JPH0451129B2 - - Google Patents
Info
- Publication number
- JPH0451129B2 JPH0451129B2 JP63129840A JP12984088A JPH0451129B2 JP H0451129 B2 JPH0451129 B2 JP H0451129B2 JP 63129840 A JP63129840 A JP 63129840A JP 12984088 A JP12984088 A JP 12984088A JP H0451129 B2 JPH0451129 B2 JP H0451129B2
- Authority
- JP
- Japan
- Prior art keywords
- temperature
- growth
- days
- plant
- conversion
- 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.)
- Expired - Lifetime
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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
- A01G7/00—Botany in general
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Botany (AREA)
- Ecology (AREA)
- Forests & Forestry (AREA)
- Environmental Sciences (AREA)
- Cultivation Of Plants (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Description
【発明の詳細な説明】
［発明の目的］
（産業上の利用分野）
本発明は植物の開花、出穂、成熟等の生育時期
を簡易にして高精度に予測することのできる実用
性の高い植物生育期予測装置に関する。[Detailed Description of the Invention] [Object of the Invention] (Field of Industrial Application) The present invention provides a highly practical plant that can easily and accurately predict the growth period of a plant, such as flowering, heading, and maturation. Related to a growing season prediction device.
（従来の技術）
植物の開花、出穂、成熟等の生育時期を予測す
ることは、例えば受粉を必要とするリンゴ、ナ
シ、モモ等の果樹栽培において、また開花時にホ
ルモン処理を行なうブドウ栽培等において、その
作業準備や労働力を確保する上で非常に重要な意
味を持つ。また植物の取入れ・出荷時期を把握す
る上でも重要な要素となる。(Prior art) Predicting the growth period of plants, such as flowering, heading, and maturation, is difficult to predict, for example, in the cultivation of fruit trees such as apples, pears, and peaches that require pollination, and in the cultivation of grapes, which require hormone treatment at the time of flowering. , which has a very important meaning in preparing for the work and securing the workforce. It is also an important factor in understanding the timing of plant intake and shipment.
従来、このような植物の生育期予測は、各地域
毎に長年に亙つて収集された気象データと、そこ
での植物の生育記憶データとに基づき、一般的に
は熟練者や専門家による知識と経験とに頼つて各
地域毎にそれぞれ別個に行なわれている。例えば
桜の開花予測に代表されるように、各地域毎にそ
の年の気象データを調べ、更にその地域特有の過
去の生育データを参照して、所謂重回帰法や単回
帰法に従つてその開花時期が予測されている。然
し乍ら、このような開花予測は各地域での特有な
予測式と係数とに基づいてそれぞれ行なわれるも
のである為、例えば瀬戸内等の温暖な地域での予
測式と係数とを東北地方等の寒い地域での予測に
は全く使うことができないと云う問題がある。 Conventionally, such predictions of the growing season of plants have been based on weather data collected over many years in each region and the growth memory data of plants there, and generally rely on the knowledge and knowledge of experts and experts. This is done separately for each region, relying on experience. For example, when predicting the blooming of cherry blossoms, we check the weather data for each region for that year, and then refer to past growth data specific to that region to predict the blooming of cherry blossoms using the so-called multiple regression method or simple regression method. The timing is predicted. However, since such flowering predictions are made based on prediction formulas and coefficients unique to each region, for example, the prediction formulas and coefficients for a warm region such as the Setouchi Inland Sea are different from those for a cold region such as the Tohoku region. The problem is that it cannot be used for regional forecasting at all.
しかもこのような従来の予測法ではその予測精
度が悪く、その年の気象条件が大幅に変化すると
予測自体が困難になる等の問題があつた。 Moreover, such conventional prediction methods have problems such as poor prediction accuracy and difficulty in making predictions if the weather conditions of the year change significantly.
（発明が解決しようとする課題）
このように従来にあつては、植物の生育期予測
はその地域特有のケースバイケースに設定された
予測式と係数とに委ねられているのが実情であ
り、植物の開花、出穂、成熟等の生育時期を簡易
に精度良く予測することが困難であると云う不具
合があつた。(Problem to be solved by the invention) As described above, in the past, the reality is that prediction of the growing season of plants has been left to prediction formulas and coefficients set on a case-by-case basis unique to the region. However, there was a problem in that it was difficult to easily and accurately predict the growth period of plants, such as flowering, heading, and maturation.
本発明はこのような事情を考慮してなされたも
ので、その目的とするところは、地域性に拘るこ
とのない普遍的な予測式を用い、更には一定の地
域範囲においては同一の係数を用いて植物の生育
時期を高精度に予測することのできる簡易な構成
の実用性の高い植物生育期予測装置を提供するこ
とにある。 The present invention was made in consideration of these circumstances, and its purpose is to use a universal prediction formula regardless of regional characteristics, and furthermore, to use the same coefficients within a certain regional range. It is an object of the present invention to provide a highly practical plant growing season prediction device with a simple configuration that can be used to predict the growing season of plants with high accuracy.
