TWI815111B - Rice panicle initiation stage estimation method and system thereof - Google Patents
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- 235000007164 Oryza sativa Nutrition 0.000 title claims abstract description 131
- 235000009566 rice Nutrition 0.000 title claims abstract description 131
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- 230000000977 initiatory effect Effects 0.000 title abstract description 3
- 240000007594 Oryza sativa Species 0.000 title description 2
- 241000209094 Oryza Species 0.000 claims abstract description 199
- 238000013178 mathematical model Methods 0.000 claims abstract description 14
- 230000004069 differentiation Effects 0.000 claims description 105
- 230000003595 spectral effect Effects 0.000 claims description 72
- 238000000611 regression analysis Methods 0.000 claims description 30
- 238000001228 spectrum Methods 0.000 abstract description 14
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- 238000001514 detection method Methods 0.000 description 9
- 238000004364 calculation method Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 241000196324 Embryophyta Species 0.000 description 3
- 210000005069 ears Anatomy 0.000 description 3
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- 238000004140 cleaning Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
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Abstract
Description
本發明係關於一種水稻幼穗分化期〔panicle initiation stage，PI stage〕之估計〔estimation〕方法、其系統及其管理系統；特別是關於一種利用反射光譜進行水稻幼穗分化期之估計方法、其系統及其管理系統；更特別是關於一種利用反射光譜進行轉換成〔conversion〕植生指數〔vegetation index〕進行水稻幼穗分化期之估計方法、其系統及其管理系統。 The present invention relates to a method for estimating the panicle initiation stage (PI stage) of rice, its system and its management system; in particular, it relates to a method for estimating the differentiation stage of rice panicle using reflection spectrum, and its management system. The system and its management system; more particularly, it relates to a method for estimating the differentiation stage of rice spikelets by converting the reflection spectrum into a [conversion] vegetation index, its system and its management system.
有關習用穗長自動測量技術，例如：中國專利公告第CN-101929843號〝水稻穗長的自動測量裝置及測量方法〞之發明專利，其揭示一種水稻穗長的自動測量裝置及測量方法。該水稻穗長的自動測量裝置包含一機架、一樣品放置板、一細節相機、一全景相機、一前景光源、一背景光源、一圖像採集卡、一電腦及一電源。 Regarding conventional automatic ear length measurement technology, for example, the invention patent No. CN-101929843 "Automatic Measuring Device and Measuring Method for Rice Ear Length" discloses an automatic measuring device and method for measuring rice ear length. The automatic measuring device for rice ear length includes a frame, a sample placement plate, a detail camera, a panoramic camera, a foreground light source, a background light source, an image acquisition card, a computer and a power supply.
承上，前述專利公告第CN-101929843號之該樣品放置板安裝在該機架內，且該樣品放置板用於放置一稻穗莖杆。該細節相機及全景相機分別利用該機架安裝在該樣品放置板之上方，而該細節相機用於拍攝該稻穗莖杆部分的一細節圖像，且該全景相機用於拍攝該稻穗的全景圖像。 Following the above, the sample placing plate of the aforementioned patent publication No. CN-101929843 is installed in the frame, and the sample placing plate is used to place a rice stem. The detail camera and the panoramic camera are respectively installed above the sample placement plate using the frame, and the detail camera is used to take a detailed image of the stem part of the rice ear, and the panoramic camera is used to take pictures of the rice ear. panoramic image.
承上，前述專利公告第CN-101929843號之該細節相機及全景相機分別利用該圖像採集卡相連於該電腦，而該前景光源安裝在該樣品放置板之上方，且該前景 光源用於為該細節相機提供一前景照明。該背景光源安裝在該樣品放置板之下方，且該背景光源用於為該全景相機提供一背景照明。該電腦接收該圖像採集卡所輸入的圖像，並進行影像處理，以便測量出該稻穗之長度。該電源為該前景光源、背景光源及電腦進行供電。 Following on from the above, the detail camera and panoramic camera of the aforementioned patent publication No. CN-101929843 are respectively connected to the computer using the image acquisition card, and the foreground light source is installed above the sample placement plate, and the foreground The light source is used to provide a foreground lighting for the detail camera. The background light source is installed below the sample placement plate, and the background light source is used to provide background illumination for the panoramic camera. The computer receives the image input by the image capture card and performs image processing to measure the length of the rice ear. The power supply supplies power to the foreground light source, background light source and computer.
然而，前述專利公告第CN-101929843號並未進一步揭示如何以適當技術手段〔例如：植生指數或其植生相關資料之應用〕進行計算水稻幼穗分化期〔或其相關如何應用水稻幼穗分化期〕之水稻栽培管理技術。 However, the aforementioned Patent Publication No. CN-101929843 does not further disclose how to calculate the rice panicle differentiation period [or how to apply the rice panicle differentiation period] using appropriate technical means (such as the application of vegetation index or its vegetation-related data). ) Rice cultivation and management technology.
另一習用水稻稻穗檢測技術，例如：中國專利公開第CN-111462058號〝一種水稻有效穗快速檢測方法〞之發明專利申請案，其揭示一種水稻有效穗快速檢測方法。該水稻有效穗快速檢測方法通過水稻成熟期的水稻有效分蘖數進行取得水稻有效穗數。 Another common rice ear detection technology, for example: the invention patent application of Chinese Patent Publication No. CN-111462058 "A rapid detection method for effective rice ears", which discloses a rapid detection method for effective rice ears. This method for rapid detection of effective rice panicles obtains the number of effective rice panicles based on the number of effective tillers of rice during the rice maturity stage.
承上，前述專利公開第CN-111462058號之該水稻有效穗快速檢測方法包含：步驟A、採集水稻稻茬圖像；及步驟B、利用深度卷積神經網路方法檢測水稻的有效分蘖並計算水稻有效穗數。 Following on from the above, the method for rapid detection of effective rice panicles in the aforementioned patent publication No. CN-111462058 includes: step A, collecting rice stubble images; and step B, using a deep convolutional neural network method to detect and calculate effective tillers of rice. Effective number of rice ears.
