CN111670748B - Internet of things-based ecological high-yield cultivation method and system for stropharia rugoso-annulata - Google Patents
Internet of things-based ecological high-yield cultivation method and system for stropharia rugoso-annulata Download PDFInfo
- Publication number
- CN111670748B CN111670748B CN202010642597.5A CN202010642597A CN111670748B CN 111670748 B CN111670748 B CN 111670748B CN 202010642597 A CN202010642597 A CN 202010642597A CN 111670748 B CN111670748 B CN 111670748B
- Authority
- CN
- China
- Prior art keywords
- time
- data
- optimal
- cultivation
- culture
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G18/00—Cultivation of mushrooms
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Abstract
The invention discloses an ecological high-yield cultivation method and system for capping red wine mushrooms based on the Internet of things, relates to the technical field of Internet of things application and agricultural planting, solves the problems of low control precision, high difficulty, long period and poor quality of the existing edible mushroom cultivation environment parameters, and has the technical scheme key points that: the growth environment parameters such as temperature, humidity, illumination, carbon dioxide concentration and the like of the target culture are reasonably adjusted according to the optimal growth environment parameters of all nutrient components of the target culture, so that the target culture can grow and develop rapidly in a reasonable environment, the efficient absorption of the nutrient components is promoted, the culture period is shortened, the content of all nutrient components of the culture is improved, the whole automatic operation is realized, the professional technical requirement is low, and the technical support is provided for the ecological, high-yield, low-cost and short-period popularization of greenhouse planting of the stropharia rugoso-annulata.
Description
Technical Field
The invention relates to the technical field of application of the Internet of things and agricultural planting, in particular to an ecological high-yield cultivation method and system for cap mushroom with wine red based on the Internet of things.
Background
The cap mushroom with wine red, also called stropharia rugoso-annulata, is a high-quality edible mushroom with tender meat quality, delicate fragrance, delicious taste and rich nutrition, contains nutritional ingredients such as protein, vitamin, mineral substances, polysaccharide and the like, and becomes one of mushroom species recommended to be cultivated to developing countries by Food and Agricultural Organization (FAO) of the United nations. Researches find that the fungus polysaccharide contained in the wine red cap mushroom also has the effects of relieving mental fatigue of human bodies, helping digestion and preventing coronary heart disease, and is known as 'meat in vegetable' full-value nutritional health-care food, so that the development of the wine red cap mushroom planting industry has important practical significance for improving the dietary structure of people and improving the physique of people.
Environmental conditions such as temperature, humidity, illumination, carbon dioxide concentration and the like are important environmental factors for the growth of the edible fungi, and the cap fungus of the pinocystis vinaceus is a medium-temperature edible fungus. Research shows that when the air temperature is 25 ℃, the relative humidity is 85-90%, the illumination intensity is 100-500LX, and the concentration of carbon dioxide is less than 0.15%, the fruiting body of the stropharia rugosoannulata grows fastest, so that under the greenhouse cultivation environment, certain equipment is utilized to control and adjust the growth environmental parameters of the stropharia rugosoannulata in an ideal state, and the method is the key of industrial edible fungus production. At present, the regulation of traditional domestic fungus big-arch shelter environmental parameter mainly relies on the mode of people's king to realize, and managers need manually go to adjust equipment such as light, book curtain, fan according to crop growth index such as the humiture of artifical record, soil moisture, illuminance, make domestic fungus environmental parameter control in certain scope, and this kind of manual mode is inefficient, and the control accuracy is low and the degree of difficulty is big moreover. In addition, the planting period and the nutrient content of the product of the traditional edible fungus greenhouse cultivation technology need to be improved.
Therefore, how to research and design an ecological high-yield cultivation method and system for the stropharia rugoso-annulata based on the internet of things is a problem which needs to be solved urgently at present, and technical support is improved for greenhouse cultivation of edible fungi with low cost, high yield and high quality and in short period.
Disclosure of Invention
The invention provides an ecological high-yield cultivation method and system for russula vinosa based on the Internet of things, and aims to solve the problems of low control accuracy, high difficulty, long period and poor quality of the existing edible fungus cultivation environment parameters.
