CN111640039A - Facility vegetable disease control recommendation system and method - Google Patents
Facility vegetable disease control recommendation system and method Download PDFInfo
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- CN111640039A CN111640039A CN202010575990.7A CN202010575990A CN111640039A CN 111640039 A CN111640039 A CN 111640039A CN 202010575990 A CN202010575990 A CN 202010575990A CN 111640039 A CN111640039 A CN 111640039A
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- 201000010099 disease Diseases 0.000 title claims abstract description 144
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 title claims abstract description 144
- 235000013311 vegetables Nutrition 0.000 title claims abstract description 137
- 238000000034 method Methods 0.000 title claims abstract description 16
- 230000007613 environmental effect Effects 0.000 claims abstract description 73
- 239000013598 vector Substances 0.000 claims abstract description 35
- 238000007781 pre-processing Methods 0.000 claims abstract description 18
- 239000002689 soil Substances 0.000 claims description 27
- 238000010606 normalization Methods 0.000 claims description 8
- 238000012935 Averaging Methods 0.000 claims description 6
- 230000006806 disease prevention Effects 0.000 claims description 6
- 239000000575 pesticide Substances 0.000 claims description 6
- 238000001556 precipitation Methods 0.000 claims description 6
- 238000013480 data collection Methods 0.000 claims description 3
- 230000002265 prevention Effects 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 3
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- 230000003993 interaction Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 241000223600 Alternaria Species 0.000 description 1
- 235000010799 Cucumis sativus var sativus Nutrition 0.000 description 1
- 244000299906 Cucumis sativus var. sativus Species 0.000 description 1
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- 102100025902 RNA-binding protein 3 Human genes 0.000 description 1
- 241000607479 Yersinia pestis Species 0.000 description 1
- 238000012271 agricultural production Methods 0.000 description 1
- 230000000875 corresponding effect Effects 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
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- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Mining
Abstract
A facility vegetable disease control recommendation system and method comprises a data acquisition system, a data preprocessing system, a vegetable disease prediction system and a vegetable disease control recommendation system; the data acquisition system is used for acquiring environmental information related to occurrence and prevalence of vegetable diseases and transmitting the environmental information to the data preprocessing system; the data preprocessing system is used for discretizing and normalizing environmental information related to occurrence and prevalence of vegetable diseases; constructing an environment information characteristic vector and transmitting the environment information characteristic vector to a vegetable disease prediction system; the vegetable disease prediction system is used for converting the environmental information characteristic vector of vegetable growth into a characteristic space related to disease occurrence and transmitting the characteristic space to the vegetable disease control recommendation system; the vegetable disease control recommendation system is used for recommending a control scheme to a user. The invention utilizes a large amount of environmental information related to the occurrence and prevalence of the diseases to predict the probability of the occurrence and prevalence of the vegetable diseases, and the result is more stable and reliable.
Description
Technical Field
The invention belongs to the field of agricultural precision, and relates to a recommendation system and method for disease control of facility vegetables.
Background
Diseases of greenhouse vegetables seriously affect the yield and quality of the vegetables, and a common control measure is chemical control. Although the mode can effectively prevent and treat corresponding diseases, the method not only wastes resources and increases the agricultural production cost, but also pollutes the ecological environment, and the pesticide residue harms the health of consumers. Relevant research and production practices show that the occurrence and prevalence of vegetable diseases are the result of the comprehensive effects of vegetables, pests, weather and weather conditions, cultivation management measures and the like; the occurrence, development and circulation of major vegetable diseases are closely related to meteorological conditions. Particularly, weather conditions with a large number of rainfall days, a large rainfall amount and a high humidity are easy to cause disease epidemics, but the influence weights of the rainfall amount, the rainfall days and the rainfall intensity on the disease occurrence are not consistent, such as: the key factors of the occurrence of the cucumber alternaria leaf spot are the average relative humidity and the total rainfall days in the epidemic period, the rainfall has no obvious influence, the disease condition is positively correlated with the average relative humidity in the extension period and negatively correlated with the rainfall days in the peak period. The occurrence reasons of the diseases of the greenhouse vegetables are complex and variable, the occurrence reasons are related to a plurality of factors such as seasons, temperature, humidity, illumination, rainfall, air pressure, wind speed and wind direction, complex interaction and mutual influence exist among the factors, and the growth environments (such as greenhouse structure, greenhouse covering time, temperature