# Sensor Vegetable Greenhouse and Agricultural Product Supply Chain Management Based on Improved Neural Network

Authors: Dandan Dou
Journal: Mobile Information Systems
 (2022)

---

## Abstract

In recent years, people have begun to collect environmental data in vegetable greenhouses. Therefore, this article studies the current internal control technology of vegetable greenhouses, and improves the sensor application in vegetable greenhouses, in a targeted manner by combining with the improved neural network algorithm. The model has more accurate prediction accuracy than traditional BP. By using the Android client and ZigBee artificial intelligence control technology it creates the most suitable living conditions for the vegetables, fruits, and other crops in the vegetable greenhouse by controlling the various valves, sun visors, and light supplementary switches inside the vegetable greenhouse. The simulation results show that the model proposed in this article has higher convergence accuracy. In addition, this article applies the improved neural network to the management of the agricultural product supply chain, starting from the agricultural product supply chain and agricultural product supply chain management. Based on this, the analytic hierarchy process is used to explore the influencing factors of agricultural product supply chain management, and the analysis shows that consumers are targeting different consumer demand raised by the agricultural product supply chain. The improved neural network provides new ideas for the research of sensor vegetable greenhouses and agricultural product supply chain management.


---

## Body

## 1. Introduction

With the continuous introduction of various high-tech science and technology into the vegetable greenhouses in today’s society, the intelligent operation and management of vegetable greenhouses has been certified in many aspects [1]. Since the vegetable greenhouse is a closed environment constructed manually, it also makes it more convenient for managers to carry out arbitrary network construction and node maintenance and protection. Therefore, the actual application of the Internet to vegetable greenhouses makes them intelligent, which can improve agricultural production more efficiently, benefit farmers, and bring them more income [2]. With economic development, there are more and more vegetable greenhouses, and the area of the unit greenhouses is also getting larger. If we continue to use manual to manage the environmental changes in the greenhouses and still use cable wiring, there is no way to meet the needs of technicians for vegetable greenhouses, and high-speed and efficient work [3]. With the continuous development and application of remote intelligent system control technology and sensor networks, it provides practical technical support for intelligently managed vegetable greenhouses [4]. However, the existing sensor vegetable greenhouses still have some problems in the actual construction process, so this article will use the improved neural network to provide new ideas for the research of sensors in greenhouse vegetables [5]. The staff in charge of vegetable greenhouses can use the improved neural network sensor nodes to collect data, intuitively understand the environmental factors that contribute to the growth of crops in the greenhouse, and can also perform remote operations to artificially change the environment in the vegetable greenhouse [6].Agricultural products are an indispensable thing in people’s daily lives and occupy a high position in people’s lives; this also makes people have more stringent requirements for the quality, type, and nutritional content of agricultural products, especially for the quality and safety of agricultural products [7]. However, due to the current small production situation of agricultural products, it has brought huge difficulties to the production, processing, and sales of agricultural products in the face of huge market demand [8]. Supply chain management utilizes the coordination of supply, production, sales, and other links, which can bring high-speed, effective, and cost-reducing capabilities to enterprises [9]. Nowadays, Chinese enterprises are still in the period of exploration in the aspect of supply chain management [10]. How to use supply chain management to solve the problems that arise in each link of the agricultural product supply chain is particularly important for dealing with the problems of agriculture, rural areas, and farmers [11]. This article applies the improved neural network to the management of agricultural product supply chain and provides a reference for the management of China’s agricultural product supply chain [12].


## 2. Related Work

A common neural network algorithm, back-propagation neural network algorithm, and heuristic algorithm optimization ideas for analysis were proposed. The seagull optimization algorithm is used to optimize the BP neural network model, and the performance of the proposed model is verified for specific water quality parameter prediction scenarios. Aiming at the shortcomings of the seagull optimization algorithm as an optimization aspect and then using the benchmark function, the performance test of the optimized algorithm is launched [13]. Using the new algorithm to optimize the neural network, we can get the model of the improved seagull optimization algorithm and the improved BP neural network system. After research and analysis, it can be seen that the optimized algorithm has greatly improved the prediction accuracy and can be better applied to actual problem-solving [14, 15]. The ACARS system cannot complete the relevant system model due to the data collected by it, but the data collected by the fast access recorder can make up for its shortcomings. Therefore, the model is built on the basis of intelligent algorithms [16]. During the process, this article can use the ACARS system and fast access recorder data to construct a sample. Due to the complexity of the engine system, a variety of diagnostic methods are needed to solve the engine system failure. For diagnostic decision-making, after summarizing the currently known methods, the final decision output is determined. Its advantage is that it can consider the problem more comprehensively than when only one method is used to diagnose the decision. The literature describes in detail an algorithm that can perform weighted fusion estimation of self-adapting environment. The core of this algorithm is based on the relevant characteristic attributes of the engine’s own parameters, and by fusing the relevant characteristic attributes of existing parameters [17, 18]. After summarizing the currently known diagnostic knowledge methods, finally we determine what kind of decision-making fusion strategy method can be used and propose the multi-attribute characteristics of related diagnostic knowledge to integrate decision-making. Based on this method, the extension of decision knowledge is realized. People can build the engine fault diagnosis model that people need by using the strategy of intelligent systems so that they can break through some of the bottlenecks encountered in the traditional mathematical model and solve the problem [19]. An optimized artificial neural network system, which uses the ant colony algorithm to improve the initial weight vector of the algorithm, avoids a series of problems caused by people’s subjective random selection of weights, and then uses its unique related characteristics to replace the related problems of BP algorithm gradient. By controlling the complexity of the training algorithm, it can improve the related convergence speed [20].