The technical purpose of the invention is realized by the following technical scheme:
in a first aspect, an ecological high-yield cultivation method for the stropharia rugoso-annulata based on the Internet of things is provided, and comprises the following steps:
s1: acquiring nutrient components of the sample culture, obtaining an optimal culture node of each nutrient component according to the growth time node through statistical analysis, and calculating to obtain an optimal culture weight value of each nutrient component at the same growth time node;
s2: acquiring physical cultivation data and nutrient content data of the sample cultivation, and training the sample cultivation through an SVM classifier to obtain a parameter control recognition model;
s3: acquiring real-time content data of each nutrient component of the target culture;
s4: adding the real-time content data into a parameter control recognition model to obtain the optimal physical control parameters of each nutrient component;
s5: calculating to obtain the optimal physical control data of the node of the current growth time according to the optimal cultivation weight value and the optimal physical control parameter;
s6: acquiring real-time physical data of a target culture;
s7: and adjusting the growth environment parameters of the target culture according to the real-time physical data and the optimal physical control data to achieve optimal growth.
Preferably, in step S1, the nutrient components of the culture are filtered by PCA principal component analysis and the principal nutrient components are selected at a contribution ratio of not less than.
Preferably, in step S3, the real-time content data is averaged by an indefinite sampling method.
Preferably, in step S7, the growing environment parameters are adjusted at intervals in a plurality of time periods each day, the time period is 3-5h, and the growing environment parameters are cyclically controlled each day according to the optimal distribution of the cultivating nodes of the nutrient components.
Preferably, the real-time physical data includes temperature, humidity, light and carbon dioxide concentration.
Preferably, in step S3, the real-time physical data is counted and analyzed each time, and an alarm signal is sent for an abnormal condition.
In a second aspect, an ecological high-yield cultivation system for the wine red stropharia rugoso-annulata based on the internet of things is provided, which comprises:
the data acquisition module is used for acquiring nutrient components of the sample culture, obtaining an optimal culture node of each nutrient component according to the growth time node through statistical analysis, and calculating to obtain an optimal culture weight value of each nutrient component at the same growth time node;
the model construction module is used for acquiring physical cultivation data and nutrient content data of the sample cultivation, and training the sample cultivation through an SVM classifier to obtain a parameter control recognition model;
the first data acquisition module is used for acquiring real-time content data of each nutrient component of the target culture;
the identification module is used for adding the real-time content data into the parameter control identification model to obtain the optimal physical control parameters of each nutrient component;
the data calculation module is used for calculating to obtain the optimal physical control data of the node of the current growth time according to the optimal cultivation weight value and the optimal physical control parameter;
the second data acquisition module is used for acquiring real-time physical data of the target cultivation;
and the equipment control module is used for adjusting the growth environment parameters of the target culture according to the real-time physical data and the optimal physical control data to achieve optimal growth.
Preferably, the system further comprises a screening module for obtaining main nutrient components after filtering the nutrient components of the culture by PCA (principal component analysis), wherein the main nutrient components are selected with the contribution rate not lower than the contribution rate.
Preferably, the cultivation system also comprises a circulation control module, wherein the circulation control module is used for adjusting the growth environment parameters at intervals in a plurality of time periods every day, the time periods are 3-5h, and the circulation control is carried out on the growth environment parameters every day according to the optimal cultivation node distribution condition of the nutrient components.
Preferably, the system also comprises a data statistics and alarm module which is used for carrying out statistics and analysis on each time of real-time physical data and sending out a warning signal to abnormal conditions.
Compared with the prior art, the invention has the following beneficial effects: the cultivation method has the advantages that growth environment parameters such as temperature, humidity, illumination, carbon dioxide concentration and the like of the target cultivation substance can be reasonably adjusted, so that the target cultivation substance can grow and develop rapidly in a reasonable environment, efficient absorption of nutrient components is promoted, the cultivation period is shortened, the content of each nutrient component of the cultivation substance is improved, the whole automatic operation is realized, the professional technical requirement is low, and the technical support is provided for ecological, high-yield, low-cost and short-period popularization and planting of the wine red stropharia rugoso-annulata.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is an overall flow chart in embodiment 1 of the present invention;
fig. 2 is a functional block diagram in embodiment 2 of the present invention.
