WO2016161818A1 - 一种根据叶菜类作物长势进行变量施肥的方法 - Google Patents

一种根据叶菜类作物长势进行变量施肥的方法 Download PDF

Info

Publication number
WO2016161818A1
WO2016161818A1 PCT/CN2015/097791 CN2015097791W WO2016161818A1 WO 2016161818 A1 WO2016161818 A1 WO 2016161818A1 CN 2015097791 W CN2015097791 W CN 2015097791W WO 2016161818 A1 WO2016161818 A1 WO 2016161818A1
Authority
WO
WIPO (PCT)
Prior art keywords
growth
crop
fast
crown
growing period
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.)
Ceased
Application number
PCT/CN2015/097791
Other languages
English (en)
French (fr)
Inventor
毛罕平
高洪燕
张晓东
李青林
孙俊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiangsu University
Original Assignee
Jiangsu University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Jiangsu University filed Critical Jiangsu University
Publication of WO2016161818A1 publication Critical patent/WO2016161818A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining

Definitions

  • the invention belongs to the field of crop fertilization, in particular to a method for variable fertilization according to the growth of leafy crops.
  • Plants are made up of cells. Plant growth is an increase in the number of cells and an increase in the volume of cells. Therefore, plant growth is an irreversible increase in volume and weight. It has been found that crops undergo a "slow-fast-slow" "S-type” growth process during growth, that is, the initial growth is slow, and then gradually accelerates into the rapid growth period, reaching the maximum speed and then slowing down until the last stop. Growing. Whether it is the macroscopic characteristics of plants, such as weight, surface area, height, or microscopic characteristics of plants, such as the growth of cells and protein content, this process is followed.
  • the law of crop water and fertilizer absorption is basically the same as that of crop growth. The amount and intensity of absorption in the early stage of growth are low. With the passage of time, the absorption of nutrients gradually increases; when it matures, it tends to decrease.
  • the nutrient solution formula of leafy crops is mostly the same as the concentration of nutrient solution in the whole growing season. If the concentration of nutrient solution in the whole growing season is consistent, it does not conform to the law of crop growth and the law of nutrient absorption, resulting in unnecessary waste of fertilizer.
  • some formulas have special instructions. For example, Yamazaki nutrient solution formula suggests that lettuce should increase the concentration of nutrient solution before the ball is formed. However, due to the short growth cycle of lettuce and the ambiguity of each growth stage, it is difficult to clearly determine the growth period.
  • the present invention provides a method for variable fertilization according to the growth of leafy crops.
  • the whole growth period is divided into three according to the growth rate of the crop.
  • the purpose of variable fertilization according to the growth situation is achieved, which provides a basis for precise fertilization management of leafy crops.
  • the present invention achieves the above technical objects by the following technical means.
  • a method for variable fertilization according to the growth of leafy crops characterized in that it comprises the following steps:
  • S4. Determine the amount of fertilizer applied to the crop: determine the amount of fertilizer applied according to the fast-growing period of the crop growth determined by S3.
  • the crop crown projection area, the crown circumference and the plant height acquisition time of the crop of S1 are every 1-2 days.
  • the indicators described in S2 are absolute error, absolute correlation, and mean square error ratio.
  • the fertilization amount described in S4 is divided into three stages before the fast-growing period, in the fast-growing period, and after the fast-growing period.
  • the segments determine the amount of fertilizer applied separately.
  • the leafy crop is lettuce, cabbage, kale, rape or spinach.
  • the present invention applies variable fertilization to crops according to the growth of crops, can effectively reduce the waste of fertilizers, and ensures sufficient nutrient supply during the maximum efficiency period of crop nutrition, since the inhibition or promotion behavior of crops must be at the growth rate. It is completed before the maximum, so describing the growth law of crops has important practical significance in crop production.
  • Fig. 1 is a graph showing the time-varying projection area of leaf-crop crops according to the present invention.
  • FIG. 2 is a graph showing the relationship between the circumference of the crown of the leafy crops of the present invention as a function of time.