## 3. Sensor Vegetable Greenhouse Based on Improved Neural Network

### 3.1. Improve the Neural Network

Neural network algorithms have also been widely used in various specific scenarios. They establish a hierarchical model, and then continuously adjust the connection weights between each level according to the existing samples during the training process so that the final results are closer to reality. This article summarizes the characteristics of several common data fusion algorithms and their scope of application as shown in Table1.Table 1
Comparison of data fusion algorithms.
Fusion methodUncertaintyFusion technologyScope of applicationWeighted average method—Weighted averageConvergence of low-level dataBayesian estimationGaussian noiseBayesian estimationConvergence of high-level dataD-S evidence theory—Logical reasoningConvergence of high-level dataFuzzy logic reasoningMembershipLogical reasoningConvergence of high-level dataNeural networksLearning errorNeural networksConvergence of low-/high-level dataIt can be seen from Table1 that the neural network algorithm can not only be applied to low-level data fusion but also can be used for data fusion at a higher level. Although its structure is more complicated than general algorithms and has a higher computational load, it is used in practical applications. In general, it can usually exert better performance than general algorithms. There are many types of neural networks. Among them, the BP neural network, or back-propagation neural network, is the most widely used. It has a simple structure and can learn, organize, and adapt independently. It includes an input layer, a hidden layer, and an output layer. The hidden layer can include multiple layers, and it is widely used in prediction and other fields. The BP neural network belongs to a multi-layer feedforward network, which mainly includes forward-propagating signals and back-propagation errors. In the learning process, samples are passed through the input layer to the hidden layer, and the neurons in the hidden layer; the arithmetic processing is then passed to the output layer, and the actual data passed from the output layer are compared with the predicted data to obtain the error, and the error is propagated back. In the error propagation process, it is based on the gradient descent strategy to continuously adjust the connection weights between neurons in each layer until the final predicted value is the smallest compared to the actual value. The deviation of the hth neuron in the hidden layer is θh. Then its input formula is(1)Ah=∑i=1dVihXi.The hidden layer transfer function is generally sigmoid function, namely:(2)fx=11+e−x.The output formula of the hth neuron in the hidden layer is(3)Bh=fAh−θh.The input formula of the hth neuron in the hidden layer and the jth neuron in the output layer is(4)Cj=∑h=1gWhjBh.In this article, the transfer function of the output layer also selects the sigmoid function, and the output formula of the jth neuron in the output layer can be obtained as(5)Yj=fCj−θj.If the actual value of the jth neuron in the output layer is set toRj, then the error of the jth neuron is Ej, namely:(6)Ej=12Rj−Yj.The total error of network output is(7)E=12±∑j=1lRj−Yj2.The BP neural network needs to use the gradient descent method to obtain the optimal output and expand the error to the hidden layer. The formula is(8)E=12∑j=1lRj−fCj−θj2.The formula for the change in connection weights of the hth neuron in the hidden layer and the jth neuron in the output layer is(9)ΔWhj=−η∂E∂Whj.According to the aforementioned formula, the formula is(10)∂Cj∂Whj=Bh.According to the nature of the S function,(11)f′x=fx.1−fx.Then there is(12)gj=∂E∂Yj.∂Yj∂Cj=−Yj−Rj.Yj.1−Yj.Then the weight change amount is(13)ΔWhj=ηgjBh.The weight adjustment formula is(14)Whjn+1=Whjn+Whj.At the same time, the amount of change in the threshold can be expressed by a formula:(15)Δθj=−η∂E∂θj=−ηgj.The threshold adjustment formula is(16)θjn+1=θjn+Δθj.The weight adjustment formula is(17)Vihn+1=Vihn+ΔVih.The threshold change is(18)Δθh=−ηeh.The threshold is adjusted to(19)θhn+1=θhn+Δθh.Regarding many existing problems of neural networks, many researchers have proposed optimization methods. Among them, artificial intelligence-based heuristic algorithms are combined with them to improve the effect of neural network algorithms. This article mainly introduces the three most typical algorithms among which are the improved methods based on PSO, GA, and ACO.PSO is a natural heuristic algorithm. It is a process of randomly initializing the solution and searching for the best solution through an iterative method. The obtained solution is judged by the fitness function to determine whether it is optimal. The PS0 algorithm is often used in the BP neural network because it can well solve the long time-consuming training of the BP neural network, causing the defect of local minimums. The speed and position update formulas are(20)Vidk+1=ωVidk+c1r1pidk−Xidk+c2r2pgdk−Xidk,Xidk+1=Xidk+rVidk+1.GA is an algorithm that imitates the theory of biological evolution and finds the best solution by simulating the natural evolution process. GA sorts and combines the optimized parameters required using the BP neural network to form a chromosome. Multiple chromosomes form a population. The excellent genes in the population will be passed on to the next generation. New individuals can also be synthesized through crossover, mutation, and other methods. This is expressed as a formula:(21)Pc=Pcmaxf′<favgPcmax−Pcmin1+exp2Af′−favg/fmax−favgf′≥favg,Pm=Pmmaxf<favgPmmax−Pmmin1+exp2Af−favg/fmax−favg−1+Pmminf′≥favg.ACO is an algorithm to find the optimal solution by imitating the ant foraging form, and it can also be used to find the global optimal path. It is expressed as a formula:(22)τijt+1=1−ρ.τijt+.τij.The increment of interest-bearing element is calculated as(23)Δτij=∑k=1KΔτijk.The BP neural network in this article uses a three-layer structure. The prediction of ammonia nitrogen is of great significance to the accurate grasp of water quality. The following are input parameters of this article: water temperature, pH, dissolved oxygen, conductivity, turbidity, and permanganate index; the number of input layer nodes is 6, and the output selection is ammonia nitrogen (Cmg/L); the number of output layer nodes is 1, the number of hidden layer nodes is 6, the number of iterations is 100, and the training function is trainlm; the population of seagulls is 100, and the number of iterations is 100. Figure1 shows the convergence comparison of the two model algorithms.Figure 1
Comparison of SOA-BP and BP convergence.It can be observed from Figure1 that the SOA-BP model converges faster than the BP model. In terms of convergence accuracy, the same is iterated 100 times, the convergence value of the BP model is 4.536 e−7, and the convergence value of the proposed SOA-BP model is 2.029 e−8; the convergence accuracy of the proposed model is higher. The simulation of 23 sets of verification data is shown in Figure 2.Figure 2
Comparison of SOA-BP and BP predicted and actual values.