［発明の構成］
（課題を解決するための手段）
本発明は植物の生育速度が狭い温度範囲におい
てアレニウスの法則に従うことに着目し、植物の
生育に関する過去の生育データからアレニウスの
法則に従つて植物固有の感温特性を、所定の標
準温度での生育温度変換日数、生育温度係数、
および生育起算日として求め、この感温特性に
従つて植物の生育期予測を行なうことを特徴とし
ている。[Structure of the Invention] (Means for Solving the Problems) The present invention focuses on the fact that the growth rate of plants follows Arrhenius' law in a narrow temperature range, and calculates the growth rate of plants according to Arrhenius' law from past growth data. The unique thermosensitivity characteristics of plants are determined by the number of days of growth temperature conversion at a predetermined standard temperature, the growth temperature coefficient,
The plant's growth period is predicted based on this temperature-sensitive characteristic.
即ち、本発明に係る植物生育予測期装置は、(a)
予め収集されたデータに基づいて求められている
予測対象植物に関する感温特性すなわち標準温度
での生育温度変換日数、生育温度係数、および生
育起算日を、例えば予測対象植物を特定すること
によりそれぞれ設定し、(b)センサ部にて所定の周
期で計測される気温データから日平均気温と日気
温較差を求め、この気温情報に従つて前記標準温
度を基準とするその日の温度変換日数を前記生育
温度係数を用いて求める。そして(c)この温度変換
日数を前記生育起算日から累積して現在までの温
度変換累積日数を求め、(d)この温度変換累積日数
と前記生育温度変換日数とから求められる前記植
物の生育までに要する温度変換予測日数と今後予
測される気温とから前記生育温度係数を用いて前
記植物の生育時期を予測するようにしたことを特
徴とするものである。 That is, the plant growth prediction period device according to the present invention has (a)
For example, by specifying the prediction target plant, the temperature-sensitive characteristics of the prediction target plant, that is, the number of growing temperature conversion days at the standard temperature, the growth temperature coefficient, and the starting date of growth, are determined based on the data collected in advance. (b) Calculate the daily average temperature and daily temperature range from the temperature data measured at a predetermined cycle by the sensor unit, and according to this temperature information, calculate the number of days of temperature conversion for that day using the standard temperature as a reference for the growth period. Determine using the temperature coefficient. and (c) calculate the cumulative number of temperature conversion days by accumulating this number of days of temperature conversion from the growth start date, and (d) until the growth of the plant determined from this cumulative number of temperature conversion days and the number of days of growth temperature conversion. The present invention is characterized in that the growth period of the plant is predicted using the growth temperature coefficient from the predicted number of days of temperature conversion required for the temperature change and the predicted future temperature.
また更に(e)上記温度係数変換累積日数と生育温
度変換日数とから求められる植物の生育までに要
する温度変換予測日数と上記植物の生育予定日と
から、上記植物の育成速度を制御する為の温度を
生育温度係数を用いて推定するようにしたことを
特徴とするものである。 Furthermore, (e) controlling the growth speed of the plant from the predicted number of days required for the plant to grow, which is determined from the cumulative number of days of temperature coefficient conversion and the number of days of growth temperature conversion, and the expected growth date of the plant; The present invention is characterized in that temperature is estimated using a growth temperature coefficient.
（作用）
このように構成された本発明に係る植物生育期
予測装置によれば、植物の生育日数が所定の標準
温度での生育温度変換日数として評価し、実際の
生育日数はその日の気温から生育温度係数に従つ
て温度変換日数に変換されて予測処理に用いられ
る。(Function) According to the plant growing season prediction device according to the present invention configured as described above, the number of growing days of a plant is evaluated as the number of growing temperature conversion days at a predetermined standard temperature, and the actual number of growing days is calculated from the temperature on that day. It is converted into temperature conversion days according to the growth temperature coefficient and used for prediction processing.
そしてリアルタイムで収集される気温データか
ら生育起算日からの温度変換累積日数を求め、こ
の温度変換累積日数と前記生育温度変換日数とに
従つて現在の生育状況が正確に把握される。そし
て、今後の予測気温を与えることにより、生育温
度係数を用いて植物の生育時期が予測され、また
生育予定日を与えることにより上記生育温度係数
を用いて植物の生育速度を制御する為の温度が推
定される。 Then, the cumulative number of days of temperature conversion from the start date of growth is determined from the temperature data collected in real time, and the current growth status is accurately grasped according to this cumulative number of days of temperature conversion and the number of days of growth temperature conversion. Then, by giving the predicted future temperature, the growth period of the plant is predicted using the growth temperature coefficient, and by giving the expected growth date, the temperature to control the growth rate of the plant is calculated using the growth temperature coefficient. is estimated.