  • Fig. 3 is a graph showing the variation of plant height of leafy crops according to the present invention with time.
  • the leafy crops such as lettuce, cabbage, kale, rapeseed, and spinach are similar in nature, and the method of the present invention can be applied to the amount of fertilization.
  • the method of variable fertilization according to the growth of lettuce is taken as an example to illustrate the basis of leafy crops. The method of variable fertilization is carried out.
  • the test was carried out in the Venlo type greenhouse of Jiangsu University (32.11N, 119.27E).
  • the test materials were Italian whole-season resistant semi-boiled lettuce (Nanjing Wo Vegetable Seed Co., Ltd.). Seeds are placed in the trays for cultivation. When the seedlings grow to "five leaves and one heart", the lettuce seedlings with similar growth conditions are planted into the pots, and the inner diameter of the pots is 20 cm.
  • the soilless culture model of nutrient solution plus perlite was used in the study.
  • the experiment was carried out in an environmentally controlled greenhouse, which provided a stable growth environment for the crops, ensuring that the night minimum temperature was not lower than 15 °C, and the maximum temperature was not higher than 30. °C, the light intensity is 2000 ⁇ 4000LX. Fertilization management according to the nutritional formula of Yamazaki lettuce.
  • the specific operation is to use a white plate with a semicircular hole at each edge to be symmetrically stuck at the root of the lettuce, but it does not impose any restriction on the lettuce to ensure its original growth state.
  • the standard coordinate paper is used.
  • the lettuce main image collection is similar to the overhead image acquisition, and the ruler is placed on the vertical line of the lettuce canopy. Before the collection, the flowerpots should be uniformly marked. When the collection is carried out, the marked surface of the flowerpot is used as the main direction. Because the individual leaves of the lettuce are prominent, in order to fully reflect the growth condition and eliminate the accidental error, the flowerpot is rotated clockwise in the test. °, collect the main image of the lettuce again, and the average of the two images is taken as the final feature.
  • the canopy projection area and the crown circumference information of the lettuce were extracted from the overhead image, and the plant height information was extracted from the overhead image.
  • the corresponding features of each image extraction are averaged, that is, three sequences of lettuce crown projected area (TPCA), crown circumference (TPCP) and plant height (HP) are obtained, and the three growth parameters change with time. As shown in Fig. 1 to Fig. 3, it can be seen that the curve approximates "S type".
  • Table 1 Logistic time series prediction model of canopy projection area, plant height and crown circumference of lettuce
  • t is the number of times the growth information is collected, and the estimated values of crown projection area, crown circumference and plant height.
  • t is the number of times the growth information is collected, and is the estimated value of the crown projection area, the crown circumference and the plant height.
  • the evaluation indicators of the model mainly have three parameters: relative error, absolute correlation and mean square error ratio.
  • the model passed the test can be used for prediction.
  • the calculation results are shown in Table 3.
  • t s2 is the starting point of the fast-growing period
  • t e2 is the end point of the fast-growing period
  • a and b are the coefficients, as shown in Table 2; the measured values of the growth information at the initial moment.
  • the measured values of the projected area of the crown and the circumference of the crown are respectively 47.2145 cm 2 and 17.0352 cm, so the fast-growing period of the crown projected area after calculation is from 8.1543 to 24.6957 days, and the duration is 16.5414 days. .
  • the fast-growing period of the crown circumference ranges from 4.2059 to 25.7666 days for a duration of 21.5607 days.
  • t s1 is the starting point of the fast-growing period
  • t e1 is the end of the fast-growing period
  • i and j are the coefficients.
  • the fast-growing period of the plant height obtained from the calculation is from 3.9666 to 28.1687 days, and the duration is 24.2001 days.
  • the whole growth cycle is divided into three stages.
  • the first stage is before the fast-growing period (1 ⁇ 3.9666 days)
  • the second stage is the fast growing period (3.9667 ⁇ 28.1687 days)
  • the third stage is after the fast-growing period (28.1688 ⁇ harvest).

