Method for detecting flowering process and flowering quality of peach tree

Abstract

The invention discloses a method for detecting the flowering process and the flowering quality of a peach tree, and relates to the field of monitoring and sensing the growth condition of a fruit tree, and the method comprises the steps: cutting an image of a detection window, detecting the initial flowering time and the initial falling time of flowers, counting the flowering process of the detection window, and evaluating the flowering quality. The limitation that monitoring investigation consumes too much manpower in a traditional method is solved, and automatic quantitative monitoring of peach blossom growth is effectively achieved.

Description

Technical Field

[0001] The invention relates to the field of fruit tree growth status monitoring, and in particular to a method for detecting the flowering process and flowering quality of peach trees. Background Art

[0002] Peach trees are a type of fruit tree that blooms in large quantities. The flowering process and quality of peach trees vary depending on the influence of various factors inside the plant and outside the environment. When encountering adverse weather conditions such as high temperatures, low temperatures, frost, and rain during the flowering period, peach trees will experience rapid withering and low fruit setting rates. It is necessary to take timely agronomic measures to preserve flowers and fruits. When conditions are suitable, peach trees bloom in large quantities and of good quality. Appropriate flower and fruit thinning measures should be taken to ensure stable yield and quality. As the trend of global warming intensifies, the average temperature in most regions has risen, and the frequency and intensity of extreme high temperatures have increased. The temperature changes dramatically during the flowering period of fruit trees in spring in many places, and adverse weather conditions affect the flowering and fruit setting of fruit trees.

[0003] By monitoring the flowering process of peach trees, we can evaluate the growth status of flowers, provide references for understanding the fruit setting rate and early fruit drop, and guide subsequent management measures such as fruit thinning or fruit preservation. In traditional production practice, flowering period information is mainly obtained by manual on-site observation and recording of peach flowering, flower and fruit drop, etc., and there is a lack of automatic quantitative monitoring methods for peach flower growth. At present, some orchards are equipped with surveillance cameras to record orchard images. Various target detection and instance segmentation models can be used to identify fruit tree flowering and extract flowering feature information. However, after the peach flowers bloom, there is a high density and overlapping and blocking of flowers, which leads to poor results of deep learning models in detecting flower opening and initial withering. Summary of the invention

[0004] The purpose of the present invention is to provide a method for detecting the flowering process and flowering quality of peach trees, extracting the initial opening time and initial withering time of flowers in multiple detection windows in a monitoring image, and counting the duration of opening of each window. The method for evaluating the growth status of peach flowers according to the ratio of the number of windows with different opening durations and the corresponding flowering quality weights can realize automatic acquisition of information such as the flowering process and quality status by using monitoring images.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for detecting the flowering process and flowering quality of peach trees comprises the following steps:

[0007] Detection window image cropping;

[0008] Detection of initial flower opening time and initial flower withering time;

[0009] Statistics of flowering process in detection window;

[0010] Flowering quality assessment;

[0011] In some embodiments, the detection window image cropping includes: setting a number of detection windows distributed on the branches of the peach tree, covering the branches without overlapping, and having the same length and width in the fruit tree growth image collected at a fixed point at a fixed observation angle, and using the detection windows to crop the image to obtain a sub-image set W to be processed.

[0012] In some embodiments, the flower initial opening time and initial withering time detection includes: using a deep learning model to detect whether there is a flower target in the window image, and the time corresponding to the first appearance of the flower target is the flower initial opening time d 1 ; Use the time series red flower index calculated by window time series image to detect the initial flower fall time and detect d 1 The time when the red flower index rises to the maximum value and then starts to decline is the initial fall time d 2 .

[0013] In some embodiments, the safflower index is the ratio of the difference between the mean values ​​of the red channel and the green channel of the detection window image to the mean value of the green channel. The safflower index of the time series can be processed by SG filtering to reduce noise.

[0014] In some embodiments, the detection window blooming process statistics include extracting the initial opening time d of each window 1 , initial fall time d 2 , calculate the complete opening duration (d 2 -d 1 +1), and count the number of windows with flowering process detected and the proportion of windows with different opening time.

[0015] In some embodiments, the flowering quality assessment is performed by multiplying the proportions of windows with different opening durations by the flowering quality weights of the corresponding windows and then adding the results together.