従つて気温によつて種々変化する実際の育成日
数を、標準温度における上記温度変換日数を用い
て評価することが可能となるので、地域性に拘る
ことなしに普遍的な予測式を用いることが可能と
なる。また一定の地域範囲内においては高温年と
低温年、或いは山麓部と平野部等の気温条件等に
拘ることなしに植物の生育予測を高精度に行なう
ことが可能となる。しかも気温データを収集しな
がら生育予測を行なうので、その予測値を簡易に
精度良くリアルタイムに求め、植物の栽培計画に
効果的に用いることが可能となる。 Therefore, it is possible to evaluate the actual number of growing days, which vary depending on the temperature, using the above temperature conversion number of days at the standard temperature, so it is possible to use a universal prediction formula regardless of regional characteristics. It becomes possible. Furthermore, within a certain area, it is possible to predict plant growth with high accuracy without being concerned with temperature conditions such as high temperature years and low temperature years, or the foothills and plains. Moreover, since the growth prediction is performed while collecting temperature data, the predicted value can be obtained easily and accurately in real time, and can be effectively used for plant cultivation planning.
（実施例）
以下、図面を参照して本発明の一実施例につき
説明する。(Example) Hereinafter, an example of the present invention will be described with reference to the drawings.
第１図は実施例装置の要部概略構成図であり、
第２図は実施例装置における生育期予測処理の概
念を示す図である。 FIG. 1 is a schematic diagram of the main parts of the embodiment device,
FIG. 2 is a diagram showing the concept of the growing season prediction process in the embodiment device.
第１図においてセンサ部１は、例えば１時間毎
に気温tiを計測して計測気温データを順次温度メ
モリ２に格納すると共に、１日毎にその日の最高
気温、最低気温、平均気温Tiを求めている。 In FIG. 1, the sensor unit 1 measures the temperature ti every hour, for example, and sequentially stores the measured temperature data in the temperature memory 2, and also calculates the maximum temperature, minimum temperature, and average temperature Ti of that day every day. There is.
この日平均気温Tiは
Ti＝24
Σi=1
ti／24
として求められる。また上記最高気温と最低気温
とから、その日の気温較差が
［最高較差］＝［最高気温］−［最低気温］
として前記センサ部１にて求められる。 The daily average temperature Ti is calculated as Ti= 24 Σ i=1 ti/24. Further, from the maximum temperature and minimum temperature, the temperature range for that day is determined by the sensor unit 1 as follows: [highest range] = [highest temperature] - [lowest temperature].
さてプロセツサからなる演算制御部３は、入力
部４からの指示に基づき、プログラムメモリ５に
格納された所定のプログラムに従つて植物の生育
期予測処理を実行する。この生育予測処理は、先
ず前記入力部３から与えられる情報に従つて予測
対象とする植物の種別、およびその品種を特定
し、データベース６からその予測対象植物に関す
る感温特性の情報を求めることから行なわれる。
その後、このデータベース６から求められた植物
の感温特性と前記センサ部１にて求められている
気温データとに従い、以下に説明するように上記
植物の所定の標準温度を基準とする温度変換日数
の概念を導入して現在の植物の生育状況を把握
し、開花、成熟等の生育時期の予測が行なわれ
る。 Now, the arithmetic control unit 3 consisting of a processor executes a plant growing season prediction process in accordance with a predetermined program stored in the program memory 5 based on instructions from the input unit 4. This growth prediction process first specifies the type and variety of the plant to be predicted according to the information provided from the input section 3, and then obtains information on the temperature-sensitive characteristics of the plant to be predicted from the database 6. It is done.
Thereafter, according to the temperature-sensitive characteristics of the plants obtained from this database 6 and the temperature data obtained by the sensor unit 1, the number of days for temperature conversion based on a predetermined standard temperature of the plants is determined as described below. The concept of this is introduced to understand the current growth status of plants and predict the growth period such as flowering and maturation.
このようにして予測された情報が表示部７にて
表示され、また必要に応じてプリンタ部８にてプ
リント出力される。 The information predicted in this way is displayed on the display section 7, and is printed out on the printer section 8 as required.
さて本装置における生育期予測の原理について
説明すると、化学反応や酵素反応と同様に、生物
反応においても狭い温度範囲においてその反応速
度と反応温度との間に定量的な関係があり、植物
の生育速度がアレニウスの法則に従うことに着目
してその予測処理が行なわれるものとなつてい
る。例えばアレニウスの式は
Ｋ＝Aexp（−Ea／RT）
Ea；見掛け上の活性化エネルギ［cal／mol］
ｋ；速度定数［day-1］ Ｔ；絶対温度［deg］
Ａ；定数 Ｒ；気体定数［1.984cal／mol］
として与えられる。例えば『ナシ』についての発
芽から開花までの生育日数について調べてみる
と、絶対温度の逆数に対する生育相対速度（１日
当りの相対的な生育速度）の対数の関係は第３図
に示すようになる。このような第３図に示すアレ
ニウス・プロツトの直線部分における勾配から、
その植物（ここでは『ナシ』）に固有な活性化エ
ネルギEaを求めることができる。 Now, to explain the principle of growth season prediction using this device, just like chemical reactions and enzymatic reactions, even in biological reactions, there is a quantitative relationship between the reaction rate and the reaction temperature within a narrow temperature range. Prediction processing is performed by focusing on the fact that speed follows Arrhenius' law. For example, the Arrhenius equation is K=Aexp(-Ea/RT) Ea: apparent activation energy [cal/mol] k: rate constant [day -1 ] T: absolute temperature [deg] A: constant R: gas constant It is given as [1.984cal/mol]. For example, when examining the number of growing days from germination to flowering for pears, the relationship between the logarithm of the relative growth rate (relative growth rate per day) against the reciprocal of absolute temperature is shown in Figure 3. . From the slope of the straight line part of the Arrhenius plot shown in Figure 3,
The activation energy Ea specific to the plant (here, pear) can be found.