承上，前述專利公開第CN-111462058號之步驟A包含步驟：A1、將整株成熟期的水稻割下，保留茬高度為2-3釐米；A2、將割下的成熟期水稻移開，並遠離稻茬位置；A3、利用移動採集設備拍下整株稻茬樣本圖像，且距離地面的拍照高度為13-20釐米；A4、對採集完的稻茬樣本圖像進行圖像預處理操作，且該圖像預處理操作包含資料清洗及資料格式轉換。 Following the above, step A of the aforementioned patent publication No. CN-111462058 includes the following steps: A1. Cut off the entire mature rice plant, leaving the stubble height at 2-3 cm; A2. Remove the cut mature rice, And stay away from the rice stubble position; A3. Use a mobile collection device to take a picture of the entire rice stubble sample, and the photo height from the ground is 13-20 cm; A4. Perform image preprocessing on the collected rice stubble sample image Operation, and the image preprocessing operation includes data cleaning and data format conversion.
承上，前述專利公開第CN-111462058號之步驟B包含步驟：B1、利用LabelImg圖像標注軟體標注稻茬樣本圖像，並建立水稻稻茬圖像基礎資料庫，且該基礎資料庫包含訓練集、測試集及驗證集；B2、為了易於檢測 目標，利用三種不同縱橫比和三種不同尺寸的錨框進行測試，最後獲得縱橫比為1：1的錨框是最優錨框；B3、為了獲得最優的水稻有效分蘖檢測模型，在其他參數保持一致的前提下，採用三種不同的骨幹絡並以兩種不同的訓練方式對水稻稻茬圖像進行訓練，且總共獲得六種模型；B4：自六種模型中選擇最優的水稻有效分蘖快速檢測模型，並進行參數優化，以獲得最終的水稻有效分蘖快速檢測模型。 Following on from the above, step B of the aforementioned patent publication No. CN-111462058 includes the steps: B1. Use LabelImg image annotation software to label rice stubble sample images, and establish a rice stubble image basic database, and the basic database includes training Set, test set and verification set; B2, for easy detection Target, use three different aspect ratios and three different sizes of anchor frames for testing, and finally obtain an anchor frame with an aspect ratio of 1:1 as the optimal anchor frame; B3. In order to obtain the optimal rice effective tiller detection model, in other parameters On the premise of maintaining consistency, three different backbone networks were used to train rice stubble images in two different training methods, and a total of six models were obtained; B4: Select the optimal effective rice tiller from the six models. Rapidly detect the model and perform parameter optimization to obtain the final rapid detection model of effective rice tillers.
然而，前述專利公開第CN-111462058號同樣的並未進一步揭示如何以適當技術手段〔例如：植生指數或其植生相關資料之應用〕進行計算水稻幼穗分化期〔或其相關如何應用水稻幼穗分化期〕之水稻栽培管理技術。 However, the aforementioned patent publication No. CN-111462058 also does not further disclose how to calculate the rice panicle differentiation period [or how to apply the rice panicle differentiation period] using appropriate technical means [such as the application of vegetation index or its vegetation-related data]. Rice cultivation and management techniques during the differentiation period.
顯然，前述中國專利公告第CN-101929843號之水稻穗長的自動測量裝置及其方法及中國專利公開第CN-111462058號之水稻有效穗快速檢測方法仍存在進一步改良之需求，以便提供準確計算水稻幼穗分化期及精準進行水稻幼穗分化期管理〔例如：施肥或其它作業〕。 Obviously, there is still a need for further improvement in the automatic measuring device and method for rice panicle length in the aforementioned Chinese Patent Publication No. CN-101929843 and the rapid detection method of rice effective panicle in Chinese Patent Publication No. CN-111462058, in order to provide accurate calculation of rice panicle length. Young panicle differentiation period and precise management of rice young panicle differentiation period (for example: fertilization or other operations).
簡言之，前述中國專利公告第CN-101929843號之發明專利及中國專利公開第CN-111462058號之發明專利申請案之揭露僅為本發明技術背景之參考及說明目前技術發展狀態而已，其並非用以限制本發明之範圍。 In short, the aforementioned disclosures of the invention patent No. CN-101929843 and the invention patent application No. CN-111462058 are only for reference and explanation of the technical background of the present invention and the current state of technological development. They are not used to limit the scope of the present invention.
有鑑於此，本發明為了滿足上述技術問題及需求，其提供一種水稻幼穗分化期之估計方法、其系統及其管理系統，其於一預定期間利用一光譜儀單元選擇一水稻植株進行攝取一水稻植株光譜影像資料，並自該水稻植株光譜影像資料擷取至少一波長區間光譜資料，且將該波長區間光譜資料進行轉換成一植被指數資料，且利用該植被指數資料於一積溫數學模型對應計算一水稻植株積溫資料，以便獲得一幼穗分化判斷值資料，且利用該水稻植株積溫資料及幼穗分化判斷值資料進行比對而獲得至少一預 測幼穗分化積溫資料，因此相對於習用水稻栽培管理系統可達成提升預測水稻幼穗分化期之準確率之目的。 In view of this, in order to meet the above technical problems and needs, the present invention provides a method for estimating the differentiation stage of rice spikelets, its system and its management system, which uses a spectrometer unit to select a rice plant to ingest a rice plant during a predetermined period. Plant spectral image data, and extract at least one wavelength interval spectral data from the rice plant spectral image data, and convert the wavelength interval spectral data into a vegetation index data, and use the vegetation index data to calculate a corresponding calculation in an accumulated temperature mathematical model. The rice plant accumulated temperature data is used to obtain a young panicle differentiation judgment value data, and the rice plant accumulated temperature data and the young panicle differentiation judgment value data are compared to obtain at least one predetermined value. By measuring the accumulated temperature data of young panicle differentiation, it can achieve the purpose of improving the accuracy of predicting the differentiation stage of rice young panicle compared with conventional rice cultivation and management systems.
