An ASABE Meeting Presentation DOI: https://doi.org/10.13031/aim.201700723 Paper Number: 1700723
Detection of Rape Canopy SPAD Based on Multispectral Images of Low Altitude Remote Sensing Platform Chen Yang 866 Yuhangtang Road, Hangzhou, Zhejiang Province, China
Written for presentation at the 2017 ASABE Annual International Meeting Sponsored by ASABE Spokane, Washington July 16-19, 2017 ABSTRACT. Nutrient detection and distribution visualization of rapeseed canopy by low-altitude multi-spectral remote sensing is very important for precision nutrient management. In this research, a multi-spectral camera was used on the unmanned aerial vehicles (UAV) low-level remote sensing simulation platform to obtain multi-spectral images of rape canopy at seedling stage and six vegetation indices were selected for optimization, and a linear analytical model of canopy SPAD was established based on the vegetation indices. The influence of image acquisition height and velocity on the model prediction was further analyzed by setting different camera heights and velocities. The results show that the prediction performance of the model may be improved to different extents with the increase of image acquisition height and reduction of image acquisition velocity and SPAD linear prediction model based on vegetation index (NIRG)/(NIR+G) is optimal when the camera velocity is 0.1m/s and height is 1.9m, the correlation coefficient Rp is up to 0.7354. This research lays a theoretical foundation for rapidly obtaining the large-scale rape canopy nitrogen information via UAV platform-based low altitude multi-spectral remote sensing technology in the future. Keywords. low-level remote sensing, multi-spectral, SPAD prediction, vegetation index, rape canopy
Introduction Rape is an important oil crop widely planted in China. The abundance of nitrogen during growth affects the synthesis and photosynthetic rate of rape cell protein. It is of great significance to monitor the nitrogen content of rapeseed and develop a reasonable nitrogen application program in a timely and effective manner for the purposing of increasing the yield [1]. Rapid detection of nitrogen is a prerequisite for precision nutrient management of rape. It is difficult to apply such conventional nitrogen detection methods as appearance diagnosis, chemical analysis and chlorophyll measurement in the large-scale fields. Featured by a wide range of radiation, high timeliness, no harm to the crops, high accuracy and other advantages that other conventional nitrogen detection methods cannot catch up with, spectral imaging remote sensing platform has been more and more widely used in large-scale complex environment. In recent years, with the gradual decrease of UAV technology threshold, the UAV platform-based low altitude multi-spectral remote sensing technology is gradually shifted to the civilian and practical directions. Compared with traditional satellites and manned aircraft, UAVs are featured by high mobility, strong environmental adaptability, airspace constraints and low meteorological conditions, and possess unique advantages when they are used as the basis for low altitude multi-spectral remote sensing platform [2]. Based on the characteristics of spectrum detection, the low altitude multi-spectral remote sensing technology of UAV platform is expected to achieve large-scale, timely and efficient access to crop nitrogen detection. In this research, SPAD-502 type chlorophyll meter was adopted to rapidly obtain the SPAD value of the leaf. The UAV low altitude remote sensing platform is then used to carry the multi-spectral camera to obtain the multi-spectral images of rape canopy at seedling stage and extract 6 types of vegetation characteristic indices with SPAD value obtained as the evaluation criteria for nitrogen content, so as to explore the feasibility of establishing the linear analytical model of canopy SPAD and performing visual inversion based on the multi-spectral images obtained via low altitude remote sensing technology, and analyze the influence of image acquisition height and velocity on the model prediction, laying a theoretical foundation for rapidly obtaining the large-scale rape canopy nitrogen information via UAV platform-based low altitude ASABE 2017 Annual International Meeting
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multi-spectral remote sensing technology in the future[3-4].
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Materials and Methods
1.1 Test Equipment The UAV low altitude remote sensing platform used is shown in Fig. 1. The hardware of this platform is composed by traction components, linear guide, electric cylinder, tripod head, camera sensor, etc. The platform can achieve five-degreeof-freedom motions by carrying the camera sensor controlled by the control system, i.e. horizontal linear motion that moving forward and back along the linear guide by simulating UAV, up-and-down vertical motion that moving up and down along the electric cylinder by simulating UAV, as well as the three-axle motions of pitching, rolling and yawing through the tripod head by simulating UAV. The linear guide is 12m long in total and 3.8m high from the ground, which controls the electric cylinder with the lifting shaft scope of 0.5m via the sliding block installed. The tripod head is fixed in the lower part of the lifting shaft to install the multi-spectral camera. When this system is equipped with the multi-spectral camera and applied to obtain the information about the growth of rape via low altitude remote sensing by simulating the UAV, the rape shall be located in the under face of the linear guide, and five-degree-of-freedom motions are exactly and accurately controlled by the control system as per the conditions to be controlled for continuous acquisition of data.