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Theoretical Computer Science (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • Health & Medical Sciences (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Agronomy & Crop Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Marine Sciences & Fisheries (AREA)
  • Mining & Mineral Resources (AREA)
  • Primary Health Care (AREA)
  • Development Economics (AREA)
  • Animal Husbandry (AREA)
  • Game Theory and Decision Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Cultivation Of Plants (AREA)
  • Hydroponics (AREA)

Abstract

一种根据叶菜类作物长势进行变量施肥的方法，通过获取作物冠层的俯视图像和主视图像，提取冠幅投影面积、冠幅周长和株高三个长势信息，分别建立时间序列预测模型，通过计算各模型的指标得出最优模型，计算三个长势信息的速生期，根据速生期将整个生长期分为3个阶段，从而确定施肥策略；根据作物长势情况进行变量施肥，能有效地减少肥料浪费，实现了作物营养的精确管理，降低了种植成本。

Description

一种根据叶菜类作物长势进行变量施肥的方法 技术领域
本发明属于作物施肥领域，尤其是一种根据叶菜类作物长势进行变量施肥的方法。
背景技术
植物体是由细胞组成，植物生长就是细胞数目的增多和细胞体积的增大，因此植物生长是体积和重量不可逆的增加过程。已有研究发现，作物在生长过程中经历着“慢-快-慢”的“S型”生长过程，即最初生长缓慢，然后逐渐加快进入快速生长期，达到最高速度后又减慢直至最后停止生长。无论是植物的宏观特征，如重量、表面积、高度，还是植物的微观特征，如细胞数量和蛋白质含量等的增长过程均遵循着此规律。作物水肥吸收规律与作物生长规律基本相同，生长初期吸收数量、强度都较低；随着时间的推移，对营养物质的吸收量逐渐增加；到成熟阶段，又趋于减少。
目前叶菜类作物的营养液配方大多是整个生长期营养液浓度供给相同，若整个生长期营养液浓度供给一致，不符合作物生长规律及养分吸收规律，造成肥料不必要的浪费。对于叶菜类作物，有些配方有特殊说明，如山崎营养液配方建议生菜在结球以前，适当增加营养液供给浓度。但由于生菜生长周期短，且各生长阶段表现模糊，很难明确判断各生长期。
发明内容
针对现有技术中存在不足，本发明提供了一种根据叶菜类作物长势进行变量施肥的方法，通过建立基于长势信息的时间序列模型，按照作物的生长速率，将整个生长期划分为三个阶段，进而达到了根据长势情况进行变量施肥的目的，为叶菜类作物精确施肥管理提供了依据。
本发明是通过以下技术手段实现上述技术目的的。
一种根据叶菜类作物长势进行变量施肥的方法，其特征在于，包括如下步骤：
S1、获取作物在不同生长阶段的冠幅投影面积、冠幅周长和株高，以时间为横坐标，分别以冠幅投影面积、冠幅周长和株高为纵坐标得到长势信息曲线；
S2、建立与S1所述的长势信息曲线相符的时间序列预测模型，通过指标对比所述时间序列预测模型，确定指标最佳的时间序列预测模型；
S3、计算作物生长的速生期：根据S2确定的时间序列预测模型确定公式计算作物生长的速生期；
S4、确定作物的施肥量：根据S3确定的作物生长的速生期确定施肥量。
进一步，S1所述作物冠幅投影面积、冠幅周长和株高的获取时间为每1～2天。
在上述方案中，S2所述的指标为绝对误差、绝对关联度、均方差比。
在上述方案中，S4所述的施肥量分为速生期之前、速生期、速生期之后三个阶 段分别确定施肥量。
在上述方案中，所述叶菜类作物为生菜、白菜、甘蓝、油菜或菠菜。
本发明的有益效果：
(1)本发明根据作物的长势情况对作物进行变量施肥，能有效地减少肥料的浪费，并保证在作物营养的最大效率期给予充足的养分供给，由于作物的抑制或促进行为必须在生长速率达最大之前完成，所以描述作物的生长规律在作物生产中具有重要实际意义。
(2)可通过调控作物上市期，获取最大经济效益。
(3)了解作物在不同生育期的水肥吸收规律，可有效地调控水肥供给，提高了作物产量，改善了作物品质并降低了种植成本。
附图说明
图1为本发明所述叶菜类作物冠幅投影面积随时间变化曲线图。
图2为本发明所述叶菜类作物冠幅周长随时间变化曲线图。
图3为本发明所述叶菜类作物株高随时间变化曲线图。
具体实施方式
下面结合附图以及具体实施例对本发明作进一步的说明，但本发明的保护范围并不限于此。
生菜、白菜、甘蓝、油菜、菠菜等叶菜类作物性质相似，施肥量均可采用本发明所述的方法，本实施例以根据生菜长势进行变量施肥的方法为例，说明根据叶菜类作物长势进行变量施肥的方法。
根据生菜长势进行变量施肥的方法：
(1)长势信息的获取