本發明之主要目的係提供一種水稻幼穗分化期之估計方法、其系統及其管理系統，其於一預定期間利用一光譜儀單元選擇一水稻植株進行攝取一水稻植株光譜影像資料，並自該水稻植株光譜影像資料擷取至少一波長區間光譜資料，且將該波長區間光譜資料進行轉換成一植被指數資料，且利用該植被指數資料於一積溫數學模型對應計算一水稻植株積溫資料，以便獲得一幼穗分化判斷值資料，且利用該水稻植株積溫資料及幼穗分化判斷值資料進行比對而獲得至少一預測幼穗分化積溫資料，以便達成提升預測水稻幼穗分化期之準確率之目的及功效。 The main purpose of the present invention is to provide a method for estimating the differentiation stage of rice spikelets, its system and its management system, which utilizes a spectrometer unit to select a rice plant during a predetermined period to acquire spectral image data of a rice plant and obtain the spectral image data from the rice plant. The plant spectral image data captures at least one wavelength interval spectral data, and converts the wavelength interval spectral data into a vegetation index data, and uses the vegetation index data to correspond to an accumulated temperature mathematical model to calculate a rice plant accumulated temperature data, so as to obtain a young rice plant. The panicle differentiation judgment value data is obtained, and the accumulated temperature data of the rice plant and the young panicle differentiation judgment value data are compared to obtain at least one predicted young panicle differentiation accumulated temperature data, in order to achieve the purpose and effect of improving the accuracy of predicting the young panicle differentiation stage of rice. .
為了達成上述目的，本發明較佳實施例之水稻幼穗分化期之估計方法包含： In order to achieve the above object, the method for estimating the differentiation stage of rice spikelets according to the preferred embodiment of the present invention includes:
於一預定期間利用一光譜儀單元選擇一水稻植株進行攝取一水稻植株光譜影像資料； Using a spectrometer unit to select a rice plant during a predetermined period to acquire spectral image data of a rice plant;
自該水稻植株光譜影像資料擷取至少一波長區間光譜資料，且該波長區間光譜資料包含一第一波長資料及一第二波長資料； At least one wavelength interval spectral data is acquired from the rice plant spectral image data, and the wavelength interval spectral data includes a first wavelength data and a second wavelength data;
將該波長區間光譜資料進行轉換成一植被指數資料； Convert the wavelength interval spectral data into vegetation index data;
利用該植被指數資料於一積溫數學模型進行對應計算一水稻植株積溫資料，以便獲得一幼穗分化判斷值資料；及 Utilize the vegetation index data to correspond to an accumulated temperature mathematical model to calculate a rice plant accumulated temperature data, so as to obtain a young panicle differentiation judgment value data; and
利用該水稻植株積溫資料及幼穗分化判斷值資料進行比對，以便獲得至少一預測幼穗分化積溫資料。 The rice plant accumulated temperature data and the young panicle differentiation judgment value data are used for comparison, so as to obtain at least one predicted young panicle differentiation accumulated temperature data.
本發明較佳實施例之該第一波長資料及第二波長資料選自660nm至800nm之間。 In the preferred embodiment of the present invention, the first wavelength data and the second wavelength data are selected from 660nm to 800nm.
本發明較佳實施例之該植被指數資料為一歸一化植被指數資料。 In a preferred embodiment of the present invention, the vegetation index data is a normalized vegetation index data.
本發明較佳實施例之該幼穗分化判斷值資料為由該植被指數資料於一時間序列上所計算產生之一斜率。 In a preferred embodiment of the present invention, the spikelet differentiation judgment value data is a slope calculated from the vegetation index data on a time series.
本發明較佳實施例之該植被指數資料進行計算多項式回歸分析而獲得一決定係數。 In the preferred embodiment of the present invention, the vegetation index data is subjected to polynomial regression analysis to obtain a coefficient of determination.
為了達成上述目的，本發明較佳實施例之水稻幼穗分化期之估計系統包含： In order to achieve the above object, the estimation system of the rice panicle differentiation stage in the preferred embodiment of the present invention includes:
一光譜儀單元，其於一預定期間選擇一水稻植株進行攝取一水稻植株光譜影像資料； A spectrometer unit that selects a rice plant to acquire spectral image data of a rice plant during a predetermined period;
至少一波長區間光譜資料，其擷取自該水稻植株光譜影像資料，且該波長區間光譜資料包含一第一波長資料及一第二波長資料； At least one wavelength interval spectral data is extracted from the rice plant spectral image data, and the wavelength interval spectral data includes a first wavelength data and a second wavelength data;
至少一水稻植株積溫資料，其自該水稻植株進行量測而獲得； At least one rice plant accumulated temperature data obtained by measuring the rice plant;
一計算機單元，其連接於該光譜儀單元而獲得該水稻植株光譜影像資料，且該計算機單元包含一積溫數學模型；及 A computer unit connected to the spectrometer unit to obtain spectral image data of the rice plant, and the computer unit includes an accumulated temperature mathematical model; and
一植被指數資料，其將該波長區間光譜資料利用該計算機單元進行轉換而獲得； A vegetation index data, which is obtained by converting the spectral data in the wavelength range using the computer unit;
其中利用該植被指數資料於該積溫數學模型進行對應計算該水稻植株積溫資料，以便獲得一幼穗分化判斷值資料，且利用該水稻植株積溫資料及幼穗分化判斷值資料進行比對而獲得至少一預測幼穗分化積溫資料。 The vegetation index data is used to correspond to the accumulated temperature mathematical model to calculate the accumulated temperature data of the rice plant in order to obtain a judgment value data of young panicle differentiation, and the accumulated temperature data of the rice plant and the judgment value data of young panicle differentiation are compared to obtain at least 1. Accumulated temperature data for prediction of young panicle differentiation.
本發明較佳實施例之該第一波長資料及第二波長資料選自660nm至800nm之間。 In the preferred embodiment of the present invention, the first wavelength data and the second wavelength data are selected from 660nm to 800nm.
本發明較佳實施例之該植被指數資料為一歸一化植被指數資料。 In a preferred embodiment of the present invention, the vegetation index data is a normalized vegetation index data.
本發明較佳實施例之該幼穗分化判斷值資料為由該植被指數資料於一時間序列上所計算產生之一斜率。 In a preferred embodiment of the present invention, the spikelet differentiation judgment value data is a slope calculated from the vegetation index data on a time series.
本發明較佳實施例之該植被指數資料進行計算多項式回歸分析而獲得一決定係數。 In the preferred embodiment of the present invention, the vegetation index data is subjected to polynomial regression analysis to obtain a coefficient of determination.