and humidity, illumination, soil conditions and the like) of the vegetables in each greenhouse are different, so that the occurrence time, the occurrence types, the occurrence reasons, the damage degree and the like of the vegetables in different greenhouses are different, and most fruit growers are difficult to accurately predict and diagnose the diseases. The vegetable disease image and the growing environment data can be accurately obtained through the Internet of things, and the vegetable planting and the disease control thereof are guided to a certain extent through the data, but the data are massive, complex and multi-source heterogeneous, the vegetable diseases are often caused by the interaction of the data, and the utilization rate of the data in the existing research is still not high. Jiaduo corporation (http:// www.atcsp.com/yupling /) and Topu corporation (http:// www.tpyn.net/the me/20161226/index. html) developed Internet of things crop disease monitoring systems, respectively. The two systems rely on 15 factors such as air temperature and humidity, surface temperature and humidity, wind speed and wind direction and video images which are automatically acquired by the internet of things sensor, so that the occurrence rule of vegetable diseases can be predicted, the visualization, networking and automation of vegetable disease prediction and forecast are realized, and the vegetable major disease control is scientifically guided, but the two systems have higher cost. Deep learning is a classification and prediction method widely applied at present and has high accuracy. The technology is applied to early warning of vegetable diseases, can effectively and accurately judge the internal relation between the environmental information of vegetable growth and the occurrence and prevalence of the diseases, and has important practical significance in the aspect of vegetable disease control.
Disclosure of Invention
The invention aims to provide a facility vegetable disease prevention and control recommendation system and method, wherein a deep confidence network is adopted to predict the possibility of vegetable disease occurrence and prevalence, and the system is continuously corrected and optimized according to feedback system information, so that the recommendation scheme is better.
In order to achieve the purpose, the invention provides the following technical scheme:
a facility vegetable disease control recommendation system comprises a data acquisition system, a data preprocessing system, a vegetable disease prediction system and a vegetable disease control recommendation system; wherein the content of the first and second substances,
the data acquisition system is used for acquiring environmental information related to occurrence and prevalence of vegetable diseases and then transmitting the acquired environmental information related to occurrence and prevalence of vegetable diseases to the data preprocessing system;
the data preprocessing system is used for discretizing the environment information which is collected by the data collecting system and related to the occurrence and prevalence of vegetable diseases, and then normalizing the discretized environment information; performing daily averaging, finally forming a 14-dimensional vector as an environmental information vector sample of the current day, constructing an environmental information characteristic vector according to the environmental information vector sample of the current day, and transmitting the environmental information characteristic vector to a vegetable disease prediction system;
the vegetable disease prediction system is used for converting the environmental information characteristic vector of vegetable growth into a characteristic space related to disease occurrence, then obtaining hierarchical characteristic representation through automatic learning, then predicting the occurrence probability of vegetable diseases through the hierarchical characteristic representation, and transmitting the occurrence probability of vegetable diseases to the vegetable disease control recommendation system;
the vegetable disease control recommendation system is used for recommending a control scheme to a user according to the received vegetable disease occurrence probability.
The invention is further improved in that the vegetable is mixed withThe environmental information related to disease occurrence and prevalence comprises disease season, soil salinity in the shed, whether the soil in the shed is continuously planted, soil temperature in the shed, soil humidity in the shed, air temperature, air humidity, rainfall days, precipitation amount, CO2Concentration, number of pesticide applications, average daily temperature, average daily relative humidity, and average daily light.
The invention has the further improvement that the discretized environment information is normalized by using the formula (1);
wherein, ajValue before normalization for jth data of environmental information, bjIs the normalized value of the j-th data of the environmental information, wherein j is 1,2, and n is the total number of the vegetable disease categories,
is the maximum value among the n numbers of the environment information,
is the minimum value of n numbers of the environment information.
The vegetable disease prediction system is further improved in that the vegetable disease prediction system is realized through a deep confidence network, and the deep confidence network consists of three layers of limiting Boltzmann machine networks and one distinguishing limiting Boltzmann machine network.
The vegetable disease prevention and treatment recommendation system is further improved in that the vegetable disease prevention and treatment recommendation system is composed of a rule base, a fact base and an inference machine.
