Konjak continuous cropping obstacle prevention and soil improvement system based on multi-factor analysis

Abstract

The invention provides a konjak continuous cropping obstacle prevention and soil improvement system based on multi-factor analysis, and relates to the technical field of soil improvement, and the system comprises an intelligent nutrient optimization system, a microbiota dynamic adjustment system, a remote sensing and machine vision monitoring evaluation system, a self-adaptive multi-energy management system, and a block chain traceable soil management system. The comprehensive soil health assessment system and the intelligent optimization system are combined with various technical means such as remote sensing monitoring, microorganism regulation, intelligent fertilization and energy management, soil is subjected to refined management, soil health in the continuous cropping process of konjak is maintained for a long time, and the common continuous cropping obstacle problems such as soil nutrient depletion and disease accumulation are avoided. The high-yield stability in the konjak continuous cropping process is guaranteed, the continuous production capacity of soil is kept, various problems generated in soil in konjak continuous cropping planting are effectively solved, negative effects caused by continuous cropping can be effectively reduced through comprehensive prevention and dynamic management, and the overall yield and crop quality are improved.

Description

Technical Field

[0001] The invention relates to the technical field of soil improvement, in particular to a konjac continuous cropping obstacle prevention and soil improvement system based on multi-factor analysis. Background Art

[0002] According to a soil improvement method and dynamic monitoring system disclosed in China with publication number "CN117829357A", the method includes the following steps: S1, collecting soil property data and environmental data; S2 preprocessing the data to generate a data set suitable for analysis; S3, processing the data set using a machine learning model, the machine learning model using a random forest algorithm, the algorithm predicts the effects of soil improvement measures by inputting feature vectors and model parameters; S4, outputting soil improvement suggestions based on the prediction. The present invention provides farm managers with soil improvement suggestions based on predictions by collecting soil property data and environmental data and using machine learning models to predict the effects of soil improvement measures. By optimizing soil conditions and environmental factors, the method helps to improve the yield and quality of crops and provide support for sustainable agricultural development.

[0003] According to a method and system for analyzing and making decisions based on the approval of engineering construction projects disclosed in China with the publication number "CN118071169A", which relates to the field of Internet of Things technology, by obtaining information on the project land area and geographical location, the soil state of the construction land is analyzed, which helps to determine parameters such as soil permeability coefficient, heavy metal content and soil density, and provides basic data for subsequent soil state assessment, which is conducive to a better understanding of the soil conditions of the construction land, so that soil improvement and treatment measures can be reasonably planned to meet the needs of the project. By calculating the soil state assessment coefficient of the specified engineering construction project, the influence of factors such as soil permeability coefficient, heavy metal content and soil density on the construction project is comprehensively considered, which helps decision makers quickly understand the suitability of the soil and determine the importance and urgency of soil improvement and treatment work.

[0004] The above patent documents and prior art have the following technical problems when used:

[0005] Problem 1: In the prior art of the above patent technology, the obstacles to continuous cropping of konjac involve multiple factors such as soil nutrient depletion and disease accumulation. The simple random forest model cannot capture these complex interrelationships and dynamic changes. Therefore, its prediction effect is not accurate enough and cannot provide sufficiently accurate soil improvement suggestions for continuous cropping.

[0006] Question 2: The above documents and existing technologies only monitor the temperature, humidity and element content of soil, but fail to fully monitor and manage the microbial community, leading to the growth of harmful microorganisms. The lack of beneficial microorganisms will affect the biodiversity of the soil, which in turn affects the growth and yield of konjac.

[0007] Question 3: In the above documents and existing technologies, in large-scale konjac planting scenarios, traditional soil sampling and data collection methods are too time-consuming and costly, and it is difficult to achieve a rapid response. Therefore, relying solely on soil data collected by ground sensors cannot fully reflect the soil conditions;

[0008] Question 4: The soil improvement measures in the above-mentioned documents and existing technologies that are not combined with environmental conditions are too simple and difficult to adapt to complex climate change. There is also a lack of soil management records and traceability systems, which leads to opaque operations in the soil management process and an inability to accurately judge the historical status of soil health, which in turn affects long-term planting plans. Summary of the invention

[0009] Technical issues solved

[0010] In view of the deficiencies in the prior art, the present invention provides a konjac continuous cropping obstacle prevention and soil improvement system based on multi-factor analysis, which solves the following problems:

[0011] 1. The simple random forest model cannot capture these complex relationships and dynamic changes, so its prediction effect is not accurate enough;

[0012] 2. Failure to adequately monitor and manage microbial communities, leading to the growth of harmful microorganisms and affecting crop yields;

[0013] 3. In large-scale konjac planting scenarios, traditional soil sampling and data collection methods are too time-consuming and costly to achieve rapid response;

[0014] 4. Soil improvement measures that are not combined with environmental conditions are too simple and difficult to adapt to complex climate change and the lack of soil management records and traceability systems.

[0015] Technical Solution

[0016] To achieve the above purpose, the present invention is implemented by the following technical scheme: a konjac continuous cropping obstacle prevention and soil improvement system based on multi-factor analysis, the system includes a nutrient intelligent optimization system, a microbial community dynamic adjustment system, a remote sensing and machine vision monitoring and evaluation system, an adaptive multi-energy management system, and a blockchain traceable soil management system, wherein:

[0017] The remote sensing and machine vision monitoring and assessment system: uses drone and satellite remote sensing technology to analyze vegetation health through the NDVI vegetation index, and combines machine vision technology to monitor soil moisture, nutrient distribution, and signs of pests and diseases in real time, providing accurate soil health assessment;

[0018] The nutrient intelligent optimization system: receives data on the macroscopic soil health status from the remote sensing and machine vision monitoring and evaluation system, monitors the key nutrients of nitrogen, phosphorus and potassium in the soil through real-time sensors, and dynamically adjusts the soil nutrients by combining a deep learning algorithm;

[0019] The microbial community dynamic adjustment system: based on the soil evaluation data of the remote sensing and machine vision monitoring and evaluation system and the real-time monitoring data of the nutrient intelligent optimization system, analyzes the composition of the microbial community in the soil, adopts the Lotka-Volterra competition model to describe the interaction between harmful and beneficial microorganisms, and monitors the microbial community structure in the soil in real time;

[0020] The adaptive multi-energy management system integrates and utilizes a variety of renewable energy sources, combines the soil environment feedback from the remote sensing and machine vision monitoring and evaluation system, dynamically adjusts the soil environment, including temperature, humidity, light, irrigation, and performs functional and control adjustments on the soil environment;

[0021] The blockchain traceable soil management system uses blockchain technology to build a traceable system for soil management and crop production, and records each soil treatment and crop health monitoring on the blockchain for transparent management.