[0016] In some embodiments, the window blossom quality weights include: a weight for less than normal duration, a weight for normal duration, and a weight for more than normal duration. Taking the normal duration as a reference, the weight for less than normal duration is smaller than the weight for normal duration, and the weight for more than normal duration is larger than the weight for normal duration.

[0017] The beneficial effects that may be brought about by the method for detecting the flowering process and flowering quality of peach trees disclosed in this application include but are not limited to:

[0018] The final result is the key time information of initial opening and initial withering in the local and overall flowering process of peach trees; according to the proportion of flowering in the detection window, the phenological information of the initial flowering period, the full flowering period, the flower falling period and the like of the traditional observation can be obtained, and the flowering quality of the whole tree can be evaluated according to the proportion and weight of different opening duration windows. By setting a reasonable detection window, the adverse effects of some image background objects on growth monitoring are reduced; the growth appearance process after the flower blooms is described by the safflower index time series data of the present invention, while the errors caused by image quality and image processing algorithms are reduced, and the time information related to the pollination and fertilization of peach flowers is reasonably reflected, so as to realize the flower quality evaluation; compared with the traditional manual observation and judgment, the present invention realizes the quantitative index expression and accurately reflects the details of the key process after flowering; compared with the single deep learning method, the problem of poor recognition accuracy of flower withering when flowers are clustered is avoided. The present invention has better adaptability in digital information orchards, and can achieve relatively stable recognition effects for the flower growth image data taken regularly by the automated image acquisition equipment under different environmental conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of monitoring the growth process of peach blossoms according to an embodiment of the present invention;

[0020] Figure 2 A schematic diagram of the setting and distribution of peach blossom growth image detection windows according to an embodiment of the present invention;

[0021] Figure 3 A schematic diagram of extracting the initial opening and initial withering time of peach blossoms according to an embodiment of the present invention;

[0022] Figure 4 This is a diagram of the detection result of the peach tree flowering process according to an embodiment of the present invention;

[0023] Figure 5 This is a diagram of peach tree flowering quality detection results according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present application more clear, the present application is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.

[0025] On the contrary, the present application covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present application as defined by the claims. Further, in order to make the public have a better understanding of the present application, some specific details are described in detail in the detailed description of the present application below. Those skilled in the art can fully understand the present application without the description of these details.

[0026] The following is a detailed description of a method for monitoring the growth process and quality of peach blossoms involved in the embodiments of the present application. It should be noted that the following embodiments are only used to explain the present application and do not constitute a limitation of the present application.

[0027] like Figure 1 As shown, a method for monitoring the growth process and quality of peach blossoms comprises the following steps:

[0028] Step S01, detecting window image cropping,

[0029] The detection window is set according to the distribution of branches during the flower bud germination period, the number of windows is not less than 30, the area covers the main branches and does not overlap, and the length and width of each window are the same; the monitoring image of the i-th day is cropped using n windows to obtain the sub-image set to be processed

[0030] Step S02, detecting the initial opening time and initial falling time of the flowers,

[0031] The initial opening time of the flower is the time when the petals first open in the window time series image, using the date array Expression, using the flower target deep learning model to detect the sub-image set W to be processed i , if there is an image of window k When the first flowering is detected, the date of the window in the array is updated according to the corresponding date of the i-th day

[0032] The initial falling time is the time when the petals begin to fall or wilt in the window time series image, using the date array The red flower index RFI is determined by calculating the image of each window. When the RFI increases to the maximum and then begins to decline, the corresponding date is the initial fall time. The red flower index is the ratio of the difference between the red channel mean R and the green channel mean G of the window image and the green channel mean. The image sequence of window k According to the formula Calculate the time series RFI If the window image When a flower is detected to be blooming, the date corresponding to the first maximum value after flowering is extracted according to the time series RFI of the window as the initial flower withering time

[0033] Step S03, detecting the statistics of the flowering process of the window,

[0034] Extract the initial flowering time d of each window 1 , initial fall time d 2 , calculate the complete opening duration (d 2 -d 1+1), count the number of windows with flowering process detected and the proportion of windows with different opening time;

[0035] Step S04, flowering quality assessment,

[0036] According to the accumulation of the ratio of different flowering duration windows and the corresponding flowering quality weights, the flowering quality weights include: a weight for less than normal duration, a weight for normal duration, and a weight for more than normal duration. Taking the normal duration as a reference, the weight for less than normal duration is smaller, and the weight for more than normal duration is larger.