### 3.2. Sensor Vegetable Greenhouse

In today’s developed network environment, the wireless sensor network system has also become an emerging collection, transmission, and processing technology. According to the actual requirements of the collected environmental information in the vegetable greenhouse, this article studies the framework of the wireless sensor network and adjusts the important environmental factors that affect the vegetable greenhouse according to the collected data. Assuming that the technician who manages the vegetable greenhouse finds that the collected data and the parameter values that affect the environment set in advance have disagreements, then the ZigBee intelligent control technology can be used to implement remote operation of the on-off valves set in the vegetable greenhouse, human intervention, and control of the environment in the vegetable greenhouse. The schematic diagram of the experimental framework designed and studied in this article is shown in Figure3.Figure 3
System overall architecture diagram.The ZDO in the ZigB ee protocol stack is mainly responsible for the management plan of all sensor nodes. The application layer can use the ZDO service to obtain all the network information that it wants. In addition, ZDO provides binding services. The working mechanism of the protocol is shown in Table2.Table 2
Protocol system and protocol code.
Layered protocol stack systemProtocol code filePhysical layer (PHY)Hardware layer (HAL)Control sublayer (MAC)Link layer (Zmac)Network layer (NWK)Network layer (NWK)Application support layer (APS)Network layer (NWK)Program framework (AF)Profile and programZigbee device object ZDODevice object directory ZDOIn this article, the SHT 10 sensors used in the vegetable greenhouse network are all calibrated in advance so that they can be directly linked with the CC2530 and used. The electrical characteristics of SHT 10 are shown in Table3.Table 3
SHT10 electrical characteristics.
ParameterConditionMin.Typ.Max.UnitPower supply DC—2.365.5VSupply currentMeasuring540—μAAverage227—μADormant—0.40.6μALow-level output voltagelCL < 4 mA0—260mVHigh-level output voltageRp < 25 kΩ90%—100%VddLow-level input voltageFalling edge0—30%VddHigh-level input voltageRising edge80%—100%VddInput current on the pad———1μAThe functional characteristics are shown in Table4.Table 4
SHT 10 functional characteristics.
HumidityTemperatureParameterNumerical valueParameterNumerical valueMeasuring range10∼97% RHMeasuring range−40∼110°CResolution0.03% RHResolution0.02°CRepeatability0.02% RHRepeatability0.4°CMeasurement accuracy−3.8% RH∼3.9% RHMeasurement accuracy−0.4°C∼0.5°CHysteresis−0.1% RH∼0.1% RHDriftLess than 0.4°CThe TSL2561 hardware in this article has a particularly precise and simple structure so that the clock and serial data lines can be directly connected to the CC25 30 through the 12 C bus and link. TSL2561 is suitable for various complex greenhouse environments because of its small size and low power consumption. The collected light intensity can also be converted into a digital signal for output so that the person who manages the vegetable greenhouse can obtain the change of the light intensity in the greenhouse through the system data.The electrical characteristics of TSL2561 are shown in Table5.Table 5
Electrical characteristics of TSL2561.
ParameterSwitchLowestSpecificationHighestVoltageSupply voltage—2.833.5VSCL, SDA—−0.5—0.6VSCL, SDA———3.4VPower supplyOpen—0.250.7mACurrentTurn off—3.316μA


