Analysis of navigation accuracy test results of the tracking robot
The indoor image acquisition robot detects the position relationship between the center of the vehicle and the black guide line through four tracing infrared sensors, and adjusts the speed of the drive wheels on both sides of the vehicle to achieve automatic tracking function. In this paper, the motion control of the system is tested on the hard surface, and two different motion state tests of straight line and traveling turn are designed to test the motion speed and running position control effect of the platform respectively to verify the reliability of its operation [27]. The motion trajectory of the robot at three speeds of 0.2 m/s, 0.4 m/s, and 0.6 m/s was recorded by the marker pen. The distance between the robot and the black guide line was measured every 10 cm for 20 times at each speed, and the measurement results are shown in Table 2.
Table.2 Navigation accuracy test results of the tracer robot
Speed (m/s)
|
Linear deviation (cm)
|
Curve deviation (cm)
|
Linear deviation variance (cm2)
|
Curve deviation variance (cm2)
|
0.2
|
± 0.103
|
± 0.115
|
0.347
|
1.694
|
0.4
|
± 0.605
|
± 0.125
|
0.149
|
2.133
|
0.6
|
± 0.580
|
± 0.565
|
0.135
|
7.911
|
The deviation of the image acquisition robot speed response is shown in Fig. 8. The analysis shows that the slower the driving speed, the higher the robot's navigation accuracy. Considering the operation efficiency, the 0.4 m/s running speed is selected without affecting the quality of photos. The robot acquisition mode is set to automatic acquisition mode, when setting the travel speed of the tracer robot to 0.4m/s, the rotation time of the servo is 1.2s, the interval time of continuous shooting is 0.2s, the image transmission time is about 0.5s, the camera acquires images about 10s for 3 times, and the efficiency of individual camera image acquisition is about 18 images/min. The test results show that the system can acquire crop images stably and reliably.
Wheat leaf pose acquisition and analysis
To verify the feasibility of the system, the system was tested at the Baima base of Nanjing Agricultural University. From April 7, 2021 to May 7, 2021, the system was used to collect the lateral view images of wheat from flowering to maturity, during which 2556 images were collected and 96 samples were extracted. After image enhancement, the different poses of each genotype of wheat were arranged as shown in Fig. 9(a). It was observed that different genotypes of wheat collected by this system showed different posture performance, the top one leaf of Ruihuamai 523 grew horizontally, the top one leaf of Jimai 22 grew upward, and the top one leaf of Xumai 33 had a tendency to grow downward. The enhanced RGB images were clearer, so this dataset can be used for posture assessment and classification of wheat[28].
After manual calibration of the angle extraction, it was found that the mean values of the top one leaf angle of three genotypes, two treatments and four sets of replicated trials, namely,Ruihuamai 523, Jimai 22 and Xumai 33, were 87.51°, 54.48°and 106.58°, respectively, confirming the observation.
After 5 days of drought stress, the lateral views of potted wheat collected by the crop image acquisition robot are arranged according to genotype and treatment after image enhancement. To maintain the control variable principle, the same row shows wheat posture with different genotypes for the same treatment, while the same column shows wheat posture with different treatments for the same genotype. To show the degree of leaf curl more clearly, a local magnification was added to the leaf part of the image after drought stress treatment for each genotype, as shown in Fig. 9(b).
Image processing and parameter extraction results
In this paper, image processing is mainly studied by pre-processing the collected images for wheat plant height and stem width measurement, and then the accuracy of the gestalt acquisition system is obtained by comparing the predicted plant height test from the collected wheat pictures with the real measurement test of wheat plant height in the greenhouse [29].
In this paper, to extract the predicted value of wheat plant height, firstly, the side view of wheat is pre-processed by graying, binarization, color segmentation, image enhancement, and other functions in MATLAB; then the area judgment is used to identify the wheat plant with the calibration connected domain, as shown in Fig. 10(a); finally, by identifying the four edge points in the green part of the image, the distance between the four edge points is calculated, and the median distance obtained is the length of the connected domain rectangle, which is the predicted value of wheat plant height, and the resultant interface is shown in Fig. 10(b).In this paper, we specify that the measured portion of the single wheat plant height is the vertical distance from the top of the wheat ears to the top of the pot. The actual measurement of wheat stem width is the average of the two values from the spike to one leaf and from one leaf to two leaves, as shown in Fig. 10(c).