[0037] The weight of less than normal duration, the weight of normal duration, and the weight of more than normal duration. Taking the normal duration as a reference, the weight of less than normal duration is smaller, and the weight of more than normal duration is larger.

[0038] The following is an explanation using a specific embodiment as an example:

[0039] The growth of fruit trees is monitored and collected at a fixed point with a fixed observation angle. In order to realize flowering detection and index calculation, the monitoring image data includes at least red, green and blue channel values.

[0040] Step S01, detection window image cropping, set 33 windows of 224×224 pixels on the non-flowering branches, the area covers the main branches and does not overlap, crop the monitoring image of the i-th day, the effect is shown in Figure 2 , get the sub-image set to be processed;

[0041] Step S02, flower initial opening time, initial withering time detection, according to Figure 3 The principle of extracting the initial opening and initial withering time of peach flowers is shown. The flower target deep learning model is used to detect the sub-image set to be processed to detect the flowering targets in each window, and the initial opening time of the flowers is obtained. The initial withering time of the flowers is obtained according to the corresponding date when the red flower index RFI increases to the maximum and then begins to decrease;

[0042] Step S03, detect the statistics of the flowering process of the window, and extract the initial flowering time d for the window with flowering process 1 , initial fall time d 2 , calculate the complete opening duration (d 2 -d 1 +1), see the effect Figure 4 , the number of windows with flowering process detected and the proportion of windows with different opening time are counted. The effect is shown in Figure 5 ;

[0043] Step S04, flowering quality evaluation, referring to the relationship between the flowering time of the detection window and the fruit setting rate, the weight of the opening time less than the normal opening time is set to 0.1, the weight of the normal opening time is set to 0.6, and the weight of the opening time more than the normal opening time is set to 0.9. The proportion of windows with different opening time lengths is multiplied by the flowering quality weight of the corresponding window, and the flowering quality evaluation result is accumulated. For the effect, see Figure 5 .

Claims

1. A method for detecting the flowering process and flowering quality of peach trees, characterized in that: The following steps are involved: Detection window image cropping; Detection of initial flower opening time and initial flower withering time; Statistics of flowering process in detection window; Evaluation of flowering quality.

2. A method for detecting the flowering process and flowering quality of peach trees according to claim 1, characterized in that: The detection window image cropping is to use multiple windows to crop the image to obtain a set of sub-images to be processed.

3. A method for detecting the flowering process and flowering quality of peach trees according to claim 1, characterized in that: The detection of the initial opening time and initial withering time of the flower includes: using a deep learning model to detect that the time corresponding to the first appearance of a flower target in a window image is the initial opening time of the flower, and determining the initial withering time according to the time when the maximum value of the red flower index of the image begins to decrease.

4. A method for detecting the flowering process and flowering quality of peach trees according to claim 1, characterized in that: The flowering process statistics of the detection windows include statistics of the initial opening time, initial withering time, continuous opening time, and the ratio of the number of windows with different opening time lengths of the flowers in each detection window.

5. A method for detecting the flowering process and flowering quality of peach trees according to claim 1, characterized in that: The flowering quality evaluation includes first calculating the product of the ratio of windows with different opening time lengths and the flowering quality weights of the corresponding windows, and then accumulating the results.

6. A method for detecting the flowering process and flowering quality of peach trees according to claim 2, characterized in that: The detection window includes a plurality of detection windows which are arranged according to the distribution of branches, cover the branches in an area without overlapping, and have the same length and width.

7. A method for detecting the flowering process and flowering quality of peach trees according to claim 3, characterized in that: The red flower index is the ratio of the difference between the mean values ​​of the red channel and the green channel of the detection window image to the mean value of the green channel.

8. A method for detecting the flowering process and flowering quality of peach trees according to claim 5, characterized in that: The window flowering quality weights include: the weight of less than normal duration, the weight of normal duration, and the weight of more than normal duration. Taking the normal duration as a reference, the weight of less than normal duration is smaller than that of normal duration, and the weight of more than normal duration is larger than that of normal duration.

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