge computing method and obtaining a high-quality environmental data set. The modeling and prediction module is used for constructing a prediction model based on a machine learning algorithm and the high-quality data set, inputting the high-quality data set, and outputting a prediction result. The decision control module is used for executing management instructions through an automatic control system based on the prediction result, detecting abnormal conditions, proposing processing suggestions, and obtaining planting environment suggestions.

Description

Technical Field

[0001] This invention relates to the field of industrial production technology, and in particular to a convenient and low-cost industrial production system for ecological agriculture. Background Technology

[0002] Industrialized production technology refers to the intelligent and refined management of the entire agricultural production process by integrating advanced technologies such as the Internet of Things (IoT), machine learning, automated control, and resource recycling. This technology not only covers digital processes from environmental monitoring and data processing to predictive modeling, but also includes sustainable practices such as water resource reuse, organic waste composting, and green agricultural input research and development.

[0003] In the field of industrialized production technology, traditional agriculture relies on limited manual measurements, making it difficult to achieve real-time and comprehensive monitoring of field environmental conditions. At the same time, traditional methods suffer from delays and errors in data preprocessing, affecting subsequent analysis results. Furthermore, traditional prediction models are often based on simple statistical methods and cannot capture complex nonlinear relationships. In addition, the efficiency of water and fertilizer use in traditional agricultural production is low, which easily leads to waste and environmental pollution. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a convenient and low-cost industrialized production system for ecological agriculture to solve the problems of low efficiency in the use of water resources and fertilizers in traditional agricultural production, which easily leads to waste and environmental pollution.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a convenient and low-cost industrialized production system for ecological agriculture, comprising:

[0008] The system includes a data acquisition module, a data processing module, a modeling and prediction module, a decision control module, a resource utilization module, and an application module.

[0009] The data acquisition module is used to monitor the environmental conditions of the field using sensors to obtain the raw environmental dataset;

[0010] The data processing module is used to preprocess the original environmental dataset using edge computing methods to obtain a high-quality environmental dataset.

[0011] The modeling and prediction module is used to build a prediction model based on machine learning algorithms and high-quality datasets, and to take high-quality datasets as input and output prediction results.

[0012] The decision control module is used to execute management instructions through an automated control system based on the prediction results, detect abnormal situations and propose handling suggestions to obtain planting environment suggestions;

[0013] The resource utilization module is used to reuse the water resources in the planting environment proposal by adopting rainwater collection and wastewater recycling methods, develop organic waste composting facilities, and convert agricultural production waste into organic fertilizer to obtain a resource recycling system.

[0014] The application module is used to study the resource recycling system using microbial screening methods, design special plant nutrient solutions and beneficial microbial communities, test and evaluate the effects, and obtain agricultural input products.

[0015] As a preferred embodiment of the convenient and low-cost industrialized ecological agricultural production system described in this invention, the step of using sensors to monitor the environmental conditions of the field and obtain the original environmental dataset includes:

[0016] The integrated intelligent sensor array (ISSA) is installed in selected fields to ensure coverage of the entire planting area;

[0017] Environmental data is collected using a weighted average method, with each sensor collecting data once at a set time interval;

[0018] The collected data is transmitted to the central control platform in real time. The data is initially stored during the transmission process to form the original environmental dataset.

[0019] As a preferred embodiment of the convenient and low-cost industrialized production system for ecological agriculture described in this invention, the step of preprocessing the original environmental dataset using edge computing to obtain a high-quality environmental dataset involves the following steps:

[0020] Data cleaning algorithms are executed in real time on edge devices (ED) to remove outliers and noise points that occur during sensor transmission.

[0021] The sliding window mid-range filter (SWMF) method is used to reduce the impact of instantaneous fluctuations and preserve the true data trend.

[0022] The Z-Score standardization formula is used to standardize the cleaned data, making the data from different sensors comparable.

[0023] Principal Component Analysis (PCA) is used to extract key features, reducing data dimensionality while retaining maximum information content. Data dimensionality reduction is achieved by constructing the covariance matrix and solving for the eigenvectors.

[0024] The pre-processed data is time-synchronized on the edge device to ensure that data from different sensors have the same reference timestamp, resulting in a high-quality environmental dataset.