1:光譜儀單元 1: Spectrometer unit
11:波長區間光譜資料 11: Wavelength range spectral data
100:水稻植株 100:Rice plant
2:計算機單元 2: Computer unit
21:積溫數學模型 21: Mathematical model of accumulated temperature
3:水稻植株積溫資料 3: Accumulated temperature data of rice plants
9:預測幼穗分化積溫資料 9: Prediction of accumulated temperature data for spikelet differentiation
第1圖：本發明較佳實施例之水稻幼穗分化期之估計系統之方塊示意圖。 Figure 1: A block diagram of a system for estimating the differentiation stage of rice spikelets according to a preferred embodiment of the present invention.
第2圖：本發明較佳實施例之水稻幼穗分化期之估計方法之流程示意圖。 Figure 2: A schematic flow chart of the method for estimating the differentiation stage of rice spikelets according to the preferred embodiment of the present invention.
第3圖：本發明較佳實施例之水稻幼穗分化期之估計系統採用波長區間光譜資料之第一波長資料及第二波長資料進行迴歸分析及其決定係數之示意圖。 Figure 3: A schematic diagram illustrating the regression analysis and its determination coefficient using the first wavelength data and the second wavelength data of the wavelength interval spectral data of the rice panicle differentiation stage estimation system according to the preferred embodiment of the present invention.
第4圖：本發明較佳實施例之水稻幼穗分化期之估計系統採用由植被指數資料於一時間序列上所計算產生之幼穗分化判斷值資料之示意圖。 Figure 4: A schematic diagram of the rice panicle differentiation stage estimation system according to the preferred embodiment of the present invention using the young panicle differentiation judgment value data calculated from vegetation index data in a time series.
第5A圖：本發明另一較佳實施例之水稻幼穗分化期之估計系統採用第一組波長區間光譜資料之第一波長資料及第二波長資料進行迴歸分析及其決定係數之示意圖。 Figure 5A: A schematic diagram illustrating the regression analysis and its determination coefficient using the first wavelength data and the second wavelength data of the first set of wavelength interval spectral data in the rice panicle differentiation stage estimation system according to another preferred embodiment of the present invention.
第5B圖：本發明另一較佳實施例之水稻幼穗分化期之估計系統採用第二組波長區間光譜資料之第一波長資料及第二波長資料進行迴歸分析及其決定係數之示意圖。 Figure 5B: A schematic diagram illustrating the regression analysis and its determination coefficient using the first wavelength data and the second wavelength data of the second set of wavelength range spectral data of the rice panicle differentiation stage estimation system according to another preferred embodiment of the present invention.
第5C圖：本發明另一較佳實施例之水稻幼穗分化期之估計系統採用第三組波長區間光譜資料之第一波長資料及第二波長資料進行迴歸分析及其決定係數之示意圖。 Figure 5C: A schematic diagram illustrating the regression analysis and its determination coefficient using the first wavelength data and the second wavelength data of the third set of wavelength interval spectral data in the rice panicle differentiation stage estimation system according to another preferred embodiment of the present invention.
第5D圖：本發明另一較佳實施例之水稻幼穗分化期之估計系統採用第四組波長區間光譜資料之第一波長資料及第二波長資料進行迴歸分析及其決定係數之示意圖。 Figure 5D: A schematic diagram illustrating the regression analysis and its determination coefficient using the first wavelength data and the second wavelength data of the fourth set of wavelength interval spectral data for the rice panicle differentiation stage estimation system according to another preferred embodiment of the present invention.
第5E圖：本發明另一較佳實施例之水稻幼穗分化期之估計系統採用第五組波長區間光譜資料之第一波長資料及第二波長資料進行迴歸分析及其決定係數之示意圖。 Figure 5E: A schematic diagram illustrating the regression analysis and its determination coefficient using the first wavelength data and the second wavelength data of the fifth set of wavelength range spectral data for the rice panicle differentiation stage estimation system according to another preferred embodiment of the present invention.
第5F圖：本發明另一較佳實施例之水稻幼穗分化期之估計系統採用第六組波長區間光譜資料之第一波長資料及第二波長資料進行迴歸分析及其決定係數之示意圖。 Figure 5F: A schematic diagram illustrating the regression analysis and its determination coefficient using the first wavelength data and the second wavelength data of the sixth group of wavelength interval spectral data in the rice panicle differentiation stage estimation system according to another preferred embodiment of the present invention.
第5G圖：本發明另一較佳實施例之水稻幼穗分化期之估計系統採用第七組波長區間光譜資料之第一波長資料及第二波長資料進行迴歸分析及其決定係數之示意圖。 Figure 5G: A schematic diagram illustrating the regression analysis and its determination coefficient using the first wavelength data and the second wavelength data of the seventh group of wavelength interval spectral data in the rice panicle differentiation stage estimation system according to another preferred embodiment of the present invention.
為了充分瞭解本發明，於下文將舉例較佳實施例並配合所附圖式作詳細說明，且其並非用以限定本發明。 In order to fully understand the present invention, preferred embodiments will be exemplified and described in detail below with the accompanying drawings, which are not intended to limit the present invention.
本發明較佳實施例之水稻幼穗分化期之估計方法、其系統及其管理系統適用於各種稻米品種〔例如：台梗9號、台農71號、台南11號或其它稻米品種〕之水稻栽培管理系統，且本發明較佳實施例之水稻幼穗分化期之估計方法、其系統及其管理系統可選擇結合應用執行於各種自動化設備、各種半自動化設備或各種非自動化設備，但其並非用以限制本發明之應用範圍。 The method for estimating the differentiation stage of rice spikelets, its system and its management system according to the preferred embodiment of the present invention are applicable to various rice varieties [for example: Taigen No. 9, Tainong No. 71, Tainan No. 11 or other rice varieties]. Cultivation management system, and the method for estimating the differentiation period of rice spikelets according to the preferred embodiment of the present invention, its system and its management system can be selectively combined and applied to various automated equipment, various semi-automated equipment or various non-automated equipment, but it is not used to limit the scope of application of the present invention.