[0022] Preferably, in the remote sensing and machine vision monitoring and evaluation system, drones and satellite remote sensing are used to perform regional scanning to collect vegetation and soil information. In remote sensing image analysis, the normalized vegetation index NDVI is used to evaluate vegetation health, and the formula is as follows:

[0023]

[0024] in:

[0025] NIR is the reflectance value in the near-infrared band;

[0026] RED is the reflectance value of the red light band;

[0027] NDVI is a vegetation assessment index. The closer the NDVI is to 1, the healthier the vegetation.

[0028] For the calculation of soil moisture, the improved soil adjustment index MADI is used for evaluation, and the formula is as follows:

[0029]

[0030] in:

[0031] NIR is the reflectance value in the near-infrared band;

[0032] SWIR is the reflectance value in the short-wave infrared band, which is used to distinguish between wet soil and dry soil;

[0033] Soil_Moisture_Sensor is the soil moisture measurement value of the ground sensor;

[0034] α and β are weight coefficients used to adjust the ratio of remote sensing and ground data.

[0035] Preferably, in the nutrient intelligent optimization system, the soil health status is obtained from the remote sensing and machine vision monitoring and evaluation system, and the nitrogen, phosphorus and potassium in the soil are monitored in combination with real-time sensors. The optimization goal is set to maintain the balance of nutrients in the soil, and the nutrient optimization is performed through the Lagrangian optimization formula:

[0036] L(N, P, K, λ)

[0037] =U(N,P,K)-λ(N target -N)-λ(P target -P)-λ(K target -K)

[0038] in:

[0039] L(N, P, K, λ) is the Lagrangian function used to solve the nutrient optimization problem;

[0040] U(N, P, K) is the utility function of nutrient optimization, which represents the effective utilization rate of nitrogen, phosphorus and potassium in the soil;

[0041] λ is the Lagrangian coefficient, which is used to solve the nutrient optimization problem;

[0042] N, P, and K are the current contents of nitrogen, phosphorus, and potassium in the soil;

[0043] N target , P target , K target To indicate the target content of each nutrient.

[0044] Preferably, in the microbial community dynamic adjustment system, due to the dynamic evolution of the soil microbial community, the Lotka-Volterra competition model is used to describe the interaction between harmful and beneficial microorganisms, and the formula is as follows:

[0045]

[0046] in:

[0047] N1 is the number of beneficial microorganisms;

[0048] N2 is the number of harmful microorganisms;

[0049] r1 and r2 are the growth rates of beneficial and harmful microorganisms, indicating the reproduction speed of microorganisms in the current environment;

[0050] K1 and K2 are the environmental carrying capacities of beneficial and harmful microorganisms, respectively, indicating the upper limit of the number of microbial communities that the soil can support;

[0051] α and β are the competition intensity between different microbial groups, reflecting the inhibition and competition relationship between microbial groups.

[0052] Preferably, the adaptive multi-energy management system adjusts soil moisture, temperature, light, etc. based on soil assessment data and environmental information, and optimizes energy distribution through a linear programming model, and the formula is as follows:

[0053] And 0≤P i ≤P i，max

[0054] And 0≤P i ≤P i，max

[0055] in:

[0056] C is the total energy consumption of the system, indicating the cost of energy use;

[0057] P i is the output power of the i-th energy source;

[0058] c i is the unit energy consumption cost of each energy source;

[0059] E demand is the total energy demand of agricultural facilities;

[0060] P i , max is the upper limit of power for each energy source.

[0061] Preferably, the blockchain traceable soil management system records each soil management operation in the form of a transaction on the blockchain. In the blockchain, each soil management operation can be regarded as a transaction, and the formula is as follows:

[0062] T i =ID i ,Time i ,Operation i , Status i ,Data i

[0063] in:

[0064] T i The transaction information of the i-th soil management operation;

[0065] ID iIt is the unique identifier of the operation, ensuring that it cannot be tampered with in the blockchain;

[0066] Time i The timestamp of the operation records the execution time of the operation;

[0067] Operation i The specific type of operation, indicating whether this management is fertilization, irrigation or other operations;

[0068] Status i The status of the operation, including completed and uncompleted;

[0069] Data i Detailed data related to operations, including soil moisture and nutrient concentration.

[0070] Preferably, the operation process of the system is as follows:

[0071] Sp1: Data collection and multi-dimensional monitoring: The remote sensing and machine vision monitoring and evaluation system uses drones or satellites to perform remote sensing monitoring of soil health and crop growth, and combines machine vision technology to evaluate soil surface conditions, vegetation growth, and early signs of pests and diseases to obtain data A;

[0072] Sp2: Dynamic monitoring and optimization of soil nutrients. The nutrient intelligent optimization system relies on soil nutrient information provided by remote sensing and real-time sensors, dynamically adjusts fertilization strategies through optimization formulas, and obtains data B;

[0073] Sp3: Dynamic monitoring and adjustment of soil microbial communities: The microbial community dynamic adjustment system obtains soil macro-assessment data and real-time nutrient data, analyzes the composition of microbial communities in the soil, and obtains data C;

[0074] Sp4: Assessment of soil health status, the remote sensing and machine vision monitoring and assessment system generates a control plan based on the monitoring and analysis of data A, data B and data C using NDVI assessment of remote sensing image analysis;

[0075] Sp5: Multi-energy control and management of soil environment. The adaptive multi-energy management system allocates multiple renewable energy sources according to the control scheme obtained in Sp4 and the current energy demand and environmental status, drives each energy source to automatically adjust fertilization, microbial adjustment and facility control strategies according to the analysis results, and performs multi-energy control and management of soil environment.

[0076] Sp6: The blockchain traceability and management of the system. The blockchain traceable soil management system will record each operation such as fertilization, microbial adjustment, and environmental control on the blockchain. The historical data in the blockchain provides a basis for AI nutrient optimization, microbial dynamic adjustment, and facility management.