In the figure: 1. a data acquisition module; 2. a model building module; 3. a first data acquisition module; 4. an identification module; 5. a data calculation module; 6. a second data acquisition module; 7. a device control module; 8. a screening module; 9. a cycle control module; 10. and a data statistics alarm module.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.
Example (b): an ecological high-yield cultivation method of cap mushrooms with wine red based on the Internet of things is suitable for greenhouse cultivation and industrial cultivation, and comprises the following steps:
s1: and acquiring nutrient components of the sample culture, obtaining the optimal culture node of each nutrient component according to the growth time node through statistical analysis, and calculating to obtain the optimal culture weight value of each nutrient component at the same growth time node. For example: A. b, C the nutrient component A in the culture is most effective in growth and absorption at time a node, the nutrient component B is most effective in growth at time B node, and the nutrient component C is most effective in growth at time C node. Then, the most effective weight ratio of the three nutrients at time node a A, B, C is 0.6:0.3:0.1, the most effective weight ratio of the three nutrients at time node b A, B, C is 0.4:0.4:0.2, and the most effective weight ratio of the three nutrients at time node c A, B, C is 0.2:0.2: 0.6.
After the nutrient components are obtained, the nutrient components of the culture are filtered by a PCA (principal component analysis) method, and then the main nutrient components are obtained by selection, wherein the main nutrient components are selected at a contribution rate not lower than that of the main nutrient components, so that the data operation difficulty can be reduced.
S2: and acquiring physical cultivation data and nutrient content data of the sample cultivation, and training the sample cultivation through an SVM classifier to obtain a parameter control recognition model. Physical cultivation data includes, but is not limited to, temperature, humidity, light, carbon dioxide concentration, air pressure value. The nutrient content data is the ratio of the individual nutrients to the total nutrients.
S3: and acquiring real-time content data of each nutrient component of the target culture. The real-time content data is subjected to average value acquisition by an indefinite-point sampling method, such as a five-point sampling method, sampling is carried out, the average value is obtained, and sampling points are randomly distributed each time. In addition, the real-time physical data of each time are counted and analyzed, warning signals are sent out when abnormal conditions occur, and meanwhile data support can be provided for the cultivation research of the target cultivation.
S4: and adding the real-time content data into the parameter control recognition model to obtain the optimal physical control parameters of each nutrient component. Wherein, the optimal physical control parameter is an environmental condition parameter promoting the most efficient absorption of a certain nutrient component at the current time node.
S5: and calculating to obtain the optimal physical control data of the node of the current growth time according to the optimal cultivation weight value and the optimal physical control parameter. And calculating to obtain the environmental condition parameters with high, balanced and reasonable nutrient content through variance.
S6: real-time physical data of the target culture, including but not limited to temperature, humidity, light and carbon dioxide concentration, is obtained by the biosensor and may be adjusted according to the target culture.
S7: and adjusting the growth environment parameters of the target culture according to the real-time physical data and the optimal physical control data to achieve optimal growth. The environmental parameters in different time periods in each day are different, the growth environmental parameters are adjusted at intervals in a plurality of time periods in each day, the time period is 3-5h, the growth environmental parameters are circularly controlled in each day according to the optimal cultivation node distribution condition of the nutrient components, the energy consumption required by the adjustment of the growth environmental parameters can be effectively reduced, and the data acquisition and processing times are reduced.
Example 2: an ecological high yield cultivation system of red ball lid mushroom based on thing networking includes:
and the data acquisition module 1 is used for acquiring the nutrient components of the sample culture, obtaining the optimal culture node of each nutrient component according to the growth time node through statistical analysis, and calculating to obtain the optimal culture weight value of each nutrient component at the same growth time node. Data acquisition can be counted by big data.
And the model construction module 2 is used for acquiring physical cultivation data and nutrient content data of the sample cultivation, and training the sample cultivation through an SVM classifier to obtain a parameter control recognition model. The method can be realized by installing a central computer in a planting base.