## 3.1. Improve the Neural Network

Neural network algorithms have also been widely used in various specific scenarios. They establish a hierarchical model, and then continuously adjust the connection weights between each level according to the existing samples during the training process so that the final results are closer to reality. This article summarizes the characteristics of several common data fusion algorithms and their scope of application as shown in Table1.Table 1
Comparison of data fusion algorithms.
Fusion methodUncertaintyFusion technologyScope of applicationWeighted average method—Weighted averageConvergence of low-level dataBayesian estimationGaussian noiseBayesian estimationConvergence of high-level dataD-S evidence theory—Logical reasoningConvergence of high-level dataFuzzy logic reasoningMembershipLogical reasoningConvergence of high-level dataNeural networksLearning errorNeural networksConvergence of low-/high-level dataIt can be seen from Table1 that the neural network algorithm can not only be applied to low-level data fusion but also can be used for data fusion at a higher level. Although its structure is more complicated than general algorithms and has a higher computational load, it is used in practical applications. In general, it can usually exert better performance than general algorithms. There are many types of neural networks. Among them, the BP neural network, or back-propagation neural network, is the most widely used. It has a simple structure and can learn, organize, and adapt independently. It includes an input layer, a hidden layer, and an output layer. The hidden layer can include multiple layers, and it is widely used in prediction and other fields. The BP neural network belongs to a multi-layer feedforward network, which mainly includes forward-propagating signals and back-propagation errors. In the learning process, samples are passed through the input layer to the hidden layer, and the neurons in the hidden layer; the arithmetic processing is then passed to the output layer, and the actual data passed from the output layer are compared with the predicted data to obtain the error, and the error is propagated back. In the error propagation process, it is based on the gradient descent strategy to continuously adjust the connection weights between neurons in each layer until the final predicted value is the smallest compared to the actual value. The deviation of the hth neuron in the hidden layer is θh. Then its input formula is(1)Ah=∑i=1dVihXi.The hidden layer transfer function is generally sigmoid function, namely:(2)fx=11+e−x.The output formula of the hth neuron in the hidden layer is(3)Bh=fAh−θh.The input formula of the hth neuron in the hidden layer and the jth neuron in the output layer is(4)Cj=∑h=1gWhjBh.In this article, the transfer function of the output layer also selects the sigmoid function, and the output formula of the jth neuron in the output layer can be obtained as(5)Yj=fCj−θj.If the actual value of the jth neuron in the output layer is set toRj, then the error of the jth neuron is Ej, namely:(6)Ej=12Rj−Yj.The total error of network output is(7)E=12±∑j=1lRj−Yj2.The BP neural network needs to use the gradient descent method to obtain the optimal output and expand the error to the hidden layer. The formula is(8)E=12∑j=1lRj−fCj−θj2.The formula for the change in connection weights of the hth neuron in the hidden layer and the jth neuron in the output layer is(9)ΔWhj=−η∂E∂Whj.According to the aforementioned formula, the formula is(10)∂Cj∂Whj=Bh.According to the nature of the S function,(11)f′x=fx.1−fx.Then there is(12)gj=∂E∂Yj.∂Yj∂Cj=−Yj−Rj.Yj.1−Yj.Then the weight change amount is(13)ΔWhj=ηgjBh.The weight adjustment formula is(14)Whjn+1=Whjn+Whj.At the same time, the amount of change in the threshold can be expressed by a formula:(15)Δθj=−η∂E∂θj=−ηgj.The threshold adjustment formula is(16)θjn+1=θjn+Δθj.The weight adjustment formula is(17)Vihn+1=Vihn+ΔVih.The threshold change is(18)Δθh=−ηeh.The threshold is adjusted to(19)θhn+1=θhn+Δθh.Regarding many existing problems of neural networks, many researchers have proposed optimization methods. Among them, artificial intelligence-based heuristic algorithms are combined with them to improve the effect of neural network algorithms. This article mainly introduces the three most typical algorithms among which are the improved methods based on PSO, GA, and ACO.PSO is a natural heuristic algorithm. It is a process of randomly initializing the solution and searching for the best solution through an iterative method. The obtained solution is judged by the fitness function to determine whether it is optimal. The PS0 algorithm is often used in the BP neural network because it can well solve the long time-consuming training of the BP neural network, causing the defect of local minimums. The speed and position update formulas are(20)Vidk+1=ωVidk+c1r1pidk−Xidk+c2r2pgdk−Xidk,Xidk+1=Xidk+rVidk+1.GA is an algorithm that imitates the theory of biological evolution and finds the best solution by simulating the natural evolution process. GA sorts and combines the optimized parameters required using the BP neural network to form a chromosome. Multiple chromosomes form a population. The excellent genes in the population will be passed on to the next generation. New individuals can also be synthesized through crossover, mutation, and other methods. This is expressed as a formula:(21)Pc=Pcmaxf′<favgPcmax−Pcmin1+exp2Af′−favg/fmax−favgf′≥favg,Pm=Pmmaxf<favgPmmax−Pmmin1+exp2Af−favg/fmax−favg−1+Pmminf′≥favg.ACO is an algorithm to find the optimal solution by imitating the ant foraging form, and it can also be used to find the global optimal path. It is expressed as a formula:(22)τijt+1=1−ρ.τijt+.τij.The increment of interest-bearing element is calculated as(23)Δτij=∑k=1KΔτijk.The BP neural network in this article uses a three-layer structure. The prediction of ammonia nitrogen is of great significance to the accurate grasp of water quality. The following are input parameters of this article: water temperature, pH, dissolved oxygen, conductivity, turbidity, and permanganate index; the number of input layer nodes is 6, and the output selection is ammonia nitrogen (Cmg/L); the number of output layer nodes is 1, the number of hidden layer nodes is 6, the number of iterations is 100, and the training function is trainlm; the population of seagulls is 100, and the number of iterations is 100. Figure1 shows the convergence comparison of the two model algorithms.Figure 1
Comparison of SOA-BP and BP convergence.It can be observed from Figure1 that the SOA-BP model converges faster than the BP model. In terms of convergence accuracy, the same is iterated 100 times, the convergence value of the BP model is 4.536 e−7, and the convergence value of the proposed SOA-BP model is 2.029 e−8; the convergence accuracy of the proposed model is higher. The simulation of 23 sets of verification data is shown in Figure 2.Figure 2
Comparison of SOA-BP and BP predicted and actual values.