[0077] Preferably, the renewable energy in the adaptive multi-energy management system includes wind energy and solar energy, and wind energy and solar energy are generated through solar panels and wind turbines respectively.

[0078] Preferably, the soil collection in the nutrient intelligent optimization system includes soil sensors and weather stations, and the soil sensors include sensors for measuring nitrogen, phosphorus and potassium content, and the weather station provides information on ambient temperature, humidity and precipitation.

[0079] Beneficial Effects

[0080] The present invention provides a konjac continuous cropping obstacle prevention and soil improvement system based on multi-factor analysis, which has the following beneficial effects:

[0081] 1. The present invention adopts the remote sensing and machine vision monitoring and evaluation system in the system, including drone remote sensing, micro remote sensing technology and machine vision technology, and monitors the soil health status in real time through the NDVI vegetation index and the MADI soil moisture index, combined with high-precision soil sensors. It can accurately obtain data on soil moisture, nutrient distribution and early signs of pests and diseases, and realize timely discovery and intervention of soil degradation, excessive humidity or nutrient imbalance during continuous cropping. Konjac has high requirements for soil, and the system can accurately judge the health status of the soil and help prevent the yield decline caused by continuous cropping obstacles.

[0082] 2. The present invention adopts a deep learning algorithm and a Lagrangian optimization model in the system to monitor the key nutrients of nitrogen, phosphorus and potassium in the soil in real time through a soil sensor and a nutrient intelligent optimization system, and dynamically adjusts the fertilization scheme to obtain accurate data of soil nutrients and a reasonable nutrient supplement strategy, thereby achieving a balanced supply of nutrients in the soil, avoiding excessive fertilization or nutrient loss, maintaining the long-term productivity of the soil, and maintaining the stability of soil nutrients during the konjac planting process, avoiding the nutrient imbalance problem caused by continuous cropping obstacles.

[0083] 3. The present invention adopts a systematic utilization of the Lotka-Volterra competition model and a dynamic adjustment technology of a microbial community. By analyzing the dynamic balance of soil microorganisms, the growth of beneficial microorganisms is promoted, harmful microorganisms are inhibited, and a healthy microbial community structure is formed inside the soil, thereby reducing the breeding of harmful pathogens, achieving effective prevention of diseases during continuous cropping, and reducing outbreaks of diseases caused by the accumulation of pathogens in the soil. Since konjac is susceptible to continuous cropping obstacles and develops diseases, the accumulation of pathogens in the soil can be effectively prevented by optimizing the microbial structure.

[0084] 4. The present invention adopts adaptive multi-energy management system, renewable energy technologies such as solar energy and wind energy, and dynamically adjusts the temperature, humidity, illumination and irrigation in the soil environment through a linear programming optimization algorithm, so that the rational distribution of various energy sources is achieved, the energy consumption of soil management is reduced, the controllability of the soil environment is improved, energy is saved, and resource utilization is improved. Since continuous cropping of konjak requires stable environmental conditions, the system can automatically adjust according to demand, maintain the appropriate level of soil temperature and humidity, and promote the healthy growth of konjak.

[0085] 5. The present invention adopts blockchain technology and smart contract system to record each soil management operation on the blockchain, including the time, status and related data of operations such as fertilization and irrigation, so that the management and processing operations of the entire system are transparent and the soil management records cannot be tampered with, so that each soil management operation is traceable, ensuring the transparency and security of the soil improvement and planting management process. Since konjac planting is crucial to the long-term management of soil, blockchain technology provides a reliable traceability mechanism for soil management, helping farmers track soil health history and reduce human errors.

[0086] 6. The present invention adopts a comprehensive soil health assessment system and an intelligent optimization system, combined with remote sensing monitoring, microbial regulation, intelligent fertilization, energy management and other technical means, to carry out refined management of the soil, and long-term maintenance of soil health during the continuous cropping of konjac, avoiding common continuous cropping obstacles such as soil nutrient depletion and disease accumulation, ensuring the high-yield stability during the continuous cropping of konjac, maintaining the continuous production capacity of the soil, and effectively solving various problems caused by the soil in the continuous cropping of konjac. Through comprehensive prevention and dynamic management, the negative impact of continuous cropping can be effectively reduced, and the overall yield and crop quality can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 is a system structure diagram of the present invention;

[0088] Figure 2 It is a flow chart of the system operation of the present invention;

[0089] Figure 3 This is a comparison diagram of the NDVI experiment in the fourth specific embodiment of the present invention;

[0090] Figure 4 This is a comparison chart of the yield experiment in the specific embodiment 4 of the present invention;

[0091] Figure 5 This is a comparison diagram of energy consumption experiments in the fourth specific embodiment of the present invention;

[0092] Figure 6A visualization chart of NDVI and MADI of the remote sensing and machine vision monitoring and evaluation system of the present invention;

[0093] Figure 7 A visualization chart of nutrient optimization calculation of the nutrient intelligent optimization system of the present invention;

[0094] Figure 8 A visualization chart of the Lotka-Volterra model of the microbial community dynamic adjustment system of the present invention;

[0095] Fig. 9 A visualization diagram of energy distribution of the adaptive multi-energy management system of the present invention;

[0096] Fig.10 This is a visualization chart of transaction records of the blockchain traceable soil management system of the present invention. DETAILED DESCRIPTION

[0097] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Specific embodiment one:

[0099] like Figure 1-2 As shown in the figure, the konjac continuous cropping obstacle prevention and soil improvement system based on multi-factor analysis includes nutrient intelligent optimization system, microbial community dynamic adjustment system, remote sensing and machine vision monitoring and evaluation system, adaptive multi-energy management system, blockchain traceable soil management system, among which:

[0100] Remote sensing and machine vision monitoring and evaluation system: Using drone and satellite remote sensing technology, the NDVI vegetation index is used to analyze vegetation health. At the same time, machine vision technology is used to monitor soil moisture, nutrient distribution, and signs of pests and diseases in real time to provide accurate soil health assessment. In the remote sensing and machine vision monitoring and evaluation system, drones and satellite remote sensing are used for regional scanning to collect vegetation and soil information. In remote sensing image analysis, the normalized vegetation index NDVI is used to evaluate vegetation health. The formula is as follows:

[0101]

[0102] in:

[0103] NIR is the reflectance value in the near-infrared band;

[0104] RED is the reflectance value of the red light band;

[0105] NDVI is a vegetation assessment index. The closer the NDVI is to 1, the healthier the vegetation.