The first data acquisition module 3 is used for acquiring real-time content data of each nutrient component of the target culture.
And the identification module 4 is used for adding the real-time content data into the parameter control identification model to obtain the optimal physical control parameters of each nutrient component.
And the data calculation module 5 is used for calculating to obtain the optimal physical control data of the node of the current growth time according to the optimal cultivation weight value and the optimal physical control parameter.
And the second data acquisition module 6 is used for acquiring real-time physical data of the target cultivation.
And the equipment control module 7 is used for adjusting the growth environment parameters of the target culture according to the real-time physical data and the optimal physical control data to achieve optimal growth. The corresponding control equipment includes but is not limited to LED light, a fan, a temperature control system and dry ice storage equipment.
The system also comprises a screening module 8, which is used for obtaining main nutrient components after filtering the nutrient components of the culture by PCA principal component analysis method, wherein the main nutrient components are selected with the contribution rate not lower than the contribution rate.
The system also comprises a circulation control module 9 which is used for adjusting the growth environment parameters at intervals in a plurality of time periods every day, wherein the time periods are 3-5h, and circularly controlling the growth environment parameters every day according to the optimal cultivation node distribution condition of the nutrient components.
The system also comprises a data statistics and alarm module 10, which is used for carrying out statistics and analysis on real-time physical data each time and sending out a warning signal to abnormal conditions.
The present embodiment is only for explaining the present invention, and it is not limited to the present invention, and those skilled in the art can make modifications of the present embodiment without inventive contribution as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present invention.
Claims (8)
1. An ecological high-yield cultivation method of the stropharia rugoso-annulata based on the Internet of things is characterized by comprising the following steps of:
s1: acquiring nutrient components of the sample culture, obtaining an optimal culture node of each nutrient component according to the growth time node through statistical analysis, and calculating to obtain an optimal culture weight value of each nutrient component at the same growth time node;
s2: acquiring physical cultivation data and nutrient content data of the sample cultivation, and training the sample cultivation through an SVM classifier to obtain a parameter control recognition model;
s3: acquiring real-time content data of each nutrient component of the target culture;
s4: adding the real-time content data into a parameter control recognition model to obtain the optimal physical control parameters of each nutrient component;
s5: calculating to obtain the optimal physical control data of the node of the current growth time according to the optimal cultivation weight value and the optimal physical control parameter;
s6: acquiring real-time physical data of a target culture;
s7: and adjusting the growth environment parameters of the target culture according to the real-time physical data and the optimal physical control data to achieve optimal growth.
2. The Internet of things-based ecological high-yield cultivation method for the pholiota vinosa as claimed in claim 1, wherein in step S3, the real-time content data is averaged by an indefinite-point sampling method.
3. The Internet of things-based ecological high-yield cultivation method for cap mushroom with wine red mushrooms as claimed in claim 1, wherein in step S7, the growth environment parameters are adjusted at intervals in a plurality of time periods each day, the time periods are 3-5h, and the growth environment parameters are cyclically controlled each day according to the optimal cultivation node distribution of nutrient components.
4. The Internet of things-based ecological high-yield cultivation method for the pholiota vinosa, as claimed in claim 1, wherein the real-time physical data include temperature, humidity, illumination and carbon dioxide concentration.
5. The ecological high-yield cultivation method of the stropharia rugoso-annulata based on the internet of things as claimed in claim 1, wherein in step S3, statistics and analysis are carried out on real-time physical data each time, and warning signals are sent out when abnormal conditions occur.
6. The utility model provides an ecological high yield cultivation system of red ball lid mushroom based on thing networking, characterized by includes:
the data acquisition module (1) is used for acquiring nutrient components of the sample culture, obtaining the optimal culture node of each nutrient component according to the growth time node through statistical analysis, and calculating the optimal culture weight value of each nutrient component at the same growth time node;
the model construction module (2) is used for acquiring physical cultivation data and nutrient content data of the sample cultivation, and training the sample cultivation through an SVM classifier to obtain a parameter control recognition model;
the first data acquisition module (3) is used for acquiring real-time content data of each nutrient component of the target culture;
the identification module (4) is used for adding the real-time content data into the parameter control identification model to obtain the optimal physical control parameters of each nutrient component;
the data calculation module (5) is used for calculating to obtain the optimal physical control data of the node of the current growth time according to the optimal cultivation weight value and the optimal physical control parameter;
the second data acquisition module (6) is used for acquiring real-time physical data of the target cultivation;
and the equipment control module (7) is used for adjusting the growth environment parameters of the target cultivation according to the real-time physical data and the optimal physical control data to achieve optimal growth.