Similarly, by identifying the four edge points in the green part of the image, the distance between the four edge points is calculated, and the minimum value of the distance obtained is the width of the connected domain rectangle, which is the stem width of the wheat image.The image processing process and stem width result display interface are shown in Fig. 11.
Accuracy analysis of image acquisition system
In order to check the image acquisition accuracy of the crop image acquisition robot more intuitively, a linear regression analysis was performed on all extracted values of wheat plant height and stem width against the actual ground measurements. Due to the shooting angle, the image will have a certain linear tilt deformation, which is not conducive to the extraction and recognition of target features by the computer; meanwhile, in the vision measurement system, the image will have different degrees of geometric distortion in geometric position, size, shape, and orientation due to the lens manufacturing accuracy, which will affect the measurement accuracy of the vision measurement system [30]. Therefore, in this paper, the predicted values of plant height and stem width were multiplied by the corresponding correction coefficients at flowering and maturity stages, and then the predicted values were compared with the measured values to obtain the fitted images as shown in Fig. 12.
As can be seen from Fig. 12, the R2 is 0.7151 and 0.6278, respectively, indicating that the plant height acquisition values of the three genotypes are closer to the image extraction values, and the fitting functions are significantly positively correlated with high accuracy [31]; indicating that the method of acquiring morphological parameters of a single wheat plant in a certain area is feasible based on the automatic crop image acquisition system of the infrared tracer robot.
Results of statistical analysis
The results of SPSS main effects ANOVA for wheat morphological parameters are shown in Table 3. The results showed that the actual measured values of plant height and stem width were significantly correlated with the predicted values, indicating that this picture collection system has some accuracy and stability. Drought stress did not significantly affect the plant height and stem width of wheat plants at flowering, indicating that wheat plants do not change significantly in growth parameters such as plant height and stem width after flowering. Plant height was significantly correlated with stem width, and the results indicate that it is feasible to use a gestalt acquisition robot to take high-definition digital images for rapid estimation of plant height and stem width of winter wheat [32]. In addition, the top one and three leaf inclination angles were significantly correlated with genotype, indicating that different wheat species behave differently posture.
Table.3 Wheatposture evaluation and analysis of variance of main effects of each factor
Test
|
Analysis of variance effect
|
Significance
|
Plant height
|
Predicted value of plant height
|
< 0.001***
|
Varieties(random)
|
< 0.05*
|
Drought treatment(random)
|
0.222
|
Stem width
|
Predicted value of Stem
|
< 0.001***
|
Varieties(random)
|
0.232
|
Drought treatment(random)
|
< 0.05*
|
Top first leaf inclination
|
Predicted value of top first leaf inclination
|
< 0.001***
|
Varieties(random)
|
< 0.01**
|
Drought treatment(random)
|
0.512
|
Top third leaf inclination
|
Prediction of top third leaf inclination
|
< 0.05**
|
Varieties(random)
|
0.117
|
Drought treatment(random)
|
0.572
|
Plant height
|
Stem width
|
< 0.01**
|
Varieties(random)
|
0.105
|
Drought treatment(random)
|
0.069
|
Plant height
|
Varieties
|
0.083
|
Drought treatment(random)
|
0.237
|
Stem width
|
Varieties
|
0.719
|
Drought treatment(random)
|
0.458
|
Top first leaf inclination
|
Varieties
|
< 0.01**
|
Drought treatment(random)
|
0.923
|
Top third leaf inclination
|
Varieties
|
< 0.01**
|
Drought treatment(random)
|
0.279
|
*、**、*** indicate significance at the p = .05, .01, and .001 levels, respectively. |
The wheat plant height, stem width, leaf one inclination, and leaf three inclination were subjected to LSD post hoc analysis with the genotypes as well as the treatments, respectively, and the height of the histogram represents the mean value of its parameters, and the error bars represent the deviation between the actual measured and predicted values of this parameter, with different letters on the histogram indicating significant differences (p≤0.05) determined by LSD. The results were obtained as the significant variance histogram shown in Figure 13.
From Fig. 13, it was found that the influence factors such as drought stress and genotype had no significant effect on plant height and stem width, while the leaf inclination angle was more influential. In fact, winter wheat mainly develops spikelets from flowering stage, and its plant height and stem width will not change significantly, while the leaf inclination angle may change due to drought stress, then the results of this statistical analysis are consistent with the growth pattern of wheat.