[0106] For the calculation of soil moisture, the improved soil adjustment index MADI is used for evaluation, and the formula is as follows:

[0107]

[0108] in:

[0109] NIR is the reflectance value in the near-infrared band;

[0110] SWIR is the reflectance value in the short-wave infrared band, which is used to distinguish between wet soil and dry soil;

[0111] Soil_Moisture_Sensor is the soil moisture measurement value of the ground sensor;

[0112] α and β are weight coefficients used to adjust the ratio of remote sensing and ground data;

[0113] Nutrient intelligent optimization system: Receive the macroscopic soil health status data from the remote sensing and machine vision monitoring and evaluation system, monitor the key nutrients of nitrogen, phosphorus and potassium in the soil through real-time sensors, and dynamically adjust the soil nutrients by combining deep learning algorithms. In the nutrient intelligent optimization system, the soil health status is obtained from the remote sensing and machine vision monitoring and evaluation system, and the nitrogen, phosphorus and potassium in the soil are monitored by real-time sensors. The optimization goal is to maintain the balance of nutrients in the soil, and the nutrients are optimized through the Lagrangian optimization formula:

[0114] L(N, P, K, λ)

[0115] =U(N,P,K)-λ(N target -N)-λ(P target -P)-λ(K target -K)

[0116] in:

[0117] L(N, P, K, λ) is the Lagrangian function used to solve the nutrient optimization problem;

[0118] U(N, P, K) is the utility function of nutrient optimization, which represents the effective utilization rate of nitrogen, phosphorus and potassium in the soil;

[0119] λ is the Lagrangian coefficient, which is used to solve the nutrient optimization problem;

[0120] N, P, and K are the current contents of nitrogen, phosphorus, and potassium in the soil;

[0121] N target , P target , K targetTo indicate the target content of each nutrient;

[0122] Microbial community dynamic adjustment system: Based on the soil evaluation data of the remote sensing and machine vision monitoring and evaluation system and the real-time monitoring data of the nutrient intelligent optimization system, the composition of the microbial community in the soil is analyzed, and the Lotka-Volterra competition model is used to describe the interaction between harmful and beneficial microorganisms. The microbial community structure in the soil is monitored in real time. In the microbial community dynamic adjustment system, due to the dynamic evolution of the soil microbial community, the Lotka-Volterra competition model is used to describe the interaction between harmful and beneficial microorganisms. The formula is as follows:

[0123]

[0124] in:

[0125] N1 is the number of beneficial microorganisms;

[0126] N2 is the number of harmful microorganisms;

[0127] r1 and r2 are the growth rates of beneficial and harmful microorganisms, indicating the reproduction speed of microorganisms in the current environment;

[0128] K1 and K2 are the environmental carrying capacities of beneficial and harmful microorganisms, respectively, indicating the upper limit of the number of microbial communities that the soil can support;

[0129] α and β are the competition intensity between different microbial groups, reflecting the inhibition and competition relationship between microbial groups;

[0130] Adaptive multi-energy management system: Integrate and utilize multiple renewable energy sources, combine the soil environment feedback from remote sensing and machine vision monitoring and evaluation systems, dynamically adjust the soil environment, including temperature, humidity, light, irrigation, and perform functional and control adjustments on the soil environment.

[0131] The adaptive multi-energy management system adjusts soil moisture, temperature, light, etc. based on soil assessment data and environmental information, and optimizes energy distribution through a linear programming model. The formula is as follows:

[0132] And 0≤P i ≤P i，max

[0133] And 0≤P i ≤P i，max

[0134] in:

[0135] C is the total energy consumption of the system, indicating the cost of energy use;

[0136] P i is the output power of the i-th energy source;

[0137] c i is the unit energy consumption cost of each energy source;

[0138] E demand Total energy demand for agricultural facilities;

[0139] P i , max is the power limit of each energy source;

[0140] Blockchain traceable soil management system: Use blockchain technology to build a traceable system for soil management and crop production, record each soil treatment and crop health monitoring on the blockchain, and manage transparently. The blockchain traceable soil management system records each soil management operation in the form of a transaction on the blockchain. In the blockchain, each soil management operation can be regarded as a transaction. The formula is as follows:

[0141] T i =ID i ,Time i ,Operation i , Status i ,Data i

[0142] in:

[0143] T i The transaction information of the i-th soil management operation;

[0144] ID i It is the unique identifier of the operation, ensuring that it cannot be tampered with in the blockchain;

[0145] Time i The timestamp of the operation records the execution time of the operation;

[0146] Operation i The specific type of operation, indicating whether this management is fertilization, irrigation or other operations;

[0147] Status i The status of the operation, including completed and uncompleted;

[0148] Data i Detailed data related to operations, including soil moisture and nutrient concentration.

[0149] The above system integrates advanced sensors, AI algorithms, the Internet of Things, blockchain and other technologies to achieve all-round monitoring and management of the soil during konjac cultivation. From preventing continuous cropping obstacles to dynamically optimizing soil health, each system works together to form an intelligent closed-loop management. Specific embodiment 2:

[0151] like Figure 1-2 As shown, according to the contents in the above specific embodiments, the following technical contents are further disclosed:

[0152] In the remote sensing and machine vision monitoring and evaluation system, equipment control is performed through drones, satellite remote sensing equipment, and ground sensors. The drones are equipped with multispectral or hyperspectral cameras for near infrared (NIR), red light (RED), and short wave infrared (SWIR) data collection. Satellite remote sensing equipment obtains images of macro areas from satellites for long-term soil and vegetation health monitoring. Ground sensors, such as soil moisture sensors, collect moisture data in real time and upload it to the data center. Using drone and satellite remote sensing technology, multispectral images of the field surface are taken regularly, and the collected images are uploaded to the central processing system. The NDVI formula is used to assess the health of vegetation. The soil moisture distribution is calculated by combining the improved soil regulation index MDVI with sensor data. The data from ground sensors is transmitted to the system through the Internet of Things (IoT) to provide accurate monitoring of humidity. Combined with machine vision technology, the leaves and stems of crops are photographed in real time. The images are analyzed through AI algorithms to detect early signs of diseases and insect pests, so as to timely detect and intervene in soil degradation, excessive humidity or nutrient imbalance during continuous cropping. Konjac has high requirements for soil. The system can accurately judge the health of the soil and help prevent yield declines caused by continuous cropping obstacles.