本發明較佳實施例之水稻幼穗分化期之估計方法、其系統及其管理系統適合應用於各種智慧型農業栽培管理系統、各種農業推廣或試驗改良單位、各種水稻栽 培業或其它相關農業栽培研究單位，但其並非用以限制本發明之範圍。 The method for estimating the differentiation stage of rice spikelets, its system and its management system according to the preferred embodiment of the present invention are suitable for use in various intelligent agricultural cultivation management systems, various agricultural extension or experimental improvement units, and various rice cultivation systems. cultivation industry or other related agricultural cultivation research units, but it is not intended to limit the scope of the present invention.
本發明較佳實施例之水稻幼穗分化期之估計系統之方塊示意圖。請參照第1圖所示，舉例而言，本發明較佳實施例之水稻幼穗分化期之估計系統可選擇為一水稻栽培管理系統、一水稻幼穗分化期估計管理系統或其它具類似水稻栽培功能之系統，且該水稻幼穗分化期之估計系統可應用於單一或數個水稻植株100，如第1圖之右側所示。
A block diagram of a system for estimating the differentiation stage of rice spikelets according to a preferred embodiment of the present invention. Please refer to Figure 1. For example, the estimation system for the rice panicle differentiation period in the preferred embodiment of the present invention can be selected as a rice cultivation and management system, a rice panicle differentiation period estimation management system, or other similar systems. The system of cultivation functions and the estimation system of rice panicle differentiation stage can be applied to a single or
請再參照第1圖所示，舉例而言，本發明較佳實施例之水稻幼穗分化期之估計系統主要包含一光譜儀單元〔spectrometer unit〕1、至少一波長區間光譜資料〔wavelength range spectrum data〕11、一計算機單元〔calculator unit〕2及至少一水稻植株積溫資料〔rice temperature accumulation data〕3。
Please refer to Figure 1 again. For example, the system for estimating the differentiation stage of rice spikelets according to the preferred embodiment of the present invention mainly includes a
請再參照第1圖所示，舉例而言，該光譜儀單元1於一水稻田單元〔未繪示〕之一適當位置可選擇攝影適當取得至少一水稻光譜影像資料〔rice spectrum image data〕，且將該光譜儀單元1以適當技術手段〔例如：固定架或其它固定設備〕固定於該水稻田單元之任何適當位置上。
Please refer to Figure 1 again. For example, the
請再參照第1圖所示，舉例而言，該光譜儀單元1及其周邊設備一般可選擇採用一光譜攝影單元、一光譜儀裝置或其它光譜儀單元，例如：多光譜儀單元，但其並非用以限制本發明之範圍。
Please refer to Figure 1 again. For example, the
請再參照第1圖所示，舉例而言，該水稻光譜影像資料可選擇選取自一穗肥前稻田光譜影像、一穗肥後稻田光譜影像、其任意組合稻田光譜影像或其它時期〔stage〕的稻田光譜影像，例如：水稻栽培時期稻田光譜 影像〔即水稻分蘗期、幼穗分化期或其任意組合之稻田光譜影像〕。 Please refer to Figure 1 again. For example, the rice spectral image data can be selected from the spectral image of a rice field before an ear is fertilized, a spectral image of a rice field after an ear is fertilized, any combination thereof, or a rice field in other stages. Spectral images, such as: rice field spectrum during rice cultivation period Image [i.e., rice field spectral image at the tillering stage, panicle differentiation stage, or any combination thereof].
請再參照第1圖所示，舉例而言，該計算機單元2以適當技術手段〔例如：自動化方式、半自動化方式或手動方式〕連接通訊〔例如：有線連接通訊或無線連接通訊〕該光譜儀單元1、其周邊設備或其它裝置單元。
Please refer to Figure 1 again. For example, the
請再參照第1圖所示，舉例而言，該計算機單元2以適當技術手段連接該光譜儀單元1，而該計算機單元2可選自一工作站電腦〔workstation computer〕、一桌上型電腦〔desktop computer〕、一筆記型電腦〔notebook 或laptop computer〕、一平板電腦〔tablet personal computer〕、一行動通訊裝置〔mobile communication device〕、一智慧型手機〔smart phone〕或其它具計算機功能之裝置，但其並非用以限定本發明之範圍。
Please refer to Figure 1 again. For example, the
請再參照第1圖所示，舉例而言，該計算機單元2包含一積溫數學模型21，且該積溫數學模型21用以將該水稻植株100之波長區間光譜資料11之第一〔參考〕波長資料λa及第二〔參考〕波長資料λb以適當方式〔例如：[(λa-λb)/(λa+λb)]〕進行計算一植被指數及一判斷值資料。
Please refer to Figure 1 again. For example, the
請再參照第1圖所示，舉例而言，該計算機單元2可選擇包含一歸一化植被指數〔NDVI，Normalized Difference Vegetation Index〕模組及一熱影像處理模組或其組合模組，以便以適當技術手段〔例如：數學統計模組〕進行計算處理該水稻光譜影像資料或該水稻熱影像資料及水稻溫度資料。
Please refer to Figure 1 again. For example, the
請再參照第1圖所示，舉例而言，該歸一化植被指數模組或熱影像處理模組之執行方式係屬可利用電腦執行之程序步驟〔computer-executable process step〕，其可執行於各種電腦設備，例如：工作站電腦、桌上型電腦、 筆記型電腦、平板電腦、行動通訊裝置、智慧型手機或其它具計算機功能之裝置，但其並非用以限定本發明之範圍。 Please refer to Figure 1 again. For example, the execution method of the normalized vegetation index module or the thermal image processing module is a computer-executable process step, which can be executed For various computer equipment, such as: workstation computers, desktop computers, Notebook computers, tablet computers, mobile communication devices, smart phones or other devices with computer functions, but they are not intended to limit the scope of the present invention.
請再參照第1圖所示，舉例而言，該水稻植株積溫資料3可選擇適當經由一輸入單元〔input unit，未繪示〕進行輸入至該水稻幼穗分化期之估計系統，且其可選擇由一熱顯相機單元〔未繪示〕或其它具類似熱顯相功能之裝置適當產生該水稻植株積溫資料3。
Please refer to Figure 1 again. For example, the rice plant accumulated
請再參照第1圖所示，舉例而言，該熱顯相機單元於該水稻田單元〔未繪示〕之任何適當位置取得一水稻熱影像資料〔rice thermal image data〕，且將該熱顯相機單元以適當技術手段〔例如：固定架或其它固定設備〕固定於該水稻田單元之任何適當位置上。 Please refer to Figure 1 again. For example, the thermal display camera unit obtains a rice thermal image data [rice thermal image data] at any appropriate position of the rice field unit (not shown), and displays the thermal display The camera unit is fixed on any appropriate position of the rice field unit using appropriate technical means (such as a fixed frame or other fixing equipment).