A recommended method for preventing and treating diseases of greenhouse vegetables comprises the steps of collecting environmental information related to occurrence and prevalence of vegetable diseases through a data collection system, and then transmitting the collected environmental information related to the occurrence and prevalence of the vegetable diseases to a data preprocessing system;
discretizing the environmental information related to the occurrence and prevalence of vegetable diseases, which is acquired by the data acquisition system, by a data preprocessing system, and then normalizing; performing daily averaging, finally forming a 14-dimensional vector as an environmental information vector sample of the current day, and constructing an environmental information characteristic vector according to the environmental information vector sample of the current day;
converting the environmental information characteristic vector of the vegetable growth into a characteristic space related to the occurrence of diseases through a vegetable disease prediction system, then obtaining hierarchical characteristic representation through automatic learning, predicting the occurrence probability of vegetable diseases according to the hierarchical characteristic representation, and transmitting the occurrence probability of the vegetable diseases to a vegetable disease control recommendation system;
and recommending a prevention and control scheme to the user according to the received vegetable disease occurrence probability through a vegetable disease prevention and control recommendation system.
The invention is further improved in that the environmental information related to the occurrence and prevalence of vegetable diseases includes the season of the disease, the salinity of the soil in the shed, whether the soil in the shed is continuously planted, the temperature of the soil in the shed, the humidity of the soil in the shed, the temperature of the air, the humidity of the air, the number of days of rainfall, the amount of precipitation, and CO2Concentration, number of pesticide applications, average daily temperature, average daily relative humidity, and average daily light.
In a further development of the invention, the normalization is carried out using formula (1);
wherein, ajValue before normalization for jth data of environmental information, bjIs the normalized value of the j-th data of the environmental information, wherein j is 1,2, and n is the total number of the vegetable disease categories,
is the maximum value among the n numbers of the environment information,
is the minimum value of n numbers of the environment information.
The invention has the following advantages and economic effects:
(1) according to the method, the probability of occurrence and prevalence of the vegetable diseases is predicted by using the deep belief network, and the vegetable disease control recommendation system continuously optimizes the recommendation system and the deep belief network structure according to the feedback system information to obtain an effective and practical vegetable disease control recommendation system.
(2) The invention utilizes a large amount of environmental information related to the occurrence and prevalence of diseases to predict the probability of the occurrence and prevalence of vegetable diseases, and the disease prediction result is more accurate and reliable.
Drawings
In order to make the object, technical solution and advantages of the present invention more clear, the present invention provides the following drawings for illustration.
FIG. 1 is a schematic diagram of the operation of the present invention;
fig. 2 is a diagram of a deep belief network architecture.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, a facility vegetable disease control recommendation system based on a deep belief network and environmental information includes a data acquisition system, a data preprocessing system, a vegetable disease prediction system, a vegetable disease control recommendation system, and a vegetable disease control effect information feedback system.
The data acquisition system is used for acquiring environmental information related to occurrence and prevalence of vegetable diseases, and the environmental information related to occurrence and prevalence of vegetable diseases comprises disease season, soil salinity in the shed, whether the soil in the shed is continuously planted, soil temperature in the shed, soil humidity in the shed, air temperature, air humidity, rainfall days, precipitation, CO214 kinds of climate, weather and soil information related to the occurrence of diseases, such as concentration, pesticide use times, daily average temperature, daily average relative humidity and daily average illumination, and then transmitting the collected environmental information related to the occurrence and prevalence of vegetable diseases to a data preprocessing system.
The data preprocessing system quantizes the environmental information data acquired by the data acquisition system to obtain discretization data, then normalizes each discretized environmental information by using a formula (1),
wherein, ajValue before normalization for jth data of environmental information, bjIs the normalized value of the j-th data of the environmental information, wherein j is 1,2, and n is the total number of the vegetable disease categories,
is the maximum value among the n numbers of the environment information,
is the minimum value of n numbers of the environment information.
Discretizing and normalizing each environmental information acquired for multiple times every day, then carrying out daily averaging, finally forming a 14-dimensional vector as an environmental information vector sample of the current day, and constructing an environmental information feature vector according to the environmental information vector sample of the current day.