7. The Internet of things-based ecological high-yield cultivation system for the pholiota vinicola, as claimed in claim 6, further comprising a cycle control module (9) for adjusting growth environment parameters at intervals in a plurality of time periods each day, wherein the time periods are 3-5h, and controlling the growth environment parameters in a cycle each day according to the optimal cultivation node distribution conditions of nutrient components.
8. The ecological high-yield cultivation system of the stropharia rugoso-annulata based on the internet of things as claimed in claim 6, characterized by further comprising a data statistics and alarm module (10) for performing statistics and analysis on real-time physical data each time and sending out a warning signal for abnormal conditions.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010642597.5A CN111670748B (en) | 2020-07-06 | 2020-07-06 | Internet of things-based ecological high-yield cultivation method and system for stropharia rugoso-annulata |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010642597.5A CN111670748B (en) | 2020-07-06 | 2020-07-06 | Internet of things-based ecological high-yield cultivation method and system for stropharia rugoso-annulata |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111670748A CN111670748A (en) | 2020-09-18 |
| CN111670748B true CN111670748B (en) | 2021-08-06 |
Family
ID=72457126
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010642597.5A Active CN111670748B (en) | 2020-07-06 | 2020-07-06 | Internet of things-based ecological high-yield cultivation method and system for stropharia rugoso-annulata |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111670748B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113692912A (en) * | 2021-09-14 | 2021-11-26 | 闽南理工学院 | Russule planting management method and system |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001265854A (en) * | 2000-03-23 | 2001-09-28 | Takechi Hidehiro | System for raising and supplying vegetable and fruit |
| CN102508470A (en) * | 2011-10-19 | 2012-06-20 | 山东正汉生物科技集团有限公司 | Edible mushroom factory monitoring system |
| CN202758223U (en) * | 2012-08-15 | 2013-02-27 | 福建师范大学 | Intelligent control device based on Internet of things for edible-mushroom culturing room |
| CN104920114A (en) * | 2015-04-03 | 2015-09-23 | 皖西学院 | Dendrobe seedling environment contrast experiment system based on Internet of things |
| KR20160147437A (en) * | 2015-06-15 | 2016-12-23 | 주식회사 엔씽 | Plant sensor using internet of thing |
| CN106576879A (en) * | 2015-10-16 | 2017-04-26 | 谢延山 | Grifola frondosus planting system based on Internet of Things |
| CN106718363A (en) * | 2017-01-06 | 2017-05-31 | 安徽农业大学 | A kind of irrigation tests method and its test platform towards precision agriculture |
| CN108739074A (en) * | 2018-08-10 | 2018-11-06 | 安徽泓森生物科技有限公司 | A kind of mushroom automation cultivation system based on Internet of Things |
| CN110839519A (en) * | 2019-12-02 | 2020-02-28 | 成都信息工程大学 | Internet of things intelligent agricultural irrigation device and method based on deep learning |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8694454B2 (en) * | 2011-02-17 | 2014-04-08 | Superior Edge, Inc. | Methods, apparatus and systems for generating, updating and executing a vegetation control plan |
-
2020
- 2020-07-06 CN CN202010642597.5A patent/CN111670748B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001265854A (en) * | 2000-03-23 | 2001-09-28 | Takechi Hidehiro | System for raising and supplying vegetable and fruit |
| CN102508470A (en) * | 2011-10-19 | 2012-06-20 | 山东正汉生物科技集团有限公司 | Edible mushroom factory monitoring system |
| CN202758223U (en) * | 2012-08-15 | 2013-02-27 | 福建师范大学 | Intelligent control device based on Internet of things for edible-mushroom culturing room |