试验在江苏大学Venlo型温室内完成(32.11N，119.27E)，供试材料为意大利全年耐抽苔半结球生菜(南京沃蔬种业有限公司)。将种子放入穴盘中进行培育，待幼苗在生长到“五叶一心”时，将长势状况相似的生菜穴盘苗定植到花盆中，花盆内径为20cm。研究中采用营养液加珍珠岩的无土栽培模式，实验在环境可控温室中进行，可为作物提供较稳定的生长环境，保证夜最低温度不低于15℃，昼最高温度不高于30℃，光照强度为2000～4000LX。按山崎生菜营养配方进行施肥管理。
本研究自缓苗成功起，每隔2天进行一次图像采集，整个生长期共采集有效图像12次，每次获取单株生菜冠层的俯视及主视图像。生菜冠幅图像采集采用Canon EOS 400D相机，图像存储格式为JPEG，画质选择“精细”，图像分辨率为3888×2592。
生菜俯视图像采集时，注意以下几点：①在图像采集前，固定相机高度，利用标准白板对相机进行白平衡标定，以保证所得图像色彩还原准确；②使用相机的光圈优先模式进行拍摄，并将光圈设定在F8，ISO为100，使相机成像有足够的景深保证生菜在全部生长高度范围内都成像清晰；③在每次拍摄时采用延迟2s的自拍模式，用以消除人手在采集图像瞬间带来的微小抖动干扰；④为有利于后期图像处理，采集图像时以白色 平板为图像背景，具体操作上是采用两块边缘处各有一个半圆孔的白色平板对称地卡在生菜根部，但不对生菜产生任何束缚，保证其最原始的生长状态；⑤采用标准坐标纸为后期图像处理的参考标尺，每次采集图像之前，将4cm2的标准坐标纸固定于一平面上，并根据冠层高度调整坐标纸的高度，使标尺尽量与冠幅平面保持平行，减小因不同平面成像出现的畸变误差。
生菜主视图像采集与俯视图像采集相似，标尺放置于生菜冠层的中垂线上。采集前先对花盆进行统一标记，采集时将花盆有标记面作为主视方向，由于生菜个别叶片长势突出，为全面反映其生长状况，消除偶然误差，试验中将花盆顺时针旋转90°，再次采集生菜的主视图像，两次图像获取的均值做为最终特征。
从俯视图像中提取生菜冠幅投影面积和冠幅周长信息，从俯视图像中提取株高信息。分别将每次图像提取的对应特征进行平均处理，即得到生菜冠幅投影面积(TPCA)、冠幅周长(TPCP)和株高(HP)三个序列，则三个长势参数随时间变化曲线如图1～图3，可看出曲线近似“S型”。
(2)长势信息时间序列预测模型的建立
Logistic和灰色Verhulst算法具有S型增长特征，比较符合作物的生长规律，故建立生菜冠幅投影面积、株高和冠幅周长的Logistic时间序列预测模型(如表1)和灰色Verhulst时间序列预测模型(表2)。
表1生菜冠幅投影面积、株高和冠幅周长的Logistic时间序列预测模型
Figure PCTCN2015097791-appb-000001
(注：式中t为长势信息采集次数，和分别为冠幅投影面积、冠幅周长和株高的估计值。)
表2生菜冠幅投影面积、株高和冠幅周长的灰色Verhulst时间序列预测模型
Figure PCTCN2015097791-appb-000002
(注：式中t为长势信息采集次数，和分别为冠幅投影面积、冠幅周长和株高的估计值。
(3)长势信息时间序列模型的评价
对模型的评价指标主要有相对误差、绝对关联度和均方差比值3个参数，通过检验的模型才可用来预测，计算结果见表3。
表3时间序列预测模型评价结果
Figure PCTCN2015097791-appb-000003
(4)计算长势信息的速生期
由表2可知，灰色Verhulst时间序列预测模型可很好地预测冠幅投影面积和冠幅周长，冠幅投影面积和冠幅周长速生期的计算公式为：
Figure PCTCN2015097791-appb-000004
Figure PCTCN2015097791-appb-000005
式中，ts2为速生期起点；te2为速生期终点；a和b为系数，如表2所示；为初始时刻长势信息的实测值。
本实施例中，初始时刻冠幅投影面积和冠幅周长的实测值分别为47.2145cm2和17.0352cm，所以计算后得到冠幅投影面积的速生期从8.1543到24.6957天，持续时间为16.5414天。冠幅周长的速生期从4.2059到25.7666天，持续时间为21.5607天。
由表2可知，Logistic时间序列预测模型可很好地预测株高。故将Logistic时间序列预测模型由
Figure PCTCN2015097791-appb-000006
变化成
Figure PCTCN2015097791-appb-000007
形式，即
Figure PCTCN2015097791-appb-000008
则速生期的计算公式为：
Figure PCTCN2015097791-appb-000009
Figure PCTCN2015097791-appb-000010
式中，ts1为速生期起点，te1为速生期终点，i和j为系数。
所以计算后得到株高的速生期从3.9666到28.1687天，持续时间为24.2021天。
(5)生长阶段的划分及施肥策略的确定
根据生菜冠幅投影面积、株高及冠幅周长的速生期，将整个生长周期划分为3个阶段。第一阶段为速生期之前(1～3.9666天)，第二阶段为速生期为直线生长期(3.9667～28.1687天)，第三阶段为速生期之后(28.1688～收获)。通过多次试验，得出第一阶段施肥量为标准配方浓度的0.9倍即可满足作物生长；第二阶段作物生长旺盛，施肥量应为标准配方浓度的1.3倍；第三阶段施肥量为标准配方浓度的0.8倍即可达到目标产量。
所述实施例为本发明的优选的实施方式，但本发明并不限于上述实施方式，在不背离本发明的实质内容的情况下，本领域技术人员能够做出的任何显而易见的改进、替换或变型均属于本发明的保护范围。