## 3.2. Sensor Vegetable Greenhouse

In today’s developed network environment, the wireless sensor network system has also become an emerging collection, transmission, and processing technology. According to the actual requirements of the collected environmental information in the vegetable greenhouse, this article studies the framework of the wireless sensor network and adjusts the important environmental factors that affect the vegetable greenhouse according to the collected data. Assuming that the technician who manages the vegetable greenhouse finds that the collected data and the parameter values that affect the environment set in advance have disagreements, then the ZigBee intelligent control technology can be used to implement remote operation of the on-off valves set in the vegetable greenhouse, human intervention, and control of the environment in the vegetable greenhouse. The schematic diagram of the experimental framework designed and studied in this article is shown in Figure3.Figure 3
System overall architecture diagram.The ZDO in the ZigB ee protocol stack is mainly responsible for the management plan of all sensor nodes. The application layer can use the ZDO service to obtain all the network information that it wants. In addition, ZDO provides binding services. The working mechanism of the protocol is shown in Table2.Table 2
Protocol system and protocol code.
Layered protocol stack systemProtocol code filePhysical layer (PHY)Hardware layer (HAL)Control sublayer (MAC)Link layer (Zmac)Network layer (NWK)Network layer (NWK)Application support layer (APS)Network layer (NWK)Program framework (AF)Profile and programZigbee device object ZDODevice object directory ZDOIn this article, the SHT 10 sensors used in the vegetable greenhouse network are all calibrated in advance so that they can be directly linked with the CC2530 and used. The electrical characteristics of SHT 10 are shown in Table3.Table 3
SHT10 electrical characteristics.
ParameterConditionMin.Typ.Max.UnitPower supply DC—2.365.5VSupply currentMeasuring540—μAAverage227—μADormant—0.40.6μALow-level output voltagelCL < 4 mA0—260mVHigh-level output voltageRp < 25 kΩ90%—100%VddLow-level input voltageFalling edge0—30%VddHigh-level input voltageRising edge80%—100%VddInput current on the pad———1μAThe functional characteristics are shown in Table4.Table 4
SHT 10 functional characteristics.
HumidityTemperatureParameterNumerical valueParameterNumerical valueMeasuring range10∼97% RHMeasuring range−40∼110°CResolution0.03% RHResolution0.02°CRepeatability0.02% RHRepeatability0.4°CMeasurement accuracy−3.8% RH∼3.9% RHMeasurement accuracy−0.4°C∼0.5°CHysteresis−0.1% RH∼0.1% RHDriftLess than 0.4°CThe TSL2561 hardware in this article has a particularly precise and simple structure so that the clock and serial data lines can be directly connected to the CC25 30 through the 12 C bus and link. TSL2561 is suitable for various complex greenhouse environments because of its small size and low power consumption. The collected light intensity can also be converted into a digital signal for output so that the person who manages the vegetable greenhouse can obtain the change of the light intensity in the greenhouse through the system data.The electrical characteristics of TSL2561 are shown in Table5.Table 5
Electrical characteristics of TSL2561.
ParameterSwitchLowestSpecificationHighestVoltageSupply voltage—2.833.5VSCL, SDA—−0.5—0.6VSCL, SDA———3.4VPower supplyOpen—0.250.7mACurrentTurn off—3.316μA


## 4. Agricultural Product Supply Chain Management and Model Construction

### 4.1. Current Status of Agricultural Product Supply Chain Management

The traditional agricultural product supply chain is basically a temporary one-off product trading market; that is, between the processing enterprises responsible for agricultural products and the farmers and sellers, they may all be one-time transactions or are particularly loose in a short time. The two parties or three parties rely on it. The current market prices of agricultural products carry out the trading of funds and agricultural products. The detailed structure diagram is shown in Figure4:Figure 4
Traditional processing supply chain enterprise connection structure diagram.It can be seen from Figure4 that basically all transactions are temporary, which cannot ensure the stability between the three. The farmers and sellers of agricultural products are completely separated from each other, and they are unable to obtain timely and accurate information on the current market transactions, resulting in untargeted cultivation of agricultural products, and this also makes it impossible for agricultural product processing enterprises to obtain sustainable gains. The raw materials needed for agricultural products have led to higher production costs for enterprises. In addition, the quality of agricultural products cannot be uniformly planned and managed, which has also caused many problems. The structure diagram is shown in Figure 5, but the integrated structure model still has many drawbacks:Figure 5
Vertical integration structure diagram.As many domestic enterprises are unable to move forward, it is difficult to determine the supply and demand relationship of agricultural products with farmers. The agricultural product processing enterprise was unable to determine whether to produce the product, which resulted in a breach of contract between the two parties, and the farmers also did not have the technical means of the agricultural product processing enterprise to help the quality of the agricultural products. This is also the reason that the enterprises responsible for agricultural products processing are too burdened. Agricultural products processing enterprises also need to increase the cost of organization and management to be able to coordinate the interests of each department, which also increases the difficulty of enterprise supervision. Doing so is also uncertain whether it can reduce production costs.