Fig.1 Unmanned Aerial Vehicles (UAV) Low-level Remote Sensing Simulation Platform
The multi-spectral camera used in the test (ADC camera) is shown in Fig.1, which is manufactured by Tetracam in America and is equipped with 3.2 mega-pixel (2048*1536) CMOS sensor, so it can acquire the information from green, red and near-infrared bands, and supporting the remote triggering, continuous shooting, exposure delay and other functions. With 4.5~12mm zoom lens applied, the image acquisition velocity reaches 3-5s/pc. PixelWrench2 is a type of image processing software developed by Tetracam for ADC camera, which can perform multiple processing operations for images shot by ADC camera in NIR/ Green/ Red or other bands, including generating new images based on the definition of the customer or standard algorithm. 1.2 Sample Preparation In the test, 96 healthy and similar rape seedlings were chosen as the samples. The samples were divided into three groups evenly, and performed with nitrogen stress tests by dosing no nitrogen, proper nitrogen and excessive nitrogen. The nitrogen fertilizer was applied in the form of nutrient solution at the fixed time and quantity. The nitrogen concentration of nutrient solution for the group with proper nitrogen was 200mg/L, and the nitrogen concentration ratio of three nutrient solutions is 0:1:2. In the test, all samples were applied with the phosphate fertilizer and potash fertilizer with the same concentration. Proper nitrogen was applied to meet the requirements for normal growth of the crops. The mass concentration rate of nitrogen, phosphate and potash in the group with proper nitrogen was 10:4:9. The nitrogen stress test was mainly designed to increase the distinction degree of nitrogen content in rape canopy. Considering the growth difference, the nitrogen content in rape canopy described in the test was measured based on the actually acquired SPAD value. 1.3 Data Acquisition and Processing The SPAD values and multi-spectral images of rape canopies of each sample were acquired when growth difference ASABE 2017 Annual International Meeting
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occurs in different nitrogen stress groups. First, SPAD-502 type chlorophyll meter was applied to obtain the SPAD values of all developed leaves of samples, and obtain the SPAD value of canopy of each sample by weighting the area of each leaf in canopy against the SPAD value of such leave. The SPAD values of rape seedlings were acquired before acquiring the multi-spectral images of canopy, and the intervals were shortened to ensure the timeliness of the SPAD values measured. The multi-spectral data was acquired by providing ADC camera for UAV simulation platform, and the aperture, time of exposure and focal distance of the camera were adjusted before acquisition to ensure clear image and avoid overexposure. The camera parameters kept unchanged during acquisition. During acquiring, the tripod head was adjusted to keep the lens of ADC camera vertical to the ground. During test, the samples were evenly placed under the linear guide of UAV simulation platform to minimize the overlapping of samples, so as to segment and extract the canopy images of each sample. The multispectral images were acquired at 11:00-13:00 in 5 groups. Before acquisition of each group, the height and motion velocity of the camera were set via UAV simulation platform, see Table for corresponding parameters of the camera. The height and velocity of the cameras refer to the height of lens to the ground, and the absolute ground velocity respectively. Table 1 Camera Parameters of Each Multispectral Image Acquisition Group
1
2
3
4
5
Camera Height/m
1.5
1.7
1.9
1.9
1.9
Camera Velocity/(m/s)
0.1
0.1
0.1
0.2
0.3
It can be seen from Table 1 that the camera velocities were all 0.1m/s and camera heights were 1.5m, 1.7m and 1.9m respectively when the images of the first three groups were acquired for analyzing the influence of different image acquisition heights on the performance of SPAD value linear prediction model, and that the camera heights were all 1.9m, and camera velocities were 0.1m/s, 0.2m/s and 0.3m/s respectively when the images of the last three groups were acquired for analyzing the influence of different image acquisition velocities on the performance of SPAD value linear prediction model. After the multi-spectral images of the samples were obtained, the images were adjusted according to the following formula: 𝐼𝐼 −𝐼𝐼 𝑅𝑅 = 𝑟𝑟𝑟𝑟𝑟𝑟 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 (1) 𝐼𝐼𝑤𝑤ℎ𝑖𝑖𝑖𝑖𝑖𝑖 −𝐼𝐼𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
Where, I raw refers the raw image acquired, I dark refers to the image adjusted by scanning the blackboard, I white refers to the image adjusted by scanning the whiteboard, and R refers to the image finally obtained. Then, the brightness of non-vegetation background was changed to 0 to segment the background of the image. The interested area was then obtained by cutting the rape canopy of single sample with the background moved, and the multispectral image of single sample was obtained by performing denoise processing for the image with the relevant algorithms. The whole multi-spectral image processing flow is shown in Fig. 2, where, a refers to the raw image, b refers to the adjusted image, c refers to the image with the background segmented, d refers to the image with the sample separated, and e refers to the final image obtained after denoise processing. Then the number of nonzero pixel points of three channels of the processed multi-spectral images NIR, R and G was obtained to calculate the average value NIR, R and G of reflection of the samples’ rape canopies in channels NIR, R and G, and structure 6 characteristic indices of NIR/R, NIG/G, G/R, (NIRR)/(NIR+R), (NIR-G)/(NIR+G) and (G-R)/(G+R).