[0153] In the remote sensing and machine vision monitoring and evaluation system, the equipment used further includes the following:

[0154] Drone multispectral camera: used for multi-band data collection such as near infrared and red light;

[0155] Satellite image processing system: used for macro assessment;

[0156] IoT sensor network: monitors key parameters such as soil moisture and temperature;

[0157] AI visual algorithm: used for early identification of pests and diseases;

[0158] Cloud computing platform: centralized processing and storage of massive data.

[0159] The soil collection in the nutrient intelligent optimization system includes soil sensors and weather stations. The soil sensors include sensors for measuring nitrogen, phosphorus and potassium content. The weather station provides information on ambient temperature, humidity and precipitation. The nitrogen, phosphorus and potassium content are measured by soil sensors, and the data is uploaded to the intelligent analysis platform in real time. The overall health status of the soil is obtained from the remote sensing and machine vision monitoring system. The Lagrangian optimization algorithm is used to calculate the balance state of soil nutrients based on the N, P and K data collected by the sensors. The deep learning model predicts future nutrient needs based on historical data and current soil conditions, optimizes fertilization plans, connects to automated fertilization equipment, and accurately applies fertilizers according to the optimized nutrient needs.

[0160] The microbial community dynamic adjustment system uses soil sample collection equipment to regularly collect soil samples, analyzes microbial communities, uses high-throughput sequencers to analyze the genetic information of microorganisms in the soil, determines the structure of microbial communities, obtains soil physical and chemical state data from remote sensing and machine vision monitoring systems and nutrient optimization systems, uses the Lotka-Volterra competition model to describe the interactions between microorganisms, calculates the dynamic balance of beneficial and harmful microorganisms, and promotes the growth of beneficial microorganisms and inhibits harmful microorganisms by controlling soil nutrient supply, humidity, temperature and other parameters.

[0161] Renewable energy in the adaptive multi-energy management system includes wind energy and solar energy, and wind energy and solar energy are generated through solar panels and wind turbines respectively. Intelligent controllers are set to adjust soil moisture, temperature and irrigation conditions. The soil moisture, temperature and light conditions are monitored in real time through remote sensing and machine vision systems. The current energy consumption and environmental parameters are obtained from the sensor network. The linear programming algorithm is used to calculate the optimal allocation of multiple energy sources. The temperature control equipment and irrigation system are adjusted according to the real-time soil parameters to ensure the stability of the soil environment.

[0162] The blockchain traceable soil management system uses blockchain node devices to record soil management operations and data, and uses encryption modules to ensure the security of data during transmission and storage. Each operation on the soil, such as fertilization and irrigation, is recorded as a transaction, and operation data such as soil moisture and nutrient concentration are uploaded in real time through sensors. All operation records are stored in the blockchain network and can be viewed and audited by relevant parties. Specific embodiment three:

[0164] like Figure 1-2 As shown, according to the contents in the above specific embodiments, the following technical contents are further disclosed:

[0165] The system operation process is as follows:

[0166] Sp1: Data collection and multi-dimensional monitoring: Remote sensing and machine vision monitoring and evaluation systems use drones or satellites to conduct remote sensing monitoring of soil health and crop growth, and combine machine vision technology to evaluate soil surface conditions, vegetation growth, and early signs of pests and diseases to obtain data A.

[0167] Sp2: Dynamic monitoring and optimization of soil nutrients. The intelligent nutrient optimization system relies on soil nutrient information provided by remote sensing and real-time sensors. It dynamically adjusts the fertilization strategy through the optimization formula to obtain data B. According to the collected soil nutrient, humidity, temperature and other data, the artificial intelligence algorithm will analyze the current needs of crops and adjust the amount and frequency of nutrient application. Its core goal is to ensure the balance of nutrients in the soil and avoid excessive consumption or accumulation of nutrients, thereby reducing continuous cropping obstacles. The application of nutrients affects the activity of the microbial community. The system will adjust the fertilization strategy according to the feedback of microorganisms to avoid microbial ecological imbalance. The soil health status monitored by remote sensing and machine vision is used as the basis for AI to adjust the amount of fertilizer.

[0168] Sp3: Dynamic monitoring and adjustment of soil microbial communities: The microbial community dynamic adjustment system obtains soil macro-assessment data and real-time nutrient data, analyzes the composition of microbial communities in the soil, and obtains data C;

[0169] Sp4: Assessment of soil health status. The remote sensing and machine vision monitoring and assessment system generates a control plan based on the monitoring and analysis of data A, data B, and data C, using the NDVI assessment of remote sensing image analysis. The NDVI remote sensing indicator reflects the growth status of crops and determines whether there is poor growth due to soil problems. Machine vision is used to detect changes in soil surface structure, color, and moisture to predict possible soil degradation or nutrient loss. Remote sensing data will be provided to the AI ​​nutrient optimization system and microbial adjustment system as feedback information for adjusting nutrient application and microbial regulation strategies.

[0170] Sp5: Multi-energy control and management of soil environment. The adaptive multi-energy management system allocates multiple renewable energy sources according to the control scheme obtained in Sp4 and the current energy demand and environmental status, drives each energy source to automatically adjust fertilization, microbial adjustment and facility control strategies according to the analysis results, and performs multi-energy control and management of soil environment. It adjusts light, temperature, humidity and irrigation according to real-time data to provide suitable crop growth conditions. Environmental control will indirectly affect the microbial community in the soil. Appropriate temperature and humidity conditions will promote the reproduction of beneficial microorganisms and inhibit the expansion of harmful microorganisms. By adaptively adjusting energy consumption, it maximizes the use of natural resources and ensures efficient and low-carbon operation of agricultural equipment.

[0171] Sp6: Systematic blockchain traceability and management. The blockchain traceable soil management system will record each fertilization, microbial adjustment, environmental control and other operations on the blockchain. The historical data in the blockchain provides a basis for AI nutrient optimization, microbial dynamic adjustment and facility management. The system records all details about soil improvement, energy allocation, nutrient application, etc., which is convenient for later tracking of soil health changes and the effects of each operation.