### 4.2. Main Issues of China’s Agricultural Product Supply Chain Management

At present, the circulation of domestic agricultural products is still in the contradiction between small production scale and huge market scale. Small-scale production scale refers to the production model in which domestic agricultural products are grown and sold by scattered farmers. The farmers themselves cannot bring the products into the market for trading, and it is difficult for agricultural products to circulate in the market. This contradictory restraint has contributed to the current situation in China. A buying and selling market is dominated by a wholesale market. Today’s agricultural product supply chain is divided into parts from production to circulation, which can be understood as moving from farmers to the wholesale market; there is another part from circulation to consumers, which is understood as the circulation from the wholesale market to the demanders, and from farmers to consumers. It is developed with the wholesale market as the center. Therefore, according to the existing wholesale market-centric form, the agricultural product supply chain is a small short chain, and there is no way to apply the supply chain management model.Since the members of the farm product supply chain are composed of different economic systems, there is a contradiction in economic interests between the two parties, and this contradiction will cause conflicts among the personnel, resulting in no way to target every step in the agricultural product supply chain. However, in the management process of the agricultural product supply chain, everyone needs to participate in the cooperation with each other to be better, to establish sufficient trust with each other, and to be able to make better use of the supply chain management model. When implementing supply chain management, in terms of enterprises, it is not necessary to use the original buying and selling thinking to consider issues in order to change their minds, and to share relevant responsibilities with partners and enjoy the benefits together.The management model of important enterprises in the foreign agricultural product supply chain has evolved from internal cost and production management to external supply chain management. The points of competition among various enterprises have also changed from the original low-end price competition to the high-end long-term strategic choice and specific implementation aspects. The foreign agricultural product supply chain model is based on the unified and large-scale production of agricultural products. Each member of the agricultural product supply chain has relatively balanced rights in the demand market. Compared with other countries, there is a gap between China and other countries in terms of management ideas, cooperation methods, logistics systems, and information systems in agricultural supply chain management. Due to the existence of these problems, there is a gap between China and other countries in the production and operation model of agricultural products, and the composition of the industrial chain. Some important influencing factors are analyzed, as shown in Table6.Table 6
Comparison of Chinese and foreign agricultural product supply chains.
ChinaForeignSupply chain management conceptOutdated concept, almost no supply chain management departmentAdopt a supply chain management competition strategyCooperation mechanismSmall production leads to uneven market power, and it is difficult to establish a cooperative mechanismVertical integration or horizontal integration cooperation mechanism, contract systemLogistics systemRepeated construction, low response, low cold chain technologyProfessional logistics, advanced technology and equipment, high level of cold chainInformation systemInsufficient application, low level of information technology, low sharingComplete information center with openness, node applicability, and scalability


### 4.3. Construction of China’s Agricultural Product Supply Chain Management Model

Chinese consumers have a habit of eating fresh fruits and vegetables for a long time. This leads to two aspects in the form of purchase. One is direct consumption, that is, the purchase of agricultural products that do not need to be processed by factories or enterprises. Agricultural products that have only undergone simple processing are also known as non-processing and primary processing. Another is that agricultural products are purchased and consumed after processing by the enterprise, which is the use of fine processing or deep processing of agricultural products. Fine processing or deep processing refers to the secondary or more processing of agricultural products. It mainly refers to improving and reusing the protein components, plant fiber components, oil components, and new nutrients of agricultural products. Fine and deep processing will lead to the quality of agricultural products. The main objects of direct consumption are raw and fresh agricultural products, and the main objects of consumption after processing are processed agricultural products that can be used for food. Based on the aforementioned two key consumption methods, this article can divide the agricultural product supply chain into fresh agricultural product supply chain and agricultural product processing supply chain. The supply chain of fresh agricultural products can be understood as an economic activity in which fresh and live agricultural products go directly from the producers of agricultural products to consumers. Its focus is on how to ensure the freshness of agricultural products. The agricultural product processing supply chain can be understood as an economic activity in which processed products obtained after agricultural products are processed from enterprise processors to consumers, and its focus is to process agricultural products. Therefore, the management of the agricultural product supply chain must take into account the characteristics of the supply chain of various types of agricultural products.The center of the agricultural product processing supply chain management is the enterprise responsible for agricultural product processing. The enterprise builds a good partnership with supply and demand companies, and then cooperates and manages agricultural product logistics, market information, and capital flow to complete agricultural product processing. The process of processing and value enhancement brings a variety of products to consumers. The premise of agricultural product processing supply chain management is the management of agricultural products, from the initial production of agricultural products to the process of processing, and finally to consumption. Then there is the management of the processing supply chain; the purpose is to average the benefits of each point in the supply chain, improve the economy of the entire supply chain, and also meet consumer needs. Through the processing of agricultural products, especially the deep processing of agricultural products, the original properties of agricultural products have been transformed and their value in the circulation process has been enhanced.The ultimate goal of fresh agricultural product supply chain management is to meet the requirements of consumers. It centers on service companies, and builds good partnerships with supply and demand companies, and then through the logistics, market information, and funding of agricultural products. Cooperation and management of flow and other aspects to complete the increase in the value of fresh agricultural products provides consumers with fresh and live agricultural products. The premises of the management of the fresh produce supply chain and the management of the supply chain of produce processing are the same in this respect. They are both the process of transferring agricultural products from production to processing to the sales link, and then the management of the fresh produce supply chain. The ultimate goal of controlling the logistics and market information of agricultural products is to meet the needs of consumers. In addition, the increase in the service value of the fresh agricultural product supply chain can also be reflected in the operation and service management of service-oriented enterprises. For example, factors such as the shopping environment of consumers in retail supermarkets and the quality of employee services can also affect whether consumers buy or not.