請再參照第1圖所示，舉例而言，該熱顯相機單元亦可選自一熱影像攝取單元，且該熱顯相機單元亦可選自一溫度感測單元、一紅外線感測單元、一熱影像分析單元、一具有類似溫度感測功能之裝置或其任意組合，以便適當進行偵測溫度。 Please refer to Figure 1 again. For example, the thermal display camera unit can also be selected from a thermal image capture unit, and the thermal display camera unit can also be selected from a temperature sensing unit, an infrared sensing unit, A thermal image analysis unit, a device with similar temperature sensing function, or any combination thereof, in order to appropriately detect the temperature.
請再參照第1圖所示，舉例而言，至少一水稻溫度資料或其它相關溫度資料以適當技術手段〔例如：自動化方式、半自動化方式或手動方式〕擷取自該水稻植株100之水稻熱影像資料，以便該水稻溫度資料應用於相關水稻幼穗分化期之估計作業。
Please refer to Figure 1 again. For example, at least one rice temperature data or other related temperature data is captured from the
第2圖揭示本發明較佳實施例之水稻幼穗分化期之估計方法之流程示意圖，其對應於第1圖所示之水稻幼穗分化期之估計系統。請參照第1及2圖所示，本發明較佳實施例之水稻幼穗分化期之估計方法包含步驟S1：首先，舉例而言，以適當技術手段〔例如：自動化方式、半自動化方式或手動方式〕於一預定期間利用該光譜儀單元1選擇單一或數個該水稻植株100進行適當攝取一水稻植
株光譜影像資料，即攝取單一或一系列該水稻植株光譜影像資料。
Figure 2 shows a schematic flow chart of a method for estimating the differentiation stage of rice panicles according to a preferred embodiment of the present invention, which corresponds to the system for estimating the differentiation stage of rice panicles shown in Figure 1 . Please refer to Figures 1 and 2. The method for estimating the rice panicle differentiation stage in the preferred embodiment of the present invention includes step S1: First, for example, using appropriate technical means [for example: automated, semi-automatic or manual Method] Use the
請再參照第1及2圖所示，舉例而言，該預定期間可選自一移植後期間〔例如：10天或其它適當天數〕，而該預定期間可選擇一預定時間間隔〔例如：1天間隔、2天間隔、3天間隔或其它適當時間間隔〕，且該預定期間可選擇一預定數據採集時間〔例如：上午10點至下午2點或其它適當採集時間〕。 Please refer to Figures 1 and 2 again. For example, the predetermined period can be selected from a post-transplant period [for example: 10 days or other appropriate days], and the predetermined period can be selected from a predetermined time interval [for example: 1 day interval, 2-day interval, 3-day interval or other appropriate time intervals], and a scheduled data collection time can be selected for the predetermined period [for example: 10 am to 2 pm or other appropriate collection time].
請再參照第1及2圖所示，本發明較佳實施例之水稻幼穗分化期之估計方法包含步驟S2：接著，舉例而言，以適當技術手段〔例如：自動化方式、半自動化方式或手動方式〕於該計算機單元2或其它具類似計算功能之裝置自該水稻植株100之水稻植株光譜影像資料擷取至少一波長區間光譜資料11，且該波長區間光譜資料11包含一第一〔參考〕波長資料及一第二〔參考〕波長資料。
Please refer to Figures 1 and 2 again. The method for estimating the rice panicle differentiation stage in the preferred embodiment of the present invention includes step S2: Next, for example, using appropriate technical means [for example: automated method, semi-automatic method or Manual method) The
請再參照第1及2圖所示，舉例而言，該水稻植株100之波長區間光譜資料11之第一〔參考〕波長資料及第二〔參考〕波長資料可選自660nm至800nm之間或其它適當第一〔參考〕波長資料及第二〔參考〕波長資料之資料組。
Please refer to Figures 1 and 2 again. For example, the first [reference] wavelength data and the second [reference] wavelength data of the wavelength interval
請再參照第1及2圖所示，本發明較佳實施例之水稻幼穗分化期之估計方法包含步驟S3：接著，舉例而言，以適當技術手段〔例如：自動化方式、半自動化方式或手動方式〕於該計算機單元2或其它具類似計算功能之裝置將該水稻植株100之波長區間光譜資料11之第一〔參考〕波長資料λa及第二〔參考〕波長資料λb以適當方式〔例如：[(λa-λb)/(λa+λb)]〕進行轉換成至少一植被指數資料〔NVDI data〕。
Please refer to Figures 1 and 2 again. The method for estimating the rice panicle differentiation stage in the preferred embodiment of the present invention includes step S3: Next, for example, using appropriate technical means [for example: automated method, semi-automatic method or Manually use the
第3圖揭示本發明較佳實施例之水稻幼穗分化 期之估計系統採用波長區間光譜資料之第一波長資料及第二波長資料進行迴歸分析及其決定係數之示意圖。請參照第3圖所示，舉例而言，本發明較佳實施例之水稻幼穗分化期之估計系統利用該植被指數資料進行計算多項式回歸分析〔quadratic polynomial regression〕而獲得一決定係數〔R2〕。 Figure 3 shows a schematic diagram of the estimation system of rice panicle differentiation stage according to the preferred embodiment of the present invention, which uses the first wavelength data and the second wavelength data of the wavelength interval spectral data to conduct regression analysis and its determination coefficient. Please refer to Figure 3. For example, the estimation system of the rice panicle differentiation stage in the preferred embodiment of the present invention uses the vegetation index data to perform quadratic polynomial regression analysis to obtain a determination coefficient [R 2 〕.