Claims (5)

  1. 一种根据叶菜类作物长势进行变量施肥的方法，其特征在于，包括如下步骤：
    S1、获取作物在不同生长阶段的冠幅投影面积、冠幅周长和株高，以时间为横坐标，分别以冠幅投影面积、冠幅周长和株高为纵坐标得到长势信息曲线；
    S2、建立与S1所述的长势信息曲线相符的时间序列预测模型，通过指标对比所述时间序列预测模型，确定指标最佳的时间序列预测模型；
    S3、计算作物生长的速生期：根据S2确定的时间序列预测模型确定公式计算作物生长的速生期；
    S4、确定作物的施肥量：根据S3确定的作物生长的速生期确定施肥量。
  2. 如权利要求1所述的根据叶菜类作物长势进行变量施肥的方法，其特征在于，S1所述作物冠幅投影面积、冠幅周长和株高的获取时间为每1～2天。
  3. 如权利要求1所述的根据叶菜类作物长势进行变量施肥的方法，其特征在于，S2所述的指标为绝对误差、绝对关联度、均方差比。
  4. 如权利要求1所述的根据叶菜类作物长势进行变量施肥的方法，其特征在于，S4所述的施肥量分为速生期之前、速生期、速生期之后三个阶段分别确定施肥量。
  5. 如权利要求1～4中任意一项所述的根据叶菜类作物长势进行变量施肥的方法，其特征在于，所述叶菜类作物为生菜、白菜、甘蓝、油菜或菠菜。
PCT/CN2015/097791 2015-04-08 2015-12-18 一种根据叶菜类作物长势进行变量施肥的方法 Ceased WO2016161818A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201510163619.9A CN104820866B (zh) 2015-04-08 2015-04-08 一种根据叶菜类作物长势进行变量施肥的方法
CN201510163619.9 2015-04-08

Publications (1)