## 4.1. Current Status of Agricultural Product Supply Chain Management

The traditional agricultural product supply chain is basically a temporary one-off product trading market; that is, between the processing enterprises responsible for agricultural products and the farmers and sellers, they may all be one-time transactions or are particularly loose in a short time. The two parties or three parties rely on it. The current market prices of agricultural products carry out the trading of funds and agricultural products. The detailed structure diagram is shown in Figure4:Figure 4
Traditional processing supply chain enterprise connection structure diagram.It can be seen from Figure4 that basically all transactions are temporary, which cannot ensure the stability between the three. The farmers and sellers of agricultural products are completely separated from each other, and they are unable to obtain timely and accurate information on the current market transactions, resulting in untargeted cultivation of agricultural products, and this also makes it impossible for agricultural product processing enterprises to obtain sustainable gains. The raw materials needed for agricultural products have led to higher production costs for enterprises. In addition, the quality of agricultural products cannot be uniformly planned and managed, which has also caused many problems. The structure diagram is shown in Figure 5, but the integrated structure model still has many drawbacks:Figure 5
Vertical integration structure diagram.As many domestic enterprises are unable to move forward, it is difficult to determine the supply and demand relationship of agricultural products with farmers. The agricultural product processing enterprise was unable to determine whether to produce the product, which resulted in a breach of contract between the two parties, and the farmers also did not have the technical means of the agricultural product processing enterprise to help the quality of the agricultural products. This is also the reason that the enterprises responsible for agricultural products processing are too burdened. Agricultural products processing enterprises also need to increase the cost of organization and management to be able to coordinate the interests of each department, which also increases the difficulty of enterprise supervision. Doing so is also uncertain whether it can reduce production costs.


## 4.2. Main Issues of China’s Agricultural Product Supply Chain Management

At present, the circulation of domestic agricultural products is still in the contradiction between small production scale and huge market scale. Small-scale production scale refers to the production model in which domestic agricultural products are grown and sold by scattered farmers. The farmers themselves cannot bring the products into the market for trading, and it is difficult for agricultural products to circulate in the market. This contradictory restraint has contributed to the current situation in China. A buying and selling market is dominated by a wholesale market. Today’s agricultural product supply chain is divided into parts from production to circulation, which can be understood as moving from farmers to the wholesale market; there is another part from circulation to consumers, which is understood as the circulation from the wholesale market to the demanders, and from farmers to consumers. It is developed with the wholesale market as the center. Therefore, according to the existing wholesale market-centric form, the agricultural product supply chain is a small short chain, and there is no way to apply the supply chain management model.Since the members of the farm product supply chain are composed of different economic systems, there is a contradiction in economic interests between the two parties, and this contradiction will cause conflicts among the personnel, resulting in no way to target every step in the agricultural product supply chain. However, in the management process of the agricultural product supply chain, everyone needs to participate in the cooperation with each other to be better, to establish sufficient trust with each other, and to be able to make better use of the supply chain management model. When implementing supply chain management, in terms of enterprises, it is not necessary to use the original buying and selling thinking to consider issues in order to change their minds, and to share relevant responsibilities with partners and enjoy the benefits together.The management model of important enterprises in the foreign agricultural product supply chain has evolved from internal cost and production management to external supply chain management. The points of competition among various enterprises have also changed from the original low-end price competition to the high-end long-term strategic choice and specific implementation aspects. The foreign agricultural product supply chain model is based on the unified and large-scale production of agricultural products. Each member of the agricultural product supply chain has relatively balanced rights in the demand market. Compared with other countries, there is a gap between China and other countries in terms of management ideas, cooperation methods, logistics systems, and information systems in agricultural supply chain management. Due to the existence of these problems, there is a gap between China and other countries in the production and operation model of agricultural products, and the composition of the industrial chain. Some important influencing factors are analyzed, as shown in Table6.Table 6
Comparison of Chinese and foreign agricultural product supply chains.
ChinaForeignSupply chain management conceptOutdated concept, almost no supply chain management departmentAdopt a supply chain management competition strategyCooperation mechanismSmall production leads to uneven market power, and it is difficult to establish a cooperative mechanismVertical integration or horizontal integration cooperation mechanism, contract systemLogistics systemRepeated construction, low response, low cold chain technologyProfessional logistics, advanced technology and equipment, high level of cold chainInformation systemInsufficient application, low level of information technology, low sharingComplete information center with openness, node applicability, and scalability


## 4.3. Construction of China’s Agricultural Product Supply Chain Management Model

Chinese consumers have a habit of eating fresh fruits and vegetables for a long time. This leads to two aspects in the form of purchase. One is direct consumption, that is, the purchase of agricultural products that do not need to be processed by factories or enterprises. Agricultural products that have only undergone simple processing are also known as non-processing and primary processing. Another is that agricultural products are purchased and consumed after processing by the enterprise, which is the use of fine processing or deep processing of agricultural products. Fine processing or deep processing refers to the secondary or more processing of agricultural products. It mainly refers to improving and reusing the protein components, plant fiber components, oil components, and new nutrients of agricultural products. Fine and deep processing will lead to the quality of agricultural products. The main objects of direct consumption are raw and fresh agricultural products, and the main objects of consumption after processing are processed agricultural products that can be used for food. Based on the aforementioned two key consumption methods, this article can divide the agricultural product supply chain into fresh agricultural product supply chain and agricultural product processing supply chain. The supply chain of fresh agricultural products can be understood as an economic activity in which fresh and live agricultural products go directly from the producers of agricultural products to consumers. Its focus is on how to ensure the freshness of agricultural products. The agricultural product processing supply chain can be understood as an economic activity in which processed products obtained after agricultural products are processed from enterprise processors to consumers, and its focus is to process agricultural products. Therefore, the management of the agricultural product supply chain must take into account the characteristics of the supply chain of various types of agricultural products.The center of the agricultural product processing supply chain management is the enterprise responsible for agricultural product processing. The enterprise builds a good partnership with supply and demand companies, and then cooperates and manages agricultural product logistics, market information, and capital flow to complete agricultural product processing. The process of processing and value enhancement brings a variety of products to consumers. The premise of agricultural product processing supply chain management is the management of agricultural products, from the initial production of agricultural products to the process of processing, and finally to consumption. Then there is the management of the processing supply chain; the purpose is to average the benefits of each point in the supply chain, improve the economy of the entire supply chain, and also meet consumer needs. Through the processing of agricultural products, especially the deep processing of agricultural products, the original properties of agricultural products have been transformed and their value in the circulation process has been enhanced.The ultimate goal of fresh agricultural product supply chain management is to meet the requirements of consumers. It centers on service companies, and builds good partnerships with supply and demand companies, and then through the logistics, market information, and funding of agricultural products. Cooperation and management of flow and other aspects to complete the increase in the value of fresh agricultural products provides consumers with fresh and live agricultural products. The premises of the management of the fresh produce supply chain and the management of the supply chain of produce processing are the same in this respect. They are both the process of transferring agricultural products from production to processing to the sales link, and then the management of the fresh produce supply chain. The ultimate goal of controlling the logistics and market information of agricultural products is to meet the needs of consumers. In addition, the increase in the service value of the fresh agricultural product supply chain can also be reflected in the operation and service management of service-oriented enterprises. For example, factors such as the shopping environment of consumers in retail supermarkets and the quality of employee services can also affect whether consumers buy or not.


## 5. Conclusion

In this article, the optimized neural network system sensors are added to the vegetable greenhouse. This article analyzes the environmental detection and control system in the vegetable greenhouse in detail, and summarizes the overall system separately. Among them, the sensor network using the ZigBee intelligent control technology collects environmental data in the vegetable greenhouse, and then builds the model based on the intelligent algorithm, uses the ACARS system and the fast access recorder data to construct the sample, and finally optimizes it. It not only improves the accuracy of the transmission process of the overall collected data but also balances the current network load capacity. In terms of collecting data sensors, this article also carried out a more detailed explanation of the system software and hardware of the sensors used in temperature, humidity, and light intensity, and explained their working principles and theoretical principles. We can also use the ZigBee intelligent control technology to remotely operate the switch valves set in the vegetable greenhouse to adjust the important environmental factors that affect the vegetable greenhouse. This method is used to artificially intervene and control the environment in the vegetable greenhouse. In addition, this article also applies the optimized and improved neural network system to agricultural product supply chain management. In view of the analysis of the current situation of China’s agricultural product supply chain management, as well as the agricultural product processing supply chain management model and the fresh supply chain management model and the factors that affect them, the overall and comprehensive analyses of key enterprises and supply and demand enterprises in the supply chain, it can be concluded that the traditional agricultural product supply chain is basically a temporary one-off product trading market, that is, between the processing enterprises responsible for agricultural products, farmers, and sellers, they may all be one-off transactions or are particularly loose in a short period of time; the agricultural products are grown and sold by scattered farmers themselves. The farmers themselves cannot bring their products to the market for trading. It is difficult for agricultural products to be circulated in the market, resulting in a small short chain of agricultural product supply chain. Supply chain management mode application, which is the focus of the fresh agricultural product supply chain, is how to ensure the freshness of agricultural products. The ultimate goal of the fresh agricultural product supply chain management is to meet the requirements of consumers. The focus of the agricultural product processing supply chain is to process agricultural products. The center of agricultural product processing supply chain management is the enterprise responsible for agricultural product processing; therefore, the agricultural product supply chain management method must fully consider various types of supply chains.



---

*Source: 4139784-2022-08-18.xml*