請參照第1、2及3圖所示，舉例而言，本發明較佳實施例之水稻幼穗分化期之估計系統採用該水稻植株100之波長區間光譜資料11之第一波長資料λa及第二波長資料λb進行迴歸分析及其決定係數〔R2〕，並選擇該第一波長資料λa為760nm及該第二波長資料λb為708nm。
Please refer to Figures 1, 2 and 3. For example, the estimation system of the rice panicle differentiation stage in the preferred embodiment of the present invention uses the first wavelength data λ a and the wavelength interval
請再參照第1及2圖所示，本發明較佳實施例之水稻幼穗分化期之估計方法包含步驟S4：接著，舉例而言，以適當技術手段〔例如：自動化方式、半自動化方式或手動方式〕於該計算機單元2或其它具類似計算功能之裝置利用該植被指數資料於該積溫數學模型21進行對應計算該水稻植株積溫資料3，以便獲得一幼穗分化判斷值資料。
Please refer to Figures 1 and 2 again. The method for estimating the rice panicle differentiation stage in the preferred embodiment of the present invention includes step S4: Next, for example, using appropriate technical means [for example: automated method, semi-automatic method or Manual method) Use the vegetation index data in the
第4圖揭示本發明較佳實施例之水稻幼穗分化期之估計系統採用由植被指數資料於一時間序列上所計算產生之幼穗分化判斷值資料之示意圖，其對應於第3圖。請參照第1、2、3及4圖所示，舉例而言，本發明較佳實施例之水稻幼穗分化期之估計系統採用該水稻植株100之波長區間光譜資料11之第一波長資料λa及第二波長資料λb進行迴歸分析及其決定係數〔R2〕，並選擇該第一波長資料λa為760nm及該第二波長資料λb為708nm。另外，該幼穗分化判斷值資料為由該植被指數資料於一時間序列上所計算產生之一斜率。
Figure 4 shows a schematic diagram showing that the rice panicle differentiation stage estimation system according to the preferred embodiment of the present invention uses young panicle differentiation judgment value data calculated from vegetation index data on a time series, which corresponds to Figure 3 . Please refer to Figures 1, 2, 3 and 4. For example, the estimation system of the rice panicle differentiation stage in the preferred embodiment of the present invention uses the first wavelength data λ of the wavelength interval
請再參照第1及2圖所示，本發明較佳實施例
之水稻幼穗分化期之估計方法包含步驟S5：接著，舉例而言，以適當技術手段〔例如：自動化方式、半自動化方式或手動方式〕於該計算機單元2或其它具類似計算功能之裝置利用該水稻植株積溫資料3及幼穗分化判斷值資料進行適當比對，以便獲得至少一預測幼穗分化積溫資料〔AGDDE〕9。
Please refer to Figures 1 and 2 again. The method for estimating the rice panicle differentiation stage in the preferred embodiment of the present invention includes step S5: Next, for example, using appropriate technical means [for example: automated method, semi-automatic method or Manual method] Use the
請再參照第1、2、3及4圖所示，舉例而言，本發明較佳實施例之水稻幼穗分化期之估計方法選擇採用在該植被指數資料於時間序列上所計算產生之斜率大約為零時，其對應至一實際記錄積溫〔AGDDO〕，而該預測幼穗分化積溫資料9大約為819.7，且該實際記錄積溫大約為791.1，兩者差距僅為1至2天。
Please refer to Figures 1, 2, 3 and 4 again. For example, the estimation method of the rice panicle differentiation period in the preferred embodiment of the present invention adopts the slope calculated on the time series of the vegetation index data. When it is approximately zero, it corresponds to an actual recorded accumulated temperature [AGDD O ], and the predicted young panicle differentiation accumulated
第5A圖揭示本發明另一較佳實施例之水稻幼穗分化期之估計系統採用第一組波長區間光譜資料之第一波長資料及第二波長資料進行迴歸分析及其決定係數之示意圖。請參照第1及5A圖所示，舉例而言，本發明另一較佳實施例之水稻幼穗分化期之估計系統採用該水稻植株100之波長區間光譜資料11之第一波長資料λa及第二波長資料λb進行迴歸分析及其決定係數〔R2〕為0.71，並選擇該第一波長資料λa為700nm及該第二波長資料λb為720nm。
Figure 5A shows a schematic diagram of a regression analysis and determination coefficient of the rice panicle differentiation stage estimation system using the first wavelength data and the second wavelength data of the first set of wavelength interval spectral data according to another preferred embodiment of the present invention. Please refer to Figures 1 and 5A. For example, the estimation system of the rice panicle differentiation stage in another preferred embodiment of the present invention uses the first wavelength data λ a and the wavelength interval
第5B圖揭示本發明另一較佳實施例之水稻幼穗分化期之估計系統採用第二組波長區間光譜資料之第一波長資料及第二波長資料進行迴歸分析及其決定係數之示意圖。請參照第1及5B圖所示，舉例而言，本發明另一較佳實施例之水稻幼穗分化期之估計系統採用該水稻植株100之波長區間光譜資料11之第一波長資料λa及第二波長資料λb進行迴歸分析及其決定係數〔R2〕為0.73，並選擇該第一波長資料λa為700nm及該第二波長資料λb為730nm。
Figure 5B shows a schematic diagram of a regression analysis and determination coefficient of the rice panicle differentiation stage estimation system using the first wavelength data and the second wavelength data of the second set of wavelength interval spectral data according to another preferred embodiment of the present invention. Please refer to Figures 1 and 5B. For example, the estimation system of the rice panicle differentiation stage in another preferred embodiment of the present invention uses the first wavelength data λ a and the wavelength interval
第5C圖揭示本發明另一較佳實施例之水稻幼
穗分化期之估計系統採用第三組波長區間光譜資料之第一波長資料及第二波長資料進行迴歸分析及其決定係數之示意圖。請參照第1及5C圖所示，舉例而言，本發明另一較佳實施例之水稻幼穗分化期之估計系統採用該水稻植株100之波長區間光譜資料11之第一波長資料λa及第二波長資料λb進行迴歸分析及其決定係數〔R2〕為0.74，並選擇該第一波長資料λa為708nm及該第二波長資料λb為730nm。
Figure 5C shows a schematic diagram of a regression analysis and determination coefficient of the rice panicle differentiation stage estimation system using the first wavelength data and the second wavelength data of the third set of wavelength interval spectral data according to another preferred embodiment of the present invention. Please refer to Figures 1 and 5C. For example, the estimation system of the rice panicle differentiation stage in another preferred embodiment of the present invention uses the first wavelength data λ a and the wavelength interval
第5D圖揭示本發明另一較佳實施例之水稻幼穗分化期之估計系統採用第四組波長區間光譜資料之第一波長資料及第二波長資料進行迴歸分析及其決定係數之示意圖。請參照第1及5D圖所示，舉例而言，本發明另一較佳實施例之水稻幼穗分化期之估計系統採用該水稻植株100之波長區間光譜資料11之第一波長資料λa及第二波長資料λb進行迴歸分析及其決定係數〔R2〕為0.71，並選擇該第一波長資料λa為660nm及該第二波長資料λb為760nm。