そこで本装置ではこの『ナシ』の開花に示され
るように、開花日数（生育日数）と温度との間に
アレニウスの法則が適用できることを前提とし
て、その植物に固有な３つの感温特性値［生育温
度変換日数、生育速度の温度係数、生育起算日］
を、予め過去の生育データから求めておき、この
感温特性値を用いて生育期予測を行つている。 Therefore, as shown in the flowering of this pear, this device assumes that Arrhenius' law can be applied between the number of flowering days (growing days) and temperature, and calculates the three temperature-sensitive characteristic values unique to the plant [ Growth temperature conversion days, growth rate temperature coefficient, growth start date]
is determined in advance from past growth data, and this temperature-sensitive characteristic value is used to predict the growing season.
尚、生育温度変換日数（DTS）は、自然の温
度条件におかれた植物の生育日数を適切な標準温
度条件におかれたときの生育日数に変換してなる
変換日数である。具体的には『ミカン』は標準温
度20℃におかれたとき67.2日で開花し、『桜』は
標準温度10℃におかれたとき36.7日で開花する等
のデータからなる。このデータは、例えば植物の
生育（出芽、開花、出穂、成熟等）に関する過去
数年（少なくとも10年）のデータについて、任意
の起算日と任意の温度係数Eaとを用いて各年に
おける生育温度変換日数をそれぞれ求め、その平
均を求める等して決定される。 Note that the number of days converted to growth temperature (DTS) is the number of days converted by converting the number of days a plant grows under natural temperature conditions to the number of days it grows when placed under appropriate standard temperature conditions. Specifically, data shows that ``mandarin oranges'' bloom in 67.2 days when kept at a standard temperature of 20℃, and ``cherry blossoms'' bloom in 36.7 days when kept at a standard temperature of 10℃. This data can be used to estimate the growth temperature for each year using an arbitrary starting date and an arbitrary temperature coefficient Ea, for example, regarding data from the past several years (at least 10 years) regarding plant growth (budding, flowering, heading, ripening, etc.). It is determined by calculating the number of conversion days for each and calculating the average.
また生育起算日はその植物が休眠から覚醒して
成長が始まる植物固有の時期である。この生育起
算日は、前述したアレニウスの法則に従い、どの
ような温度条件で休眠から覚醒して生長が始まる
かを示す場合もある。 Furthermore, the start date of growth is the unique time of the plant when it wakes up from dormancy and begins to grow. This growth start date may indicate under what temperature conditions the plant awakens from dormancy and starts growing, according to the Arrhenius law mentioned above.
更に前記生育速度の温度係数（Ea）は、その
植物の生育速度が温度１℃の変化によつて受ける
影響の強さを示すもので、例えば『桜』で0.137、
『ミカン』で0.087等として与えられる。この温度
係数（Ea）は自然温度が１℃高いと、その生育
速度が13.7％（8.7％）速まることを意味してい
る。この温度係数（Ea）は、上述した平均生育
温度変換日数と実際の育成日数との差を種々の起
算日と温度係数（Ea）の条件下でそれぞれ求め、
その差が最小となるときの温度係数（Ea）とし
て求められる。 Furthermore, the temperature coefficient (Ea) of the growth rate indicates how strongly the growth rate of the plant is affected by a 1°C change in temperature; for example, it is 0.137 for "cherry blossoms";
It is given as 0.087 mag in "Mikan". This temperature coefficient (Ea) means that if the natural temperature is 1°C higher, the growth rate will increase by 13.7% (8.7%). This temperature coefficient (Ea) is determined by calculating the difference between the average growth temperature converted days and the actual number of growing days under various conditions of starting date and temperature coefficient (Ea).
It is determined as the temperature coefficient (Ea) when the difference is minimum.
前述したデータベース６には、このような感温
特性値［生育温度変換日数、生育速度の温度係
数、生育起算日］が植物の種別、品種毎にそれぞ
れ格納されている。そして演算制御部３は入力部
４からの指示に従つて指定された植物に関する感
温特性値をこのデータベース６から選択的に求
め、この感温特性と前述したセンサ部１で求めら
れる温度データとに従つてその植物の生育期を予
測する。 The above-mentioned database 6 stores such temperature-sensitive characteristic values [the number of days of growth temperature conversion, the temperature coefficient of growth rate, and the date of starting growth] for each plant type and variety. Then, the arithmetic control unit 3 selectively obtains thermosensitive characteristic values regarding the designated plant from this database 6 in accordance with instructions from the input unit 4, and combines this thermosensitive characteristic with the temperature data obtained by the sensor unit 1 described above. Predict the growing season of the plant accordingly.