[0172] By adopting a comprehensive soil health assessment system and intelligent optimization system, combined with remote sensing monitoring, microbial regulation, intelligent fertilization, energy management and other technical means, the soil is managed in a refined manner, and the long-term maintenance of soil health during the continuous cropping of konjac is achieved. Common continuous cropping obstacles such as soil nutrient depletion and disease accumulation are avoided, ensuring the high yield stability during the continuous cropping of konjac, maintaining the continuous production capacity of the soil, and effectively solving various soil problems in the continuous cropping of konjac. Through comprehensive prevention and dynamic management, the negative impact of continuous cropping can be effectively reduced, and the overall yield and crop quality can be improved. Specific embodiment four:

[0174] like Figure 1-5 As shown, according to the contents in the above specific embodiments, the following technical contents are further disclosed:

[0175] According to the content of the above-described embodiment, the improvement effect of the system on the soil during konjak continuous cropping is verified, and the application effect of the core technology of this system model in konjak continuous cropping is verified. Hereinafter, this system is referred to as the new system, and the experimental data are as follows:

[0176] 1. Experimental purpose: To verify the effectiveness of the new system in preventing continuous cropping obstacles and improving soil, and to compare its differences with the existing traditional improvement system;

[0177] 2. Experimental comparison system:

[0178] New systems: including remote sensing and machine vision monitoring and evaluation system, nutrient intelligent optimization system, microbial community dynamic adjustment system, adaptive multi-energy management system and blockchain traceable soil management system;

[0179] Traditional system: Based on conventional soil management and monitoring technologies, it mainly includes basic soil testing and improvement methods, such as fertilization, irrigation, and physical remediation technologies, without the intervention of IoT and machine learning optimization algorithms;

[0180] 3. Experimental site: Select farmland that has been continuously planted with konjac, because long-term planting may lead to the risk of continuous cropping obstacles, and the experimental effect is more significant;

[0181] 4. Experimental samples: The experimental fields were divided into two groups, managed using the new system and the traditional system respectively. The soil properties of each group of fields, such as nutrient content, pH value, humidity, etc., were basically the same and randomly assigned to avoid human interference;

[0182] 5. Experimental steps:

[0183] Data collection: Before starting the experiment, use drones, satellite remote sensing technology and ground sensors to collect initial soil data from the experimental fields, including information such as moisture, nutrient distribution, microbial community structure, and signs of pests and diseases;

[0184] Conventional system group: restoration and improvement according to conventional soil management measures, including regular fertilization and irrigation, without further monitoring and dynamic adjustments;

[0185] New system group: Use the new system to conduct comprehensive monitoring and optimization of soil, with the following specific steps:

[0186] Sp1: Remote sensing and machine vision monitoring: collect soil moisture, nutrient distribution, vegetation health and pest information every week, and analyze soil health status;

[0187] Sp2: Intelligent nutrient optimization: Combined with deep learning algorithms, key nutrients such as nitrogen, phosphorus, and potassium in the soil are adjusted in real time based on monitoring data;

[0188] Sp3: Dynamic adjustment of microbial communities: real-time monitoring of beneficial and harmful microorganisms in the soil, and dynamic balance of microbial communities through the Lotka-Volterra model;

[0189] Sp4: Adaptive multi-energy management: Optimize irrigation and environmental conditions such as soil moisture and temperature based on monitoring data;

[0190] Sp5: Blockchain management: record each soil management operation and results.

[0191] Experimental period: The entire experimental period was set to 2 growing seasons, 18 months, and the soil health status, konjac growth, yield and disease status were recorded in each period;

[0192] 6. Experimental results and analysis:

[0193] 1) Soil health

[0194] New system group: Remote sensing monitoring showed that the NDVI value remained between 0.8 and 0.9 throughout the growing season, indicating that the vegetation was in good health. The MADI index combined with ground sensor data showed that soil moisture remained within the optimal range of 20%-25%, nutrients were evenly distributed, the microbial community structure tended to be stable, and soil health was significantly improved;

[0195] Traditional system group: NDVI values ​​fluctuate greatly, between 0.6 and 0.7, indicating that vegetation in some areas is in poor health, soil moisture is occasionally too high or too low, fertilization and irrigation lack precise regulation, and microbial communities are prone to adverse changes;

[0196] 2) Nutrient utilization rate

[0197] New system group: Through Lagrangian optimization, the utilization rate of nitrogen, phosphorus and potassium in the soil is significantly improved, the nutrients are balanced, and unnecessary fertilization costs are reduced;

[0198] Traditional system group: Due to the lack of intelligent optimization, nutrients in the soil are often insufficient or excessive, resulting in low nutrient utilization efficiency;

[0199] 3) Microbial community structure

[0200] New system group: Real-time monitoring and dynamic adjustment effectively inhibit the over-reproduction of harmful microorganisms, promote the growth of beneficial microbial communities, and make the soil ecosystem more stable;

[0201] Traditional system group: The microbial community structure is greatly affected by seasonal and environmental changes, and harmful microorganisms often proliferate;

[0202] 4) Konjac production

[0203] New system group: The average konjac yield per hectare increased by 15%-20%, pests and diseases were reduced, and the crop quality was also improved;

[0204] Traditional system group: Konjac yield fluctuates greatly, is easily affected by pests and diseases, and has a lower overall yield than the new system;

[0205] 5) Disease control

[0206] New System Group: Use machine vision and remote sensing technology to identify early signs of pests and diseases, take targeted measures in a timely manner, and effectively prevent the large-scale spread of diseases;

[0207] Traditional system group: Pest and disease monitoring is delayed, and treatment is often carried out only when the disease appears, resulting in crop damage in some areas;

[0208] 6) Resource efficiency

[0209] New system group: The adaptive multi-energy management system optimizes the use of water, fertilizer, and energy, reduces resource waste, and optimizes energy distribution through a linear programming model, reducing overall energy consumption costs by about 10%-15%;

[0210] Traditional system group: The use of resources is not optimized, and there is often over-irrigation or under-irrigation, resulting in waste of resources and increased energy consumption.