Figure 5D shows a schematic diagram of a regression analysis and determination coefficient of the rice panicle differentiation stage estimation system using the first wavelength data and the second wavelength data of the fourth set of wavelength interval spectral data according to another preferred embodiment of the present invention. Please refer to Figures 1 and 5D. For example, the estimation system of the rice panicle differentiation stage in another preferred embodiment of the present invention uses the first wavelength data λ a and the wavelength range
第5E圖揭示本發明另一較佳實施例之水稻幼穗分化期之估計系統採用第五組波長區間光譜資料之第一波長資料及第二波長資料進行迴歸分析及其決定係數之示意圖。請參照第1及5E圖所示，舉例而言，本發明另一較佳實施例之水稻幼穗分化期之估計系統採用該水稻植株100之波長區間光譜資料11之第一波長資料λa及第二波長資料λb進行迴歸分析及其決定係數〔R2〕為0.73，並選擇該第一波長資料λa為700nm及該第二波長資料λb為760nm。
Figure 5E shows a schematic diagram of a regression analysis and determination coefficient of the rice panicle differentiation stage estimation system using the first wavelength data and the second wavelength data of the fifth group of wavelength range spectral data according to another preferred embodiment of the present invention. Please refer to Figures 1 and 5E. For example, the estimation system of the rice panicle differentiation stage in another preferred embodiment of the present invention uses the first wavelength data λ a and the wavelength range
第5F圖揭示本發明另一較佳實施例之水稻幼穗分化期之估計系統採用第六組波長區間光譜資料之第一波長資料及第二波長資料進行迴歸分析及其決定係數之示意圖。請參照第1及5F圖所示，舉例而言，本發明另一較佳實施例之水稻幼穗分化期之估計系統採用該水稻植株100之波長區間光譜資料11之第一波長資料λa及第二波長
資料λb進行迴歸分析及其決定係數〔R2〕為0.75，並選擇該第一波長資料λa為708nm及該第二波長資料λb為760nm。
Figure 5F shows a schematic diagram of a regression analysis and determination coefficient of the rice panicle differentiation stage estimation system using the first wavelength data and the second wavelength data of the sixth group of wavelength interval spectral data according to another preferred embodiment of the present invention. Please refer to Figures 1 and 5F. For example, the estimation system of the rice panicle differentiation stage in another preferred embodiment of the present invention uses the first wavelength data λ a and the wavelength interval
第5G圖揭示本發明另一較佳實施例之水稻幼穗分化期之估計系統採用第七組波長區間光譜資料之第一波長資料及第二波長資料進行迴歸分析及其決定係數之示意圖。請參照第1及5G圖所示，舉例而言，本發明另一較佳實施例之水稻幼穗分化期之估計系統採用該水稻植株100之波長區間光譜資料11之第一波長資料λa及第二波長資料λb進行迴歸分析及其決定係數〔R2〕為0.70，並選擇該第一波長資料λa為708nm及該第二波長資料λb為800nm。
Figure 5G shows a schematic diagram of the estimation system of rice panicle differentiation stage according to another preferred embodiment of the present invention, which uses the first wavelength data and the second wavelength data of the seventh group of wavelength range spectral data to perform regression analysis and its determination coefficient. Please refer to Figures 1 and 5G. For example, the estimation system of the rice panicle differentiation stage in another preferred embodiment of the present invention uses the first wavelength data λ a and the wavelength interval
上述實驗數據為在特定條件之下所獲得的初步實驗結果，其僅用以易於瞭解或參考本發明之技術內容而已，其尚需進行其他相關實驗。該實驗數據及其結果並非用以限制本發明之權利範圍。 The above experimental data are preliminary experimental results obtained under specific conditions. They are only used for easy understanding or reference of the technical content of the present invention. Other relevant experiments are still required. The experimental data and its results are not intended to limit the scope of the present invention.
前述較佳實施例僅舉例說明本發明及其技術特徵，該實施例之技術仍可適當進行各種實質等效修飾及/或替換方式予以實施；因此，本發明之權利範圍須視後附申請專利範圍所界定之範圍為準。本案著作權限制使用於中華民國專利申請用途。 The foregoing preferred embodiments only illustrate the present invention and its technical features. The technology of this embodiment can still be appropriately implemented with various substantially equivalent modifications and/or substitutions; therefore, the scope of rights of the present invention shall depend on the appended patent application. The scope defined shall prevail. The copyright in this case is restricted to use for patent applications in the Republic of China.
1:光譜儀單元 1: Spectrometer unit
11:波長區間光譜資料 11: Wavelength range spectral data
100:水稻植株 100:Rice plant
2:計算機單元 2: Computer unit
21:積溫數學模型 21: Mathematical model of accumulated temperature
3:水稻植株積溫資料 3: Accumulated temperature data of rice plants
9:預測幼穗分化積溫資料 9: Prediction of accumulated temperature data for spikelet differentiation
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| CN108304973A (en) * | 2018-02-11 | 2018-07-20 | 中国农业大学 | Area crops maturity period prediction technique based on accumulated temperature, radiation and soil moisture content |
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| CN105557411A (en) * | 2015-12-15 | 2016-05-11 | 广西壮族自治区农业科学院水稻研究所 | Method for identifying heat-resistant seed setting performance of early rice at earing and flowering stages based on hour cumulative temperature |
| CN108304973A (en) * | 2018-02-11 | 2018-07-20 | 中国农业大学 | Area crops maturity period prediction technique based on accumulated temperature, radiation and soil moisture content |
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