さて本装置での生育期予測は次のようにして行
なわれる。この予測処理を第２図の処理手続きを
参照して説明すると、先ず何について生育期予測
を行なうかの植物の特定、およびその品種の特定
が行なわれる（ステツプａ）。この特定は前記入
力部４からの情報入力により行なわれ、例えば
『ナシ』の『幸水』についての開花予測を行なう
等として植物名、およびその品種が特定される。
すると演算制御部３は前記データベース６を検索
し、当該植物に関する前述した感温特性値［生育
温度変換日数、生育速度の温度係数、生育起算
日］を求める（ステツプｂ）。この処理によつて
植物の生育予測に必要な初期データの設定が行な
われる。 Now, the growing season prediction with this device is performed as follows. This prediction process will be explained with reference to the processing procedure shown in FIG. 2. First, the plant for which the growing season is to be predicted and its variety are specified (step a). This specification is performed by inputting information from the input unit 4, and the plant name and its variety are specified, for example, by predicting the flowering of ``Kosui'' of ``Pear''.
Then, the arithmetic control section 3 searches the database 6 and obtains the above-mentioned temperature-sensitive characteristic values [number of days of growth temperature conversion, temperature coefficient of growth rate, and growth start date] regarding the plant (step b). Through this processing, initial data necessary for plant growth prediction is set.
しかる後、演算制御部３は上記初期データに従
つて当該植物の前記生育起算日からの生育特性を
示す生育温度変換日数曲線（DTS曲線）を作成
し、更に前記センサ部１にて常時所定の周期毎に
計測されている自然環境での気温データから前述
した標準温度Tsを基準とする温度変換日数を求
めている（ステツプｃ）。 Thereafter, the arithmetic control unit 3 creates a growth temperature conversion days curve (DTS curve) showing the growth characteristics of the plant from the growth start date according to the above initial data, and also uses the sensor unit 1 to constantly maintain a predetermined temperature. The number of days of temperature conversion based on the standard temperature Ts mentioned above is determined from the temperature data in the natural environment measured every cycle (step c).
この生育温度変換日数は、前述したその日の平
均気温Tiを計算すると共に（ステツプc1）、その
日の気温較差を求め（ステツプc2）、前述した標
準温度Tsに対するその日の温度変換日数（DTS）
をアレニウスの式に基づいて計算することから行
なわれる（ステツプc3）。 This number of growing temperature conversion days is calculated by calculating the average temperature Ti of the day mentioned above (step c1), determining the temperature range of that day (step c2), and calculating the number of days temperature conversion of that day (DTS) for the standard temperature Ts mentioned above.
is calculated based on the Arrhenius equation (step c3).
この日温度変換日数（日DTS）は、具体的に
は
24
Σ
Σi=1
exp［Ea（Ti−Ts）／２／（273＋Ti）／（273＋Ts）
］／24
なる演算を行なうことにより求められる。 Specifically, the number of daily temperature conversion days (days DTS) is 24 Σ Σ i=1 exp[Ea (Ti−Ts)/2/(273+Ti)/(273+Ts)
]/24.
このような日温度変換日数（日DTS）を前述
した生育起算日から累積することによつて、前記
植物の生育状況が温度変換累積日数として求めら
れる（ステツプc4）。このようにして求められた
温度変換累積日数が、前述した生育温度変換日
数、生育速度の温度係数、生育起算日等の情報と
共に前記表示部７にて、例えば第４図に示すよう
に表示される。 By accumulating the number of daily temperature conversion days (days DTS) from the above-mentioned growth start date, the growth status of the plant is determined as the cumulative number of temperature conversion days (step c4). The cumulative number of days of temperature conversion obtained in this manner is displayed on the display section 7, for example, as shown in FIG. Ru.
しかして現在までの温度変換累積日数を前述し
た植物の生育温度変換日数曲線（DTS曲線）に
照し合せることにより、植物の現在の生育状況が
実際の自然環境気温に拘らず高精度に把握するこ
とが可能となり、例えばその開花までどれだけの
温度変換日数を要するか等の生育状況の把握が可
能となる。 However, by comparing the cumulative number of days of temperature conversion to date with the plant growth temperature conversion days curve (DTS curve) described above, the current growth status of plants can be grasped with high accuracy regardless of the actual natural environmental temperature. This makes it possible to grasp the growth status, such as how many days it takes to change the temperature until flowering.
予測処理（ステツプｄ）はこのような温度変換
累積日数と生育温度変換日数曲線（DTS曲線）
とから、温度変換日数で植物の生育（開花、成熟
等）までにどれだけの日数が掛るか等を求め、例
えば今後予測される自然環境の温度に照し合せる
等して前述した温度係数に従うアレニウスの式に
基づく計算処理から、上記植物の生育時期が何時
頃になるかを予測するものである。また或いは植
物の生育予定日が定められた場合には、どのよう
な温度であれば植物の生育をその予定日に合せ得
るかを予測するものである。 The prediction process (step d) is based on the cumulative number of days of temperature conversion and growth temperature conversion number of days curve (DTS curve).