[0211] The experimental results are as follows Figure 3-5 As shown:

[0212] against Figure 3 , conduct NDVI comparison, that is, prepare health index comparison. As can be seen from the chart, the NDVI value of the traditional system is between 0.64 and 0.70, showing a relatively stable but low vegetation health level. The NDVI value of the new system is significantly higher, maintained between 0.85 and 0.90, indicating that the vegetation health has improved significantly;

[0213] against Figure 4 , and compared the yield per hectare. It can be seen from the chart that the yield of the traditional system fluctuates between 3300 and 3600 kg / hectare, and the yield under the new system has increased significantly, stabilizing between 4000 and 4250 kg / hectare, indicating that the new improved system helps to increase the yield of konjac cultivation;

[0214] against Figure 5 , energy consumption is compared in kWh. It can be seen from the chart that the energy consumption of the traditional system fluctuates between 195 and 215 kWh, and the energy consumption of the new system is significantly reduced, maintained between 170 and 190 kWh, showing higher energy utilization efficiency.

[0215] 7. Summary and analysis:

[0216] Through comparative experiments, it can be concluded that the konjac continuous cropping obstacle prevention and soil improvement system based on multi-factor analysis has obvious technical advantages in the following aspects:

[0217] 1) Intelligence and automation: The new system uses drones, sensors, deep learning and other technologies to achieve real-time and accurate soil monitoring and management, greatly reducing manual intervention and improving efficiency;

[0218] 2) Nutrient optimization: Through the nutrient intelligent optimization system, the new system can maintain the dynamic balance of soil nutrients, significantly improve nutrient utilization, and reduce fertilizer waste and environmental pollution;

[0219] 3) Microbial balance: The dynamic adjustment system of the microbiome effectively regulates the competition between beneficial and harmful microorganisms, maintains the health of the soil ecosystem, and reduces the occurrence of diseases;

[0220] 4) Energy consumption and resource efficiency: The adaptive multi-energy management system optimizes the use of energy and water resources and reduces operating costs by intelligently controlling temperature, humidity and irrigation;

[0221] 5) Early warning of pests and diseases: Machine vision technology can detect signs of pests and diseases in a timely manner. Combined with the traceability function of blockchain, the new system improves the transparency and traceability of pest and disease management;

[0222] 6) Sustainability: The new system integrates a number of advanced technologies to provide a sustainable soil management solution for continuous konjac cultivation, extending the service life of the soil and increasing the sustainability of cultivation.

[0223] These technical features and experimental results fully demonstrate that the new system has effectively improved the vegetation health index, increased crop yields, and significantly reduced energy consumption through the integration of multiple technologies, remote sensing, AI optimization, and microbial adjustment. This shows that the new system has significant advantages in preventing konjac continuous cropping obstacles and improving soil, especially in terms of environmental sustainability and production efficiency. The new system has significant advantages in preventing konjac continuous cropping obstacles and improving soil health, and can provide strong support for the sustainable development of konjac cultivation. Specific embodiment five:

[0225] like Figure 1-10 As shown, according to the contents in the above specific embodiments, the following technical contents are further disclosed:

[0226] like Figure 6 As shown, the NDVI and MADI of the remote sensing and machine vision monitoring and evaluation system are simulated and calculated. The health status of vegetation is calculated through the reflectance values ​​of the near-infrared band and the red light band. The closer the NDVI value is to 1, the healthier the vegetation is. The soil moisture condition is comprehensively evaluated by combining the humidity data of the near-infrared band, short-wave infrared band and ground sensors. This indicator is used to more accurately reflect the moisture level of the soil.

[0227] like Figure 7 As shown in the figure, the nutrient optimization calculation of the nutrient intelligent optimization system is visualized. Through Lagrangian optimization, the nitrogen, phosphorus and potassium in the soil are optimized. According to the difference between the current nutrient content and the target content, it is determined whether the nutrient distribution needs to be adjusted to maintain the balance of soil nutrients.

[0228] like Figure 8As shown, the Lotka-Volterra model of the dynamic adjustment system of the microbial community is visualized. The Lotka-Volterra model is used to describe the competitive relationship between beneficial microorganisms and harmful microorganisms. Through dynamic simulation, the changing trend of the microbial community can be understood and corresponding adjustments can be made.

[0229] like Fig. 9 As shown, the energy distribution optimization of the adaptive multi-energy management system is visualized. A linear programming model is used to optimize the distribution of multiple renewable energy sources to ensure that agricultural facilities achieve the minimum energy consumption cost while meeting energy needs.

[0230] like Fig.10 As shown, the transaction record simulation of the blockchain traceable soil management system is visualized. By simulating the transaction structure of the blockchain, each soil management operation is recorded as a transaction, including timestamp, operation type and operation data, so that transparent tracking of soil management can be achieved.

[0231] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0232] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A konjac continuous cropping obstacle prevention and soil improvement system based on multi-factor analysis, characterized in that: The system includes a nutrient intelligent optimization system, a microbial community dynamic adjustment system, a remote sensing and machine vision monitoring and evaluation system, an adaptive multi-energy management system, and a blockchain traceable soil management system, among which: The remote sensing and machine vision monitoring and assessment system: uses drone and satellite remote sensing technology to analyze vegetation health through the NDVI vegetation index, and combines machine vision technology to monitor soil moisture, nutrient distribution, and signs of pests and diseases in real time, providing accurate soil health assessment; The nutrient intelligent optimization system: receives data on the macroscopic soil health status from the remote sensing and machine vision monitoring and evaluation system, monitors the key nutrients of nitrogen, phosphorus and potassium in the soil through real-time sensors, and dynamically adjusts the soil nutrients by combining a deep learning algorithm; The microbial community dynamic adjustment system: based on the soil evaluation data of the remote sensing and machine vision monitoring and evaluation system and the real-time monitoring data of the nutrient intelligent optimization system, analyzes the composition of the microbial community in the soil, adopts the Lotka-Volterra competition model to describe the interaction between harmful and beneficial microorganisms, and monitors the microbial community structure in the soil in real time; The adaptive multi-energy management system integrates and utilizes a variety of renewable energy sources, combines the soil environment feedback from the remote sensing and machine vision monitoring and evaluation system, dynamically adjusts the soil environment, including temperature, humidity, light, irrigation, and performs functional and control adjustments on the soil environment; The blockchain traceable soil management system uses blockchain technology to build a traceable system for soil management and crop production, and records each soil treatment and crop health monitoring on the blockchain for transparent management.