The vegetable disease prediction system is realized by a depth confidence network, environmental information characteristic vectors of vegetable growth are converted into characteristic space relevant to disease occurrence through a three-layer restricted Boltzmann machine network, then hierarchical characteristic representation is obtained through automatic learning, the occurrence probability of vegetable diseases is predicted through the depth confidence network, and the occurrence probability of the vegetable diseases is transmitted to the vegetable disease control recommendation system.
As shown in FIG. 2, the deep confidence network comprises three layers of limiting Boltzmann machine networks and one distinguishing limiting Boltzmann machine network, wherein RBM1, RBM2 and RBM3 are three limiting Boltzmann machine networks, and w1、w2And w3Three limiting boltzmann machine network parameters.
The vegetable disease control recommendation system consists of a rule base, a fact base and an inference machine, and recommends a control scheme to a user according to the received vegetable disease occurrence probability; feeding back feedback information of the recommendation system to the feedback system;
and the feedback system corrects the vegetable disease control scheme recommendation system according to the used feedback information and optimizes the structure of the boltzmann machine network.
The method of the facility vegetable disease control recommendation system based on the deep confidence network and the environmental information comprises the following steps:
the method comprises the steps of collecting environmental information related to occurrence and prevalence of vegetable diseases through a data collection system, and then transmitting the collected environmental information related to occurrence and prevalence of vegetable diseases to a data preprocessing system. Wherein the environmental information related to occurrence and prevalence of vegetable diseases comprises disease season, soil salinity in the shed, whether the soil in the shed is continuously planted, soil temperature in the shed, soil humidity in the shed, air temperature, air humidity, days of rainfall, precipitation, CO214 kinds of climate, weather and soil information related to the occurrence of diseases, such as concentration, pesticide use times, daily average temperature, daily average relative humidity and daily average illumination.
Quantizing each environmental information data acquired by the data acquisition system through a data preprocessing system, and then normalizing each discretized environmental information by using a formula (1);
wherein, ajValue before normalization for jth data of environmental information, bjIs the normalized value of the j-th data of the environmental information, wherein j is 1,2, and n is the total number of the vegetable disease categories,
is the maximum value among the n numbers of the environment information,
is the minimum value of n numbers of the environment information.
And performing daily averaging, finally forming a 14-dimensional vector as an environmental information vector sample of the current day, and constructing an environmental information characteristic vector according to the environmental information vector sample of the current day.
The method comprises the steps of converting environmental information characteristic vectors of vegetable growth into characteristic spaces relevant to disease occurrence through a vegetable disease prediction system, then obtaining hierarchical characteristic representation through automatic learning, predicting the occurrence probability of vegetable diseases through the vegetable disease prediction system, and transmitting the occurrence probability of the vegetable diseases to a vegetable disease control recommendation system.
Recommending a prevention and treatment scheme to a user through the vegetable disease occurrence probability received by the vegetable disease prevention and treatment recommendation system;
feeding the control effect back to the vegetable disease control effect information feedback system after operation according to the recommended control scheme;
and correcting the vegetable disease control scheme recommendation system through a vegetable disease control effect information feedback system, and optimizing the structure of the boltzmann machine network.
The above preferred embodiments are only intended to illustrate the technical solution of the present invention and not to limit, and although the present invention has been described in detail by the above preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention defined by the appended claims.
Claims (8)
1. A facility vegetable disease control recommendation system is characterized by comprising a data acquisition system, a data preprocessing system, a vegetable disease prediction system and a vegetable disease control recommendation system; wherein the content of the first and second substances,
the data acquisition system is used for acquiring environmental information related to occurrence and prevalence of vegetable diseases and then transmitting the acquired environmental information related to occurrence and prevalence of vegetable diseases to the data preprocessing system;
the data preprocessing system is used for discretizing the environment information which is collected by the data collecting system and related to the occurrence and prevalence of vegetable diseases, and then normalizing the discretized environment information; performing daily averaging, finally forming a 14-dimensional vector as an environmental information vector sample of the current day, constructing an environmental information characteristic vector according to the environmental information vector sample of the current day, and transmitting the environmental information characteristic vector to a vegetable disease prediction system;
the vegetable disease prediction system is used for converting the environmental information characteristic vector of vegetable growth into a characteristic space related to disease occurrence, then obtaining hierarchical characteristic representation through automatic learning, then predicting the occurrence probability of vegetable diseases through the hierarchical characteristic representation, and transmitting the occurrence probability of vegetable diseases to the vegetable disease control recommendation system;
the vegetable disease control recommendation system is used for recommending a control scheme to a user according to the received vegetable disease occurrence probability.
2. The facility vegetable disease control recommendation system according to claim 1, wherein the environmental information related to occurrence and prevalence of vegetable diseases comprises disease season, soil salinity in the shed, whether the soil in the shed is continuous, soil temperature in the shed, soil humidity in the shed, air temperature, air humidity, days of rainfall, precipitation amount, CO2Concentration, number of pesticide applications, average daily temperature, average daily relative humidity, and average daily light.
3. The disease control recommendation system for greenhouse vegetables as claimed in claim 1, wherein the discretized environmental information is normalized by formula (1);
wherein, ajValue before normalization for jth data of environmental information, bjIs the normalized value of the j-th data of the environmental information, wherein j is 1,2, and n is the total number of the vegetable disease categories,
is the maximum value among the n numbers of the environment information,
is the minimum value of n numbers of the environment information.
4. The facility vegetable disease control recommendation system according to claim 1, wherein the vegetable disease prediction system is implemented by a deep belief network, and the deep belief network is composed of three layers of limiting boltzmann machine networks and one distinguishing limiting boltzmann machine network.
5. The disease control recommendation system for greenhouse vegetables as claimed in claim 1, wherein the disease control recommendation system for vegetables comprises a rule base, a fact base and an inference engine.
6. A recommending method based on the system for recommending disease control for greenhouse vegetables according to claim 1,
collecting environmental information related to occurrence and prevalence of vegetable diseases through a data collection system, and then transmitting the collected environmental information related to occurrence and prevalence of vegetable diseases to a data preprocessing system;
discretizing the environmental information related to the occurrence and prevalence of vegetable diseases, which is acquired by the data acquisition system, by a data preprocessing system, and then normalizing; performing daily averaging, finally forming a 14-dimensional vector as an environmental information vector sample of the current day, and constructing an environmental information characteristic vector according to the environmental information vector sample of the current day;
converting the environmental information characteristic vector of the vegetable growth into a characteristic space related to the occurrence of diseases through a vegetable disease prediction system, then obtaining hierarchical characteristic representation through automatic learning, predicting the occurrence probability of vegetable diseases according to the hierarchical characteristic representation, and transmitting the occurrence probability of the vegetable diseases to a vegetable disease control recommendation system;
and recommending a prevention and control scheme to the user according to the received vegetable disease occurrence probability through a vegetable disease prevention and control recommendation system.
7. The recommendation method as claimed in claim 6,the method is characterized in that the environmental information related to the occurrence and prevalence of vegetable diseases comprises disease season, soil salinity in the greenhouse, whether the soil in the greenhouse is continuously planted, soil temperature in the greenhouse, soil humidity in the greenhouse, air temperature, air humidity, rainfall days, precipitation amount, CO2Concentration, number of pesticide applications, average daily temperature, average daily relative humidity, and average daily light.
8. The recommendation method according to claim 6, characterized in that normalization is performed using equation (1);
wherein, ajValue before normalization for jth data of environmental information, bjIs the normalized value of the j-th data of the environmental information, wherein j is 1,2, and n is the total number of the vegetable disease categories,
is the maximum value among the n numbers of the environment information,
is the minimum value of n numbers of the environment information.
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Cited By (1)
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| CN114063686A (en) * | 2021-11-15 | 2022-02-18 | 宁夏农林科学院植物保护研究所（宁夏植物病虫害防治重点实验室） | Agricultural pest monitoring and early warning method |
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| CN104008633A (en) * | 2014-05-26 | 2014-08-27 | 中国农业大学 | Early-warning method and system of facility spinach diseases |
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| CN104008633A (en) * | 2014-05-26 | 2014-08-27 | 中国农业大学 | Early-warning method and system of facility spinach diseases |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114063686A (en) * | 2021-11-15 | 2022-02-18 | 宁夏农林科学院植物保护研究所（宁夏植物病虫害防治重点实验室） | Agricultural pest monitoring and early warning method |
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