From this, find out how many days it will take for the plant to grow (flowering, maturing, etc.) using the number of days of temperature conversion, and then follow the temperature coefficient mentioned above, for example by comparing it with the temperature of the natural environment predicted in the future. This method predicts when the plants will grow from a calculation process based on the Arrhenius equation. Alternatively, when a scheduled growth date for a plant is determined, it is possible to predict what temperature will allow the plant to grow on that scheduled date.
このようにして予測された情報が前記表示部７
にて表示される（ステツプｅ）。つまり植物の成
長が或る温度範囲においてアレニウスの法則に従
うことを前提とし、自然環境での実際の生育日数
を或る標準温度を基準とする温度変換日数に置換
えて植物の生育状況を把握し、且つ上記温度変換
日数の下でその生育の過程を予測して開花や成熟
等の時期等が予測され、その予測値が提示される
ものとなつている。 The information predicted in this way is displayed on the display section 7.
(step e). In other words, it is assumed that plant growth follows Arrhenius' law within a certain temperature range, and the growth status of plants is understood by replacing the actual number of growing days in a natural environment with the number of days converted to temperature based on a certain standard temperature. In addition, the growth process is predicted under the above-mentioned number of days of temperature conversion, the timing of flowering, ripening, etc. is predicted, and the predicted value is presented.
従つて本装置によれば、或る適正な標準温度で
の温度変換日数として植物の生育を捕え、実際の
自然環境での植物の生育を上記温度変換日数にて
評価して現在までの生育の状況を正確に把握し、
更には今後の生育の過程を予測するので、自然環
境の変化に左右されることなく、その生育予測を
高精度に行なうことが可能となる。また上述した
温度変換日数を用いて生育状況の予測を行なうの
で、温暖地方であるか寒冷地方であるかの地域性
に影響されることなしに、同一の予測アルゴリズ
ム（予測式）の下で、その予測処理を高精度に行
なうことが可能となる。 Therefore, according to this device, the growth of plants is captured as the number of days of temperature conversion at a certain appropriate standard temperature, and the growth of plants in the actual natural environment is evaluated using the number of days of temperature conversion, and the growth to date can be determined. Understand the situation accurately,
Furthermore, since the future growth process is predicted, it is possible to predict the growth with high accuracy without being influenced by changes in the natural environment. In addition, since the growth status is predicted using the number of days of temperature conversion mentioned above, the growth status can be predicted using the same prediction algorithm (prediction formula) without being influenced by regional characteristics such as whether it is a warm region or a cold region. It becomes possible to perform the prediction process with high accuracy.
故に植物の開花時期を高精度に予測してその受
粉準備やホルモン処理準備を行なつたり、その栽
培作業の為の労働力確保の準備を適確に行なうこ
とが可能となる。また成熟時期を予測して取入れ
出荷時期を正確に把握すること等が可能となる。 Therefore, it is possible to predict the flowering time of a plant with high accuracy, prepare for pollination and hormone treatment, and accurately prepare for securing labor for the cultivation work. It also becomes possible to predict the maturity period and accurately grasp the timing of intake and shipment.
また冷暖房機器を併用して植物に与える気温条
件を制御可能な場合には、前述したように植物の
生育予定日から、その予定日に植物の生育を併せ
るに必要な温度を求め、この温度に従つて植物の
生育を制御（調整）することが可能となる。従つ
てこの予測制御により、植物の生産調整を行なう
ことが可能となる等の効果が奏せられる。 In addition, if it is possible to control the temperature conditions given to plants by using air conditioning equipment in conjunction with heating and cooling equipment, as mentioned above, from the scheduled growth date of the plants, calculate the temperature required for the plants to grow on that scheduled date, and adjust the temperature to this temperature. Therefore, it becomes possible to control (adjust) the growth of plants. Therefore, this predictive control provides effects such as making it possible to adjust the production of plants.
尚、本発明は上述した実施例に限定されるもの
ではない。例えばデータベース６に種々の植物の
温度特性値の全てを登録しておく必要はなく、植
物の種別や品種毎にその感温特性値を記憶した複
数のメモリカードとしてデータベース６を実現す
るようにしても良い。この場合には、予測対象植
物に対応したメモリカードのセツトによつてその
植物の特定が行なわれ、その感温特性値の設定が
行なわれることになる。またデータベース６を外
部機器として実現し、必要に応じて外部機器から
前記演算制御部３に必要な情報を設定入力するよ
うにすることも可能である。更には予測情報等の
表示の形態も限定されるものではなく、前述した
アレニウス・プロツトをプリント出力するように
することも可能である。その他、本発明はその要
旨を逸脱しない範囲で種々変形して実施すること
が可能である。 Note that the present invention is not limited to the embodiments described above. For example, it is not necessary to register all the temperature characteristic values of various plants in the database 6, but the database 6 can be realized as a plurality of memory cards that store the temperature-sensitive characteristic values for each type and variety of plants. Also good. In this case, the plant is specified by setting a memory card corresponding to the plant to be predicted, and its temperature-sensitive characteristic value is set. It is also possible to realize the database 6 as an external device, and set and input necessary information to the arithmetic control section 3 from the external device as necessary. Furthermore, the form of display of prediction information, etc. is not limited, and it is also possible to print out the above-mentioned Arrhenius plot. In addition, the present invention can be implemented with various modifications without departing from the gist thereof.