Publication Number Publication Date
WO2016161818A1 true WO2016161818A1 (zh) 2016-10-13

Family

ID=53731152

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2015/097791 Ceased WO2016161818A1 (zh) 2015-04-08 2015-12-18 一种根据叶菜类作物长势进行变量施肥的方法

Country Status (2)

Country Link
CN (1) CN104820866B (zh)
WO (1) WO2016161818A1 (zh)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109858067A (zh) * 2018-11-27 2019-06-07 河南农业大学 禾谷类作物长势测量模型的构建方法
CN111178437A (zh) * 2019-12-30 2020-05-19 深圳市芭田生态工程股份有限公司 一种施肥方法及计算机可读存储介质
CN111523781A (zh) * 2020-04-14 2020-08-11 四川大学 一种对林线环境下岷江冷杉幼苗长势进行综合评价的方法
CN112797888A (zh) * 2021-01-07 2021-05-14 江苏电子信息职业学院 一种基于互联网的农业信息化产业化系统
CN113034303A (zh) * 2021-03-22 2021-06-25 北京农业智能装备技术研究中心 一种温室作物施肥方法及系统
CN116843493A (zh) * 2023-07-03 2023-10-03 惠州可道科技股份有限公司 一种基于物联网的油茶林种植生态数字监测管理系统
CN120801251A (zh) * 2025-07-03 2025-10-17 重庆市测绘科学技术研究院 基于可微分物理引擎的多光谱植被根系识别系统及方法
CN121169003A (zh) * 2025-09-19 2025-12-19 江苏加德华中药材智能种植有限公司 一种猫爪草种植用管理监测方法及系统

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104820866B (zh) * 2015-04-08 2019-03-26 江苏大学 一种根据叶菜类作物长势进行变量施肥的方法
CN107238574A (zh) * 2017-06-07 2017-10-10 江苏大学 面向棉花靶向施肥的植株长势检测与诊断方法
CN109856323B (zh) * 2018-12-11 2021-08-03 江苏大学 一种确定生菜不同生长阶段生态化学计量比的方法
CN118504795B (zh) * 2024-07-18 2024-11-08 安徽省农业科学院经济作物研究所 一种适用于经济作物的自动化监测方法及系统
CN120544188A (zh) * 2025-07-29 2025-08-26 浙江工业大学 一种基于机器视觉的青菜生长速率预测方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103868891A (zh) * 2014-03-12 2014-06-18 中国农业科学院油料作物研究所 一种油菜叶片氮素营养快速诊断及推荐追氮的方法
CN103918392A (zh) * 2014-04-18 2014-07-16 福建省林业科学研究院 一种杉木和马尾松种子园平衡施肥的实验方法
CN103959973A (zh) * 2014-04-18 2014-08-06 浙江大学 一种精细化作物施肥系统及氮肥施肥方法
US20140324490A1 (en) * 2013-04-29 2014-10-30 Michael H. Gurin Distributed Farm Optimization System
CN104820866A (zh) * 2015-04-08 2015-08-05 江苏大学 一种根据叶菜类作物长势进行变量施肥的方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140324490A1 (en) * 2013-04-29 2014-10-30 Michael H. Gurin Distributed Farm Optimization System
CN103868891A (zh) * 2014-03-12 2014-06-18 中国农业科学院油料作物研究所 一种油菜叶片氮素营养快速诊断及推荐追氮的方法
CN103918392A (zh) * 2014-04-18 2014-07-16 福建省林业科学研究院 一种杉木和马尾松种子园平衡施肥的实验方法
CN103959973A (zh) * 2014-04-18 2014-08-06 浙江大学 一种精细化作物施肥系统及氮肥施肥方法
CN104820866A (zh) * 2015-04-08 2015-08-05 江苏大学 一种根据叶菜类作物长势进行变量施肥的方法

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109858067A (zh) * 2018-11-27 2019-06-07 河南农业大学 禾谷类作物长势测量模型的构建方法
CN109858067B (zh) * 2018-11-27 2022-09-27 河南农业大学 禾谷类作物长势测量模型的构建方法
CN111178437A (zh) * 2019-12-30 2020-05-19 深圳市芭田生态工程股份有限公司 一种施肥方法及计算机可读存储介质
CN111523781A (zh) * 2020-04-14 2020-08-11 四川大学 一种对林线环境下岷江冷杉幼苗长势进行综合评价的方法
CN111523781B (zh) * 2020-04-14 2023-04-25 四川大学 一种对林线环境下岷江冷杉幼苗长势进行综合评价的方法
CN112797888A (zh) * 2021-01-07 2021-05-14 江苏电子信息职业学院 一种基于互联网的农业信息化产业化系统
CN113034303A (zh) * 2021-03-22 2021-06-25 北京农业智能装备技术研究中心 一种温室作物施肥方法及系统
CN116843493A (zh) * 2023-07-03 2023-10-03 惠州可道科技股份有限公司 一种基于物联网的油茶林种植生态数字监测管理系统
CN116843493B (zh) * 2023-07-03 2024-01-02 惠州可道科技股份有限公司 一种基于物联网的油茶林种植生态数字监测管理系统
CN120801251A (zh) * 2025-07-03 2025-10-17 重庆市测绘科学技术研究院 基于可微分物理引擎的多光谱植被根系识别系统及方法
CN121169003A (zh) * 2025-09-19 2025-12-19 江苏加德华中药材智能种植有限公司 一种猫爪草种植用管理监测方法及系统

Also Published As

Publication number Publication date
CN104820866B (zh) 2019-03-26
CN104820866A (zh) 2015-08-05

Similar Documents

Publication Publication Date Title
WO2016161818A1 (zh) 一种根据叶菜类作物长势进行变量施肥的方法
Jalota et al. Integrated effect of transplanting date, cultivar and irrigation on yield, water saving and water productivity of rice (Oryza sativa L.) in Indian Punjab: field and simulation study
CN105875228A (zh) 一种用led灯对黄瓜育苗的补光方法
Gang et al. Plastic film mulching combined with nutrient management to improve water use efficiency, production of rain-fed maize and economic returns in semi-arid regions
CN107690925A (zh) 一种杂交水稻精确追肥方法
CN108401634B (zh) 一种大棚鲜食番茄氮素营养诊断及推荐施氮方法
CN111657139A (zh) 一种提高甘蔗高糖种质选择效率的田间试验方法
CN107155665B (zh) 一种基于叶面积增长模型制定作物灌水策略的方法
CN102301900A (zh) 用于大田移栽前宿根性测试的甘蔗实生苗假植方法
CN108334701B (zh) 一种大棚黄瓜氮素营养诊断及推荐施氮方法
CN104322353A (zh) 基于根层水分监测的冬小麦调亏灌溉方法
CN103650730B (zh) 一种根据作物投影面积和植株高度进行施肥的方法
CN105009901B (zh) 一种多期多样本法检测光温敏核不育水稻不育性的方法
CN103650732B (zh) 一种根据作物茎秆直径进行营养生长期施肥的方法
CN107581061B (zh) 一种油菜种子灌浆期高温耐受性高效筛选鉴定方法
Durga et al. Evaluation of effectiveness of nano sensor (IITB) based irrigation system on water productivity and yield of maize (Zea mays)
CN103650731B (zh) 一种根据作物植株高度变化进行营养生长期施肥的方法
CN116797601A (zh) 一种基于图像识别的华山松生长动态的监控方法及系统
CN115812591A (zh) 一种玉米杂交制种中双亲花期安排与预测的方法
CN103636340B (zh) 一种根据作物果实生长速度进行结果期施肥的方法
CN109005836B (zh) 一种提高水稻缓释氮肥利用效率的灌溉方法
CN105165365A (zh) 一种依据树冠冠幅竞争设计用材林林分密度及调控方法
Kim et al. Precise, real-time measurement of the fresh weight of lettuce with growth stage in a plant factory using a nutrient film technique
CN112577934A (zh) 一种基于叶绿素荧光图像分析的灌浆期耐热油菜筛选方法
Čereković et al. Evaluation of the crop coefficients for tomato crop grown in a Mediterranean climate

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 15888367

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 15888367

Country of ref document: EP

Kind code of ref document: A1