Fig.2 Multispectral Image Processing Flow
2
Results and Analysis
2.1 Comparison and Optimization of Linear Models Based on Different Vegetation Indices In the test, a total of 96 samples from the groups with insufficient nitrogen, proper nitrogen and excessive nitrogen were mixed, and randomly divided into two sets of modeling set and prediction set in the proportion of 2:1. The samples in modeling set were used for modeling, while those in prediction set were used for verifying the prediction models established. The linear models were established with the vegetation indices as model input and SPAD as model output. The linear prediction models of different vegetation indices were established assuming that the height of camera to ground is 1.9m and the camera velocity is 0.1m/s, as shown in Table 2. It can be seen from Table 2 that the linear models established based on ASABE 2017 Annual International Meeting
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vegetation indices (NIR-G)/(NIR+G) and NIR/G have good correlation. Among them, the linear prediction model of (NIRG)/(NIR+G) possesses the optimal correlation coefficient of 0.7354. Table.2 Study on Linear Prediction Model of SPAD based on Different Vegetation Indices Index
Function
Rc
RMSEC
Rp
RMSEP
NIR/R
y = 4.0177x + 18.878
0.5659
1.6747
0.6896
1.8750
NIG/G
y = 34.014x - 3.5774
0.7500
1.3435
0.7349
1.7299
G/R
y = 2.464x + 23.901
0.2722
1.9544
0.5473
2.2297
(NIR-R)/(NIR+R)
y = 32.672x + 14.667
0.5590
1.6843
0.6922
1.8744
(NIR-G)/(NIR+G)
y = 69.731x + 30.457
0.7502
1.3432
0.7354
1.7272
(G-R)/(G+R)
y = 18.671x + 21.983
0.2637
1.9592
0.5565
2.2301
2.2 Influence of Image Acquisition Height and Velocity on the Model In order to analyze the influence of image acquisition height and velocity on the model, the rule of influence of single element was explored via test by controlling the acquisition height and velocity. Under each acquisition condition, the SPAD linear prediction model was established based on 6 vegetation indices, and the vegetation indices were optimized according to the performance of the model, as shown in Table 3. As for the data in row 1 to 3, the influence of the height was explored under certain conditions. As for the data in row 3 to 5, the influence of velocity was explored under certain conditions. It can be seen from Table 3 that the optimal vegetation indices changed with the increase of acquisition height when the acquisition velocity is 0.1m/s, and the correlation coefficient of the model prediction set was increased from 0.5566 to 0.7354, indicating that the image acquisition height has relatively significant influence on the performance of model. When the acquisition height is 1.5m, the model possessed poor performance, it is probably because that the lens are too close to the canopy, and the definitions of images at different layers of the canopy were significantly different from each other. When the acquisition height is 1.9m, the optimal vegetation indices were all (NIR-G)/(NIR+G), and the correlation coefficient of the model prediction set was decreased from 0.7354 to 0.6928, indicating that the image acquisition velocity has certain influence on the performance of model. When the exposure time keeps unchanged, the camera velocity increases and imaging quality reduces, which further influences the performance of final model. Table 3 Study on SPAD Linear Prediction Model of Optimal Vegetation Index based on Different Collection of Height and Velocity
3
Height
Velocity
Optimization Index
Function
Rc
RMSEC
Rp
RMSEP
1.5 1.7
0.1
NIR/R
y=3.3371x+19.594
0.6019
1.4984
0.5566
2.3990
0.1
NIR/G
y=30.651x+0.054
0.7319
1.4241
0.7103
1.7019
1.9
0.1
(NIR-G)/(NIR+G)
y=69.731x+30.457
0.7404
1.3432
0.7354
1.7272
1.9
0.2
(NIR-G)/(NIR+G)
y=68.421x+30.511
0.7250
1.4016
0.7192
1.6863
1.9
0.3
(NIR-G)/(NIR+G)
y=64.856x+30.741
0.7362
1.4385
0.6928
1.6864
Conclusion
In this research, SPAD value was used as the evaluation criteria for nitrogen content, and the UAV low altitude remote sensing platform was then used to carry the multi-spectral camera to obtain the nutrient information about the rape canopy at seedling stage in a bid to explore the feasibility of establishing the linear analytical model of canopy SPAD and analyze the influence of image acquisition height and velocity on the model prediction. The results show that the image acquisition height and velocity may affect the prediction model to different extents, and that the prediction performance of the model may be improved to different extents with the increase of image acquisition height and reduction of image acquisition velocity. The optimal performance was achieved in the third acquisition, i.e. the height and velocity are 1.9m and 0.1m/s respectively, i.e. the correlation coefficient of the prediction set Rp reached 0.7354. This research lays a theoretical foundation for rapidly obtaining the large-scale rape canopy nitrogen information via UAV platform-based low altitude multi-spectral remote sensing technology in the future.
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