［発明の効果］
本発明によれば次のような作用効果を期待でき
る。[Effects of the Invention] According to the present invention, the following effects can be expected.
本発明によれば、植物の生育が所定の標準温度
での生育温度変換日数として評価される。そして
実際の生育日数は、その日の気温から生育温度係
数に従つて上記温度変換日数に変換されて予測処
理される。したがつて自然環境の変化や地域性等
の如何に拘らず、簡易にかつ高精度に植物の生育
状況を把握でき、その生育時期を予測することが
できる。 According to the present invention, plant growth is evaluated as the number of growing temperature conversion days at a predetermined standard temperature. Then, the actual number of growing days is predicted by converting the temperature of that day into the number of temperature-converted days according to the growing temperature coefficient. Therefore, regardless of changes in the natural environment, regional characteristics, etc., the growth status of plants can be easily and accurately grasped, and the growing season can be predicted.
すなわち本発明の装置を、生育予測をしようと
する予測対象植物の生育現場に設置すると、この
装置は温度に感じる一種の「植物体内時計」とし
ての機能（温度変換日数）を発揮する。これによ
つて生育ステージが経時的に分かるので、目的と
する生育時期を予測することができる。 That is, when the device of the present invention is installed at a growing site of a plant whose growth is to be predicted, the device functions as a kind of "plant internal clock" that senses temperature (temperature conversion days). This allows the growth stage to be known over time, making it possible to predict the desired growth period.
この結果、植物の栽培管理・調整に効果的に利
用することができ、実用上きわめて多大な効果を
奏する。 As a result, it can be effectively used for the cultivation management and adjustment of plants, and has extremely great practical effects.
図は本発明に係る植物生育期予測装置の一実施
例につき示すもので、第１図は実施例装置の要部
概略構成図、第２図は実施例装置における概略的
な処理手続きの流れを示す図、第３図はナシの生
育に関するアレニウス・プロツトを示す図、第４
図は予測結果の表示例を示す図である。
１……センサ部、２……温度メモリ、３……演
算制御部、４……入力部、５……プログラムメモ
リ、６……データベース（温度特性値）、７……
表示部、８……プリンタ部。
The figures show one embodiment of the plant growing season prediction device according to the present invention. FIG. 1 is a schematic diagram of the main parts of the device, and FIG. 2 is a schematic diagram of the flow of processing procedures in the device. Figure 3 is a diagram showing Arrhenius plot regarding pear growth, Figure 4 is a diagram showing the Arrhenius plot regarding pear growth.
The figure is a diagram showing a display example of prediction results. DESCRIPTION OF SYMBOLS 1... Sensor section, 2... Temperature memory, 3... Arithmetic control section, 4... Input section, 5... Program memory, 6... Database (temperature characteristic value), 7...
Display section, 8...Printer section.
Claims (1)
いる予測対象植物に関する感温特性として所定の
標準温度での生育温度変換日数、生育温度係数、
および生育起算日をそれぞれ設定する手段と、所
定の周期で計測される気温データから日平均気温
と日気温較差を求めるセンサ部と、このセンサ部
で求められた気温情報に従つて前記標準温度を基
準とするその日の温度変換日数を前記生育温度係
数を用いて求める手段と、この温度変換日数を前
記生育起算日から累積して現在までの温度変換累
積日数を求める手段と、この温度変換累積日数と
前記生育温度変換日数とから求められる前記植物
の生育までに要する温度変換予測日数と今後予測
される気温とから前記生育温度係数を用いて前記
植物の生育時期を予測する手段とを具備したこと
を特徴とする植物生育期予測装置。 ２ 請求項第１項に記載の植物生育期予測装置に
おいて、温度変換累積日数と生育温度変換日数と
から求められる植物の生育までに要する温度変換
予測日数と上記植物の生育予定日とから、上記植
物の育成速度を制御する為の温度を生育温度係数
を用いて推定する手段を備えたことを特徴とする
植物生育期予測装置。[Claims] 1. Temperature-sensitive characteristics of the predicted target plant determined based on data collected in advance include the number of days of growth temperature conversion at a predetermined standard temperature, the growth temperature coefficient,
and a means for setting the growth start date, a sensor unit that calculates the daily average temperature and daily temperature range from temperature data measured at a predetermined cycle, and a sensor unit that calculates the standard temperature according to the temperature information determined by the sensor unit. means for determining the number of days of temperature conversion for that day as a standard using the growth temperature coefficient; means for calculating the cumulative number of days of temperature conversion up to the present by accumulating the number of days of temperature conversion from the growth start date; and the cumulative number of days of temperature conversion. and means for predicting the growth period of the plant using the growth temperature coefficient from the predicted number of days of temperature conversion required for the plant to grow, which is determined from the number of days of conversion of the growth temperature, and the predicted future temperature. A plant growth season prediction device characterized by: 2. In the plant growing season prediction device according to claim 1, the predicted growth date of the plant is determined based on the predicted number of days required for the plant to grow, which is determined from the cumulative number of temperature conversion days and the number of growing temperature conversion days, and the expected growth date of the plant. A plant growing season prediction device comprising means for estimating a temperature for controlling the growth rate of plants using a growth temperature coefficient.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP63129840A JPH01300830A (en) | 1988-05-27 | 1988-05-27 | Predictor for plant growth period |