2. The konjac continuous cropping obstacle prevention and soil improvement system based on multifactor analysis according to claim 1, characterized in that: In the remote sensing and machine vision monitoring and evaluation system, drones and satellite remote sensing are used to perform regional scanning to collect vegetation and soil information. In remote sensing image analysis, the Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health. The formula is as follows: in: NIR is the reflectance value in the near-infrared band; RED is the reflectance value of the red light band; NDVI is a vegetation assessment index. The closer the NDVI is to 1, the healthier the vegetation. For the calculation of soil moisture, the improved soil adjustment index MADI is used for evaluation, and the formula is as follows: in: NIR is the reflectance value in the near-infrared band; SWIR is the reflectance value in the short-wave infrared band, which is used to distinguish between wet soil and dry soil; Soil_Moisture_Sensor is the soil moisture measurement value of the ground sensor; α and β are weight coefficients used to adjust the ratio of remote sensing and ground data.

3. The konjac continuous cropping obstacle prevention and soil improvement system based on multifactor analysis according to claim 1, characterized in that: In the nutrient intelligent optimization system, the soil health status is obtained from the remote sensing and machine vision monitoring and evaluation system, and the nitrogen, phosphorus and potassium in the soil are monitored in combination with real-time sensors. The optimization goal is set to maintain the balance of nutrients in the soil, and the nutrient optimization is performed using the Lagrangian optimization formula: L(N,P,K,λ)=U(N,P,K)-λ(N target -N)-λ(P target -P)-λ(K target -K) in: L(N, P, K, λ) is the Lagrangian function used to solve the nutrient optimization problem; U(N, P, K) is the utility function of nutrient optimization, which represents the effective utilization rate of nitrogen, phosphorus and potassium in the soil; λ is the Lagrangian coefficient, which is used to solve the nutrient optimization problem; N, P, and K are the current contents of nitrogen, phosphorus, and potassium in the soil; N target , P target , K target To indicate the target content of each nutrient.

4. The konjac continuous cropping obstacle prevention and soil improvement system based on multifactor analysis according to claim 1, characterized in that: In the microbial community dynamic adjustment system, due to the dynamic evolution of soil microbial communities, the Lotka-Volterra competition model is used to describe the interaction between harmful and beneficial microorganisms. The formula is as follows: in: N1 is the number of beneficial microorganisms; N2 is the number of harmful microorganisms; r1 and r2 are the growth rates of beneficial and harmful microorganisms, indicating the reproduction speed of microorganisms in the current environment; K1 and K2 are the environmental carrying capacities of beneficial and harmful microorganisms, respectively, indicating the upper limit of the number of microbial communities that the soil can support; α and β are the competition intensity between different microbial groups, reflecting the inhibition and competition relationship between microbial groups.

5. The konjac continuous cropping obstacle prevention and soil improvement system based on multifactor analysis according to claim 1, characterized in that: The adaptive multi-energy management system adjusts soil moisture, temperature, light, etc. based on soil assessment data and environmental information, and optimizes energy distribution through a linear programming model. The formula is as follows: And 0≤P i ≤P i，max And 0≤P i ≤P i，max in: C is the total energy consumption of the system, indicating the cost of energy use; P i is the output power of the i-th energy source; c i is the unit energy consumption cost of each energy source; E demand is the total energy demand of agricultural facilities; P i，max is the upper limit of power for each energy source.

6. The konjac continuous cropping obstacle prevention and soil improvement system based on multifactor analysis according to claim 1, characterized in that: The blockchain traceable soil management system records each soil management operation in the form of a transaction on the blockchain. In the blockchain, each soil management operation can be regarded as a transaction, and the formula is as follows: T i =ID i ,Time i ,Operation i ,Status i ,Data i in: T i The transaction information of the i-th soil management operation; ID i It is the unique identifier of the operation, ensuring that it cannot be tampered with in the blockchain; Time i The timestamp of the operation records the execution time of the operation; Operation i The specific type of operation, indicating whether this management is fertilization, irrigation or other operations; Status i The status of the operation, including completed and uncompleted; Data i Detailed data related to operations, including soil moisture and nutrient concentration.

7. The konjac continuous cropping obstacle prevention and soil improvement system based on multifactor analysis according to claim 1, characterized in that: The operation process of the system is as follows: Sp1: Data collection and multi-dimensional monitoring: The remote sensing and machine vision monitoring and evaluation system uses drones or satellites to perform remote sensing monitoring of soil health and crop growth, and combines machine vision technology to evaluate soil surface conditions, vegetation growth, and early signs of pests and diseases to obtain data A; Sp2: Dynamic monitoring and optimization of soil nutrients. The nutrient intelligent optimization system relies on soil nutrient information provided by remote sensing and real-time sensors, dynamically adjusts fertilization strategies through optimization formulas, and obtains data B; Sp3: Dynamic monitoring and adjustment of soil microbial communities: The microbial community dynamic adjustment system obtains soil macro-assessment data and real-time nutrient data, analyzes the composition of microbial communities in the soil, and obtains data C; Sp4: Assessment of soil health status, the remote sensing and machine vision monitoring and assessment system generates a control plan based on the monitoring and analysis of data A, data B and data C using NDVI assessment of remote sensing image analysis; Sp5: Multi-energy control and management of soil environment. The adaptive multi-energy management system allocates multiple renewable energy sources according to the control scheme obtained in Sp4 and the current energy demand and environmental status, drives each energy source to automatically adjust fertilization, microbial adjustment and facility control strategies according to the analysis results, and performs multi-energy control and management of soil environment. Sp6: The blockchain traceability and management of the system. The blockchain traceable soil management system will record each operation such as fertilization, microbial adjustment, and environmental control on the blockchain. The historical data in the blockchain provides a basis for AI nutrient optimization, microbial dynamic adjustment, and facility management.

8. The konjac continuous cropping obstacle prevention and soil improvement system based on multifactor analysis according to claim 1, characterized in that: The renewable energy in the adaptive multi-energy management system includes wind energy and solar energy, and wind energy and solar energy are generated through solar panels and wind turbines respectively.

9. The konjac continuous cropping obstacle prevention and soil improvement system based on multifactor analysis according to claim 1, characterized in that: The soil collection in the nutrient intelligent optimization system includes soil sensors and weather stations, and the soil sensors include sensors for measuring nitrogen, phosphorus and potassium content, and the weather station provides information on ambient temperature, humidity and precipitation.

Images