[0025] As a preferred embodiment of the convenient and low-cost industrialized ecological agriculture production system described in this invention, the specific steps for constructing a prediction model based on machine learning algorithms and high-quality datasets, inputting the high-quality datasets, and outputting prediction results are as follows:

[0026] The high-quality environmental dataset obtained through edge computing preprocessing is organized and labeled, and the dataset is divided into training set, validation set and test set according to time series characteristics;

[0027] Feature selection is performed on high-quality environmental datasets. The recursive feature elimination (RFE) method is used to select the feature variables that have the greatest impact on crop growth. Interaction terms and multinomial features are added to capture nonlinear relationships, and feature variables are obtained.

[0028] A prediction model is built based on a Long Short-Term Memory (LSTM) network. The input features are given, and the output prediction result Y is expressed as follows:

[0029] Y = σ(W·tanh(U·X+b))

[0030] Where Y represents the prediction result, X represents the feature variables obtained after feature selection and engineering, W is the weight matrix, U is the transformation matrix, b is the bias term, σ is the activation function, and tanh is the hyperbolic tangent function.

[0031] The prediction results are used to guide no-till, no-weed, and no-pesticide operations.

[0032] As a preferred embodiment of the convenient and low-cost industrialized ecological agriculture production system described in this invention, the steps of obtaining planting environment suggestions by executing management commands through an automated control system based on prediction results, detecting abnormal situations, and proposing handling suggestions are as follows:

[0033] Receive the prediction result Y from the modeling and prediction module, parse the prediction data, and extract key information;

[0034] Based on the analyzed prediction results, corresponding management instructions are generated;

[0035] If the forecast indicates that an increase in irrigation is needed, an "Increase Irrigation" command is generated.

[0036] When the forecast indicates insufficient soil nutrients, an instruction to "supplement with specific fertilizers" is generated.

[0037] The generated management instructions are transmitted to the automated control system, which then automatically adjusts the operating parameters of the irrigation system, fertilization equipment, and pest and disease control facilities according to the instructions.

[0038] By using sensor networks to monitor changes in field environmental conditions in real time, and combining this with trend information provided by predictive models, multi-dimensional data analysis is conducted.

[0039] An anomaly detection algorithm is introduced, based on threshold anomaly detection, to identify environmental parameter changes that exceed the normal range;

[0040] When an anomaly is detected, an early warning mechanism is immediately triggered, notifying managers via SMS, email, and mobile application, and providing specific handling suggestions, including adjusting irrigation time, checking sensor status, and initiating emergency pest and disease control measures.

[0041] As a preferred embodiment of the convenient and low-cost industrialized ecological agricultural production system described in this invention, the specific steps for reusing water resources in the planting environment using rainwater harvesting and wastewater recycling methods are as follows:

[0042] Install rainwater collection devices around the fields;

[0043] The collected rainwater undergoes preliminary filtration to remove large particles and suspended solids;

[0044] Install a wastewater recycling system (WRS) to collect irrigation tailwater and cleaning wastewater;

[0045] The recycled wastewater undergoes multi-stage purification treatment, including three stages: physical filtration, chemical precipitation, and biodegradation.

[0046] Physical filtration (PF) removes suspended solids.

[0047] Chemical precipitation of CP removes heavy metal ions;

[0048] Biodegradable BD decomposes organic pollutants through microorganisms, ultimately meeting irrigation water quality standards;

[0049] Treated rainwater and recycled wastewater are introduced into the WST storage tank and then rationally distributed to various parts of the field through the SIS intelligent irrigation system based on crop needs and feedback from soil moisture sensors.

[0050] As a preferred embodiment of the convenient and low-cost industrialized ecological agricultural production system described in this invention, the specific steps for developing organic waste composting facilities to convert agricultural production waste into organic fertilizer and obtain a resource recycling system are as follows:

[0051] Mature organic fertilizer is collected regularly from composting facilities, tested and qualified, and then applied to fields to replenish soil nutrients.

[0052] Nutrients in plant residues are recovered through a closed nutrient cycling system (CNCS).

[0053] By combining the effects of specific microorganisms, these resources can be reused in the next crop planting, forming a sustainable resource recycling system.

[0054] The prediction results are used to guide no-till, no-weed, and no-pesticide operations. The specific steps are as follows:

[0055] Biodegradable planting bags are used, which are directly filled with the substrate needed for plant growth, allowing plant roots to grow in a specific space. Black mulch is laid to block sunlight, inhibit weed seed germination, maintain soil moisture and regulate temperature. At the same time, ladybugs are introduced to prey on aphids and establish a symbiotic relationship.

[0056] Based on sensor monitoring of soil moisture and plant water requirements, valves and drip irrigation equipment are automatically controlled.

[0057] Solar panels are used to convert electricity into power, which is then stored in batteries to provide power for irrigation systems and automated control equipment.

[0058] Rainwater and wastewater are collected, and after sedimentation, filtration, and disinfection, they are returned for irrigation.

[0059] As a preferred embodiment of the convenient and low-cost industrialized ecological agricultural production system described in this invention, the following steps are taken: The resource recycling system is studied using a microbial screening method; a specialized plant nutrient solution and beneficial microbial flora are designed; the effects are tested and evaluated to obtain agricultural input products.

[0060] Microbial samples were collected from the resource recycling system, and the microbial diversity in the samples was analyzed using high-throughput sequencing (HTS) technology.

[0061] Based on the functional gene marker FGM, microbial species with the potential to promote plant growth were initially screened, as expressed in the following formula:

[0062] P = σ(H·M + F);

[0063] Where P represents the microbial species that have the potential to promote plant growth, H represents the high-throughput sequencing data matrix, M is the microbial diversity matrix, F is the functional gene marker vector, and σ is the selection function.

[0064] Microbial species P with the potential to promote plant growth will be selected and cultured in pure form under laboratory conditions.

[0065] Based on the functional validation results, a special plant nutrient solution was designed, and a beneficial microbial flora preparation was developed.

[0066] In the selected experimental fields, the designed special plant nutrient solution and beneficial microbial flora were applied to the actual planting process;

[0067] Record key crop indicators and compare them with the control group;

[0068] Strict safety testing (ST) is conducted to ensure that new agricultural input products are harmless to human health.

[0069] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the convenient-to-operate, low-cost industrialized production system for ecological agriculture as described in the first aspect of the present invention.

[0070] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the convenient-to-operate, low-cost industrialized production system for ecological agriculture as described in the first aspect of the present invention.

[0071] The beneficial effects of this invention are as follows: By installing an integrated intelligent sensor array in selected fields, comprehensive and real-time environmental condition monitoring is achieved, ensuring coverage of the entire planting area. Data cleaning algorithms are executed on edge devices (EDs), employing sliding window midpoint filtering, Z-score normalization, and principal component analysis to transform raw environmental data into a high-quality environmental dataset. The preprocessed data significantly improves quality, eliminating outliers and redundant information, ensuring the accuracy and reliability of subsequent modeling and prediction. Through feature selection, LSTM model training and optimization, a prediction model is built based on the high-quality dataset. Inputting feature variables and outputting prediction results enables accurate prediction of crop growth status and development trends, greatly improving prediction accuracy and generalization ability. This allows the system to plan irrigation, fertilization, and other management measures in advance based on prediction results, thereby optimizing resource allocation, reducing waste, and improving agricultural production efficiency. By parsing the prediction results to generate management instructions and combining them with real-time monitoring and anomaly detection algorithms, intelligent agricultural management decision-making and automated control system operation are achieved. Attached Figure Description

[0072] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0073] Figure 1This is a schematic diagram of the easy-to-operate, low-cost industrialized production system for ecological agriculture in Example 1. Detailed Implementation

[0074] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0075] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0076] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0077] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a convenient and low-cost industrialized production system for ecological agriculture, comprising:

[0078] The system includes a data acquisition module, a data processing module, a modeling and prediction module, a decision control module, a resource utilization module, and an application module.

[0079] The data acquisition module is used to monitor the environmental conditions of the field using sensors to obtain raw environmental datasets;

[0080] The integrated intelligent sensor array (ISSA) is installed in selected fields to ensure coverage of the entire planting area;

[0081] Environmental data is collected using a weighted average method, with each sensor collecting data once at a set time interval;

[0082] The collected data is transmitted to the central control platform in real time. The data is initially stored during the transmission process to form the original environmental dataset.

[0083] The data processing module is used to preprocess the original environmental dataset using edge computing methods to obtain a high-quality environmental dataset.

[0084] Data cleaning algorithms are executed in real time on edge devices (ED) to remove outliers and noise points that occur during sensor transmission.

[0085] The sliding window mid-range filter (SWMF) method is used to reduce the impact of instantaneous fluctuations and preserve the true data trend.

[0086] The Z-Score standardization formula is used to standardize the cleaned data, making the data from different sensors comparable.

[0087] Principal Component Analysis (PCA) is used to extract key features, reducing data dimensionality while retaining maximum information content. Data dimensionality reduction is achieved by constructing the covariance matrix and solving for the eigenvectors.

[0088] The pre-processed data is time-synchronized on the edge device to ensure that data from different sensors have the same reference timestamp, resulting in a high-quality environmental dataset.

[0089] The modeling and prediction module is used to build prediction models based on machine learning algorithms and high-quality datasets, and to take high-quality datasets as input and output prediction results.

[0090] The high-quality environmental dataset obtained through edge computing preprocessing is organized and labeled, and the dataset is divided into training set, validation set and test set according to time series characteristics;

[0091] Feature selection is performed on high-quality environmental datasets. The recursive feature elimination (RFE) method is used to select the feature variables that have the greatest impact on crop growth. Interaction terms and multinomial features are added to capture nonlinear relationships, and feature variables are obtained.

[0092] A prediction model is built based on a Long Short-Term Memory (LSTM) network. The input features are given, and the output prediction result Y is expressed as follows:

[0093] Y = σ(W·tanh(U·X+b))

[0094] Where Y represents the prediction result, X represents the feature variables obtained after feature selection and engineering, W is the weight matrix, U is the transformation matrix, b is the bias term, σ is the activation function, and tanh is the hyperbolic tangent function.

[0095] The prediction results are used to guide no-till, no-weed, and no-pesticide operations.

[0096] The decision control module is used to execute management instructions through the automated control system based on the prediction results, detect abnormal situations and propose handling suggestions to obtain planting environment recommendations;

[0097] Receive the prediction result Y from the modeling and prediction module, parse the prediction data, and extract key information;

[0098] Based on the analyzed prediction results, corresponding management instructions are generated;

[0099] If the forecast indicates that an increase in irrigation is needed, an "Increase Irrigation" command is generated.

[0100] When the forecast indicates insufficient soil nutrients, an instruction to "supplement with specific fertilizers" is generated.

[0101] The generated management instructions are transmitted to the automated control system, which then automatically adjusts the operating parameters of the irrigation system, fertilization equipment, and pest and disease control facilities according to the instructions.

[0102] By using sensor networks to monitor changes in field environmental conditions in real time, and combining this with trend information provided by predictive models, multi-dimensional data analysis is conducted.

[0103] An anomaly detection algorithm is introduced, based on threshold anomaly detection, to identify environmental parameter changes that exceed the normal range;

[0104] When an anomaly is detected, an early warning mechanism is immediately triggered, notifying managers via SMS, email, and mobile application, and providing specific handling suggestions, including adjusting irrigation time, checking sensor status, and initiating emergency pest and disease control measures.

[0105] The resource utilization module is used to reuse the water resources in the planting environment recommendations by adopting rainwater harvesting and wastewater recycling methods, develop organic waste composting facilities, and convert agricultural production waste into organic fertilizer to obtain a resource recycling system.

[0106] Install rainwater collection devices around the fields;

[0107] The collected rainwater undergoes preliminary filtration to remove large particles and suspended solids;

[0108] Install a wastewater recycling system (WRS) to collect irrigation tailwater and cleaning wastewater;

[0109] The recycled wastewater undergoes multi-stage purification treatment, including three stages: physical filtration, chemical precipitation, and biodegradation.

[0110] Physical filtration (PF) removes suspended solids.

[0111] Chemical precipitation of CP removes heavy metal ions;

[0112] Biodegradable BD decomposes organic pollutants through microorganisms, ultimately meeting irrigation water quality standards;

[0113] Treated rainwater and recycled wastewater are introduced into the WST storage tank and then rationally distributed to various parts of the field through the SIS intelligent irrigation system based on crop needs and feedback from soil moisture sensors.

[0114] Mature organic fertilizer is collected regularly from composting facilities, tested and qualified, and then applied to fields to replenish soil nutrients.

[0115] Nutrients in plant residues are recovered through a closed nutrient cycling system (CNCS).

[0116] By combining the effects of specific microorganisms, these resources can be reused in the next crop planting, forming a sustainable resource recycling system.

[0117] The prediction results are used to guide no-till, no-weed, and no-pesticide operations. The specific steps are as follows:

[0118] Biodegradable planting bags are used, which are directly filled with the substrate needed for plant growth, allowing plant roots to grow in a specific space. Black mulch is laid to block sunlight, inhibit weed seed germination, maintain soil moisture and regulate temperature. At the same time, ladybugs are introduced to prey on aphids and establish a symbiotic relationship.

[0119] Based on sensor monitoring of soil moisture and plant water requirements, valves and drip irrigation equipment are automatically controlled.

[0120] Solar panels are used to convert electricity into power, which is then stored in batteries to provide power for irrigation systems and automated control equipment.

[0121] Rainwater and wastewater are collected, and after sedimentation, filtration, and disinfection, they are returned for irrigation.

[0122] The application module is used to study the resource recycling system using microbial screening methods, design special plant nutrient solutions and beneficial microbial communities, test and evaluate the effects, and obtain agricultural input products.

[0123] Microbial samples were collected from the resource recycling system, and the microbial diversity in the samples was analyzed using high-throughput sequencing (HTS) technology.

[0124] Based on the functional gene marker FGM, microbial species with the potential to promote plant growth were initially screened, as expressed in the following formula:

[0125] P = σ(H·M + F);

[0126] Where P represents the microbial species that have the potential to promote plant growth, H represents the high-throughput sequencing data matrix, M is the microbial diversity matrix, F is the functional gene marker vector, and σ is the selection function.

[0127] Microbial species P with the potential to promote plant growth will be selected and cultured in pure form under laboratory conditions.

[0128] Based on the functional validation results, a special plant nutrient solution was designed, and a beneficial microbial flora preparation was developed.

[0129] In the selected experimental fields, the designed special plant nutrient solution and beneficial microbial flora were applied to the actual planting process;

[0130] Record key crop indicators and compare them with the control group;

[0131] Strict safety testing (ST) is conducted to ensure that new agricultural input products are harmless to human health.

[0132] This embodiment also provides a computer device suitable for a convenient, low-cost industrialized production system for ecological agriculture, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the convenient, low-cost industrialized production system for ecological agriculture as proposed in the above embodiment.

[0133] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0134] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the convenient and low-cost industrialized production system for ecological agriculture as proposed in the above embodiments. The storage medium can 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 Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0135] In summary, this invention, through the installation of an integrated intelligent sensor array in selected fields, ensures coverage of the entire planting area, enabling comprehensive and real-time monitoring of environmental conditions. By executing data cleaning algorithms on edge devices (EDs) and employing sliding window mean filtering, Z-score normalization, and principal component analysis, the raw environmental data is transformed into a high-quality environmental dataset. The preprocessed data significantly improves quality, eliminating outliers and redundant information, ensuring the accuracy and reliability of subsequent modeling and prediction. Through feature selection, LSTM model training and optimization, a prediction model is constructed based on the high-quality dataset. Inputting feature variables and outputting prediction results, the system achieves accurate prediction of crop growth status and development trends, greatly improving prediction accuracy and generalization ability. This allows the system to plan irrigation, fertilization, and other management measures in advance based on the prediction results, thereby optimizing resource allocation, reducing waste, and improving agricultural production efficiency. By parsing the prediction results to generate management instructions and combining them with real-time monitoring and anomaly detection algorithms, intelligent agricultural management decision-making and automated control system operation are achieved.

[0136] 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 it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A user-friendly, low-cost industrialized production system for ecological agriculture, characterized in that: include: The system includes a data acquisition module, a data processing module, a modeling and prediction module, a decision control module, a resource utilization module, and an application module. The data acquisition module is used to monitor the environmental conditions of the field using sensors to obtain the raw environmental dataset; The data processing module is used to preprocess the original environmental dataset using edge computing methods to obtain a high-quality environmental dataset. The modeling and prediction module is used to build a prediction model based on machine learning algorithms and high-quality datasets, and to take high-quality datasets as input and output prediction results. The decision control module is used to execute management instructions through an automated control system based on the prediction results, detect abnormal situations and propose handling suggestions to obtain planting environment suggestions; The resource utilization module is used to reuse the water resources in the planting environment proposal by adopting rainwater collection and wastewater recycling methods, develop organic waste composting facilities, and convert agricultural production waste into organic fertilizer to obtain a resource recycling system. The application module is used to study the resource recycling system using microbial screening methods, design special plant nutrient solutions and beneficial microbial communities, test and evaluate the effects, and obtain agricultural input products.

2. The easy-to-operate, low-cost industrialized production system for ecological agriculture as described in claim 1, characterized in that: The specific steps for using sensors to monitor environmental conditions in the field and obtain raw environmental datasets are as follows: The integrated intelligent sensor array (ISSA) is installed in selected fields to ensure coverage of the entire planting area; Environmental data is collected using a weighted average method, with each sensor collecting data once at a set time interval; The collected data is transmitted to the central control platform in real time. The data is initially stored during the transmission process to form the original environmental dataset.

3. The easy-to-operate, low-cost industrialized production system for ecological agriculture as described in claim 2, characterized in that: The method of preprocessing the original environmental dataset using edge computing to obtain a high-quality environmental dataset involves the following steps: Data cleaning algorithms are executed in real time on edge devices (ED) to remove outliers and noise points that occur during sensor transmission. The sliding window mid-range filter (SWMF) method is used to reduce the impact of instantaneous fluctuations and preserve the true data trend. The Z-Score standardization formula is used to standardize the cleaned data, making the data from different sensors comparable. Principal Component Analysis (PCA) is used to extract key features, reducing data dimensionality while retaining maximum information content. Data dimensionality reduction is achieved by constructing the covariance matrix and solving for the eigenvectors. The pre-processed data is time-synchronized on the edge device to ensure that data from different sensors have the same reference timestamp, resulting in a high-quality environmental dataset.

4. The easy-to-operate, low-cost industrialized production system for ecological agriculture as described in claim 3, characterized in that: The specific steps for constructing a prediction model based on machine learning algorithms and high-quality datasets, inputting a high-quality dataset, and outputting prediction results are as follows: The high-quality environmental dataset obtained through edge computing preprocessing is organized and labeled, and the dataset is divided into training set, validation set and test set according to time series characteristics; Feature selection is performed on high-quality environmental datasets. The recursive feature elimination (RFE) method is used to select the feature variables that have the greatest impact on crop growth. Interaction terms and multinomial features are added to capture nonlinear relationships, and feature variables are obtained. A prediction model is built based on a Long Short-Term Memory (LSTM) network. The input features are given, and the output prediction result Y is expressed as follows: Y = σ(W·tanh(U·X+b)) Where Y represents the prediction result, X represents the feature variables obtained after feature selection and engineering, W is the weight matrix, U is the transformation matrix, b is the bias term, σ is the activation function, and tanh is the hyperbolic tangent function. The prediction results are used to guide no-till, no-weed, and no-pesticide operations.

5. The easy-to-operate, low-cost industrialized production system for ecological agriculture as described in claim 4, characterized in that: Based on the prediction results, the automated control system executes management commands and detects abnormal situations, providing handling suggestions to obtain planting environment recommendations. The specific steps are as follows: Receive the prediction result Y from the modeling and prediction module, parse the prediction data, and extract key information; Based on the analyzed prediction results, corresponding management instructions are generated; If the forecast indicates that an increase in irrigation is needed, an "Increase Irrigation" command is generated. When the forecast indicates insufficient soil nutrients, an instruction to "supplement with specific fertilizers" is generated. The generated management instructions are transmitted to the automated control system, which then automatically adjusts the operating parameters of the irrigation system, fertilization equipment, and pest and disease control facilities according to the instructions. By using sensor networks to monitor changes in field environmental conditions in real time, and combining this with trend information provided by predictive models, multi-dimensional data analysis is conducted. An anomaly detection algorithm is introduced, based on threshold anomaly detection, to identify environmental parameter changes that exceed the normal range; When an anomaly is detected, an early warning mechanism is immediately triggered, notifying managers via SMS, email, and mobile application, and providing specific handling suggestions, including adjusting irrigation time, checking sensor status, and initiating emergency pest and disease control measures.

6. The easy-to-operate, low-cost industrialized production system for ecological agriculture as described in claim 5, characterized in that: The proposed method of reusing water resources in the planting environment using rainwater harvesting and wastewater recycling involves the following steps: Install rainwater collection devices around the fields; The collected rainwater undergoes preliminary filtration to remove large particles and suspended solids; Install a wastewater recycling system (WRS) to collect irrigation tailwater and cleaning wastewater; The recycled wastewater undergoes multi-stage purification treatment, including three stages: physical filtration, chemical precipitation, and biodegradation. Physical filtration (PF) removes suspended solids. Chemical precipitation of CP removes heavy metal ions; Biodegradable BD decomposes organic pollutants through microorganisms, ultimately meeting irrigation water quality standards; Treated rainwater and recycled wastewater are introduced into the WST storage tank and then rationally distributed to various parts of the field through the SIS intelligent irrigation system based on crop needs and feedback from soil moisture sensors.

7. The easy-to-operate, low-cost industrialized production system for ecological agriculture as described in claim 6, characterized in that: The development of organic waste composting facilities to convert agricultural production waste into organic fertilizer and obtain a resource recycling system involves the following specific steps: Mature organic fertilizer is collected regularly from composting facilities, tested and qualified, and then applied to fields to replenish soil nutrients. Nutrients in plant residues are recovered through a closed nutrient cycling system (CNCS). By combining the effects of specific microorganisms, these resources can be reused in the next crop planting, forming a sustainable resource recycling system. The prediction results are used to guide no-till, no-weed, and no-pesticide operations. The specific steps are as follows: Biodegradable planting bags are used, which are directly filled with the substrate needed for plant growth, allowing plant roots to grow in a specific space. Black mulch is laid to block sunlight, inhibit weed seed germination, maintain soil moisture and regulate temperature. At the same time, ladybugs are introduced to prey on aphids and establish a symbiotic relationship. Based on sensor monitoring of soil moisture and plant water requirements, valves and drip irrigation equipment are automatically controlled. Solar panels are used to convert electricity into power, which is then stored in batteries to provide power for irrigation systems and automated control equipment. Rainwater and wastewater are collected, and after sedimentation, filtration, and disinfection, they are returned for irrigation.

8. The easy-to-operate, low-cost industrialized production system for ecological agriculture as described in claim 6, characterized in that: The method employed in this study involves microbial screening to investigate the resource recycling system, designing specialized plant nutrient solutions and beneficial microbial communities, testing and evaluating their effectiveness, and ultimately obtaining agricultural input products. The specific steps are as follows: Microbial samples were collected from the resource recycling system, and the microbial diversity in the samples was analyzed using high-throughput sequencing (HTS) technology. Based on the functional gene marker FGM, microbial species with the potential to promote plant growth were initially screened, as expressed in the following formula: P = σ(H·M + F); Where P represents the microbial species that have the potential to promote plant growth, H represents the high-throughput sequencing data matrix, M is the microbial diversity matrix, F is the functional gene marker vector, and σ is the selection function. Microbial species P with the potential to promote plant growth will be selected and cultured in pure form under laboratory conditions. Based on the functional validation results, a special plant nutrient solution was designed, and a beneficial microbial flora preparation was developed. In the selected experimental fields, the designed special plant nutrient solution and beneficial microbial flora were applied to the actual planting process; Record key crop indicators and compare them with the control group; Strict safety testing (ST) is conducted to ensure that new agricultural input products are harmless to human health.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the easy-to-operate, low-cost industrialized production system for ecological agriculture as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the easy-to-operate, low-cost industrialized production system for ecological agriculture as described in any one of claims 1 to 8.

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