| NL8900624A NL193574C (en) | 1988-05-27 | 1989-03-15 | Apparatus and method for predicting plant growth. |
| DE3909525A DE3909525A1 (en) | 1988-05-27 | 1989-03-22 | Method and device for forecasting plant development |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP63129840A JPH01300830A (en) | 1988-05-27 | 1988-05-27 | Predictor for plant growth period |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JPH01300830A JPH01300830A (en) | 1989-12-05 |
| JPH0451129B2 true JPH0451129B2 (en) | 1992-08-18 |
Family
ID=15019537
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP63129840A Granted JPH01300830A (en) | 1988-05-27 | 1988-05-27 | Predictor for plant growth period |
Country Status (3)
| Country | Link |
|---|---|
| JP (1) | JPH01300830A (en) |
| DE (1) | DE3909525A1 (en) |
| NL (1) | NL193574C (en) |
Families Citing this family (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE4309594A1 (en) * | 1993-03-22 | 1994-09-29 | Ueberschaer Hans Joerg | Monitoring device |
| US5572646A (en) * | 1993-08-25 | 1996-11-05 | Casio Computer Co., Ltd. | Apparatus for displaying images of living things to show growing and/or moving of the living things |
| DE19624018A1 (en) * | 1996-06-17 | 1997-12-18 | Claas Ohg | Process for providing agricultural meteorological information |
| FR2834100B1 (en) * | 2001-12-28 | 2004-03-12 | Meteo France | METHOD FOR PROVIDING A METEOROLOGICAL INDEX |
| DE102010000236A1 (en) | 2010-01-28 | 2011-09-15 | Amazonen-Werke H. Dreyer Gmbh & Co. Kg | Method for providing information about plant treatment measures e.g. forecasts, for farmer, involves transmitting application instructions to computer terminal of farmer prior to occurrence of possible damage |
| JP5874240B2 (en) * | 2011-08-22 | 2016-03-02 | 富士通株式会社 | Information processing apparatus, harvest time prediction program, and harvest time prediction method |
| CN109858678B (en) * | 2018-12-29 | 2023-04-25 | 航天信息股份有限公司 | Method and system for determining meteorological yield of sunflowers |
| CN110150078B (en) * | 2019-05-27 | 2021-04-30 | 福建中烟工业有限责任公司 | Method and system for determining tobacco transplanting date in Fujian tobacco district |
| CN110245444B (en) * | 2019-06-21 | 2020-09-04 | 中国气象科学研究院 | Development period simulation method based on response and adaptation mechanism of crops to environment |
| CN110264018A (en) * | 2019-07-09 | 2019-09-20 | 北京兴农丰华科技有限公司 | A kind of During Growing Period of Winter Wheat prediction technique based on effective accumulated temperature over the years |
| CN110514248B (en) * | 2019-09-24 | 2020-07-14 | 上海联适导航技术有限公司 | An intelligent and automated agricultural data collection system based on big data |
| CN113988376B (en) * | 2021-09-29 | 2023-08-29 | 南京物链云农业科技有限公司 | Rice growth period prediction method, system and device |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AT286023B (en) * | 1968-10-07 | 1970-11-25 | Ruthner Othmar | Process for defining growth processes for plants |
| JPS5820128A (en) * | 1981-07-29 | 1983-02-05 | 株式会社日立製作所 | Environmental control method for greenhouse horticulture |
| FR2572821B1 (en) * | 1984-11-07 | 1992-07-31 | Henry Philippe | METHOD AND APPARATUS FOR THE ANALYSIS OF METEOROLOGICAL VARIABLES APPLICABLE IN PARTICULAR TO THE OPTIMIZATION OF AGRICULTURAL PRODUCTIONS |
| US4755942A (en) * | 1985-05-17 | 1988-07-05 | The Standard Oil Company | System for indicating water stress in crops which inhibits data collection if solar insolation exceeds a range from an initial measured value |
-
1988
- 1988-05-27 JP JP63129840A patent/JPH01300830A/en active Granted
-
1989
- 1989-03-15 NL NL8900624A patent/NL193574C/en not_active IP Right Cessation
- 1989-03-22 DE DE3909525A patent/DE3909525A1/en active Granted
Also Published As
| Publication number | Publication date |
|---|---|
| DE3909525C2 (en) | 1993-06-09 |
| JPH01300830A (en) | 1989-12-05 |
| NL193574B (en) | 1999-10-01 |
| NL193574C (en) | 2000-02-02 |
| DE3909525A1 (en) | 1989-11-30 |
| NL8900624A (en) | 1989-12-18 |
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