| CN104920114A (en) * | 2015-04-03 | 2015-09-23 | 皖西学院 | Dendrobe seedling environment contrast experiment system based on Internet of things |
| KR20160147437A (en) * | 2015-06-15 | 2016-12-23 | 주식회사 엔씽 | Plant sensor using internet of thing |
| CN106576879A (en) * | 2015-10-16 | 2017-04-26 | 谢延山 | Grifola frondosus planting system based on Internet of Things |
| CN106718363A (en) * | 2017-01-06 | 2017-05-31 | 安徽农业大学 | A kind of irrigation tests method and its test platform towards precision agriculture |
| CN108739074A (en) * | 2018-08-10 | 2018-11-06 | 安徽泓森生物科技有限公司 | A kind of mushroom automation cultivation system based on Internet of Things |
| CN110839519A (en) * | 2019-12-02 | 2020-02-28 | 成都信息工程大学 | Internet of things intelligent agricultural irrigation device and method based on deep learning |
Non-Patent Citations (2)
| Title |
|---|
| 基于物联网的农业产业园环境监测系统的设计;宋俊慷等;《智能计算机与应用》;20190701;第9卷(第4期);第136-139页 * |
| 物联网M2M中基于社区的能耗感知路由策略;郑鑫等;《小型微型计算机系统》;20170415(第4期);第797-802页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111670748A (en) | 2020-09-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Basile et al. | Responses of “Chardonnay” to deficit irrigation applied at different phenological stages: vine growth, must composition, and wine quality | |
| Hasan et al. | Effect of wheat bran supplement with sugarcane bagasse on growth, yield and proximate composition of pink oyster mushroom (Pleurotus djamor) | |
| Li et al. | Hoagland nutrient solution promotes the growth of cucumber seedlings under light-emitting diode light | |
| CN101731072A (en) | Production method of selenium-rich peanuts | |
| Rajapakse | New cultivation technology for paddy straw mushroom (Volvariella volvacea). | |
| CN108575711B (en) | A kind of method of artificial light source plant factor water planting romaine lettuce | |
| CN111639904A (en) | Fruit and vegetable planting management system based on Internet of things | |
| CN109813370A (en) | A kind of wine-growing environmental information intelligent acquisition system | |
| Stewart et al. | Effects of climate change on fruit nutrition | |
| CN111670748B (en) | Internet of things-based ecological high-yield cultivation method and system for stropharia rugoso-annulata | |
| CN104817368B (en) | A kind of preparation method for the biological organic fertilizer that can improve Fuji apple flavor quality | |
| CN111859672A (en) | Intelligent construction method for ecological environment of strawberry and bee composite facility | |
| CN108207381A (en) | A kind of light-supplementing system of dragon fruit growth and the plant growth lamp of use | |
| CN105724211B (en) | A kind of fruit kohlrabi ciltivating process | |
| KR102013799B1 (en) | Cultivation method of mushrooms by supplying nutrient and controlling growth environment to need mushroom growth | |
| Herdenstam et al. | Nature versus machine: a pilot study using a semi-trained culinary panel to perform sensory evaluation of robot-cultivated basil affected by mechanically induced stress | |
| CN104145678A (en) | Cultivation method for promoting growth of transgenic tomatoes and accumulation of astaxanthin | |
| Ochigbo et al. | Screening of soybean varieties for phosphorus use efficiency in nutrient solution | |
| Brenneman et al. | The Edibility & Cultivation of the Oyster Mushroom | |
| Asaduzzaman et al. | Introductory chapter: Quality vegetable production and human health benefits | |
| CN104541905A (en) | Grape cluster bonsai manufacture method | |
| CN103907568A (en) | Large-scale lysiphlebus fabarum artificial breeding method | |
| CN107711468A (en) | Intelligent soilless planting method | |
| Kamal et al. | Mushroom production in Bangladesh: present scenario and potentialities. | |
| Kilifarev et al. | Development of a Microcontroller Based Automated Greenhouse Cultivation System for Mushrooms-Software |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |