Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery

The use of a low-cost five-band multispectral camera (RedEdge, MicaSense, USA) and a low-altitude airborne platform is investigated for the detection of plant stress caused by yellow rust disease in winter wheat for sustainable agriculture. The research is mainly focused on: (i) determining whether or not healthy and yellow rust infected wheat plants can be discriminated; (ii) selecting spectral band and Spectral Vegetation Index (SVI) with a strong discriminating capability; (iii) developing a low-cost yellow rust monitoring system for use at farmland scales. An experiment was carefully designed by infecting winter wheat with different levels of yellow rust inoculum, where aerial multispectral images under different developmental stages of yellow rust were captured by an Unmanned Aerial Vehicle at an altitude of 16–24m with a ground resolution of 1–1.5cm/pixel. An automated yellow rust detection system is developed by learning (via random forest classifier) from labelled UAV aerial multispectral imagery. Experimental results indicate that: (i) good classification performance (with an average Precision, Recall and Accuracy of 89.2%, 89.4% and 89.3%) was achieved by the developed yellow rust monitoring at a diseased stage (45 days after inoculation); (ii) the top three SVIs for separating healthy and yellow rust infected wheat plants are RVI, NDVI and OSAVI; while the top two spectral bands are NIR and Red. The learnt system was also applied to the whole farmland of interest with a promising monitoring result. It is anticipated that this study by seamlessly integrating low-cost multispectral camera, low-altitude UAV platform and machine learning techniques paves the way for yellow rust monitoring at farmland scales

Illustration of extracted skeleton by ACTSE and tractography result by SLT on simulated phantom.

<p>(a) shows the ideal central line of simulated fiber tract overlaid on FA map (in blue). Light blue region and yellow region are defined as the starting and ending regions for the SLT. These regions also served as the constraints for ACTSE. (b) and (c) were reconstructed with added noise <i>sd = 0.003</i> (<i>SNR = 31.28</i>) and PVE level <i>t = 2</i>; (d) and (e) were reconstructed with noise <i>sd = 0.009</i> (<i>SNR = 10.67</i>) and <i>t = 2</i>. (b) and (d) show the fiber tractography results by SLT in red; (c) and (e) show the extracted tract skeleton by ACTSE in green. In (b), (c), (d) and (e), the blue rectangles amplified the same part of the results.</p

Results illustration of ACTSE and SLT of cingulum at different noise levels from sagittal view.

<p>(a), (b) and (c) are the volume data with 3 different noise levels; they were derived from the averages of 1, 3, and 5 acquisitions of subject 4 respectively. From (a), (b) to (c), SNR increased. Green shows tract skeleton extraction results by ACTSE, and red shows the fiber tractography results by SLT correspondingly.</p

Illustration of locations for SD analysis.

<p>Blue and red show the locations on AC and PC for SD analysis respectively.</p

Illustration of parameters in the fourth item in <i>E_img</i>.

<p>. Right round region is the enlargement of the dark region in Left image. <i>H</i> represents the common region of the tract's cross dissection at point <i>P(n,τ)</i> (blue point) along principle direction and the sphere with radius <i>r</i> (green line) at point <i>P(n,τ)</i>. <i>H_fa</i> represents the region in <i>H</i> with <i>I_fa</i>>0.1. <i>P_H,fa(n,τ)</i> (red point) represents the center of <i>H_fa</i>.</p

Results illustration of ACTSE and SLT of cingulum at the same noise level from sagittal view.

<p>From top to bottom row, they are 6 different volume data with the same noise level. All of them come from the average of random 3 acquisitions of subject 4. Green shows the tract skeleton extraction results by ACTSE (left column) and red shows the fiber tractography results by SLT (right column) correspondingly.</p

Illustration of skeleton extraction by ACTSE and fiber tracking by SLT for right cingulum.

<p>(a), (b), (c) and (g) are from subject 4; (d), (e), (f) and (h) are from subject 6. (a) and (d) show FA color map. (b) and (e) show tract skeleton extraction results of ACTSE (in green) from sagittal view; (c) and (f) show fiber tractography results by SLT (in red) from sagittal view. (g) and (h) show the results of ACTSE and SLT in 3D view.</p

Illustration of contour evolution for ACTSE.

<p>Illustration of contour evolution for ACTSE.</p

Illustration of 3D skeleton extraction of right cingulum bundle by ACTSE from sagittal view.

<p>(a) shows the reference curve of cingulum skeleton (in blue). (b) illustrates the anatomical structure of cingulum on color FA map. Green indicates anterior-posterior; red, left-right; blue, superior-inferior. Cingulum is arching over the corpus callosum. Three ROIs were manually defined on the front end of anterior cingulum, the middle of cingulum, and the end of posterior cingulum for every subject. These regions serve as the constraints of tract skeleton extraction for ACTSE method; for SLT method, these regions also serve as seed ROI (yellow) and end ROIs (pink) for fiber tracking. (c) show the initial curves for skeleton searching by ACTSE of 6 subjects overlaid on sagittal FA maps.. (d) show the extracted cingulum skeletons of 6 subjects overlaid on sagittal FA maps.</p

Mean error between the skeleton extracted by ACTSE or the center line of tracked fiber tract by SLT and the ideal tract center line at different noise or PVE levels on simulated curve fiber phantom data.

a<p>The phantom data was filtered by mean filter with window 3×3×3 in variable times (<i>N</i> = 1, 2, 3) to simulate different PVE levels.</p>b<p>Random noise with standard deviation (SD = 0.003,0.006,0.009,0.012,0.015) were added to phantom data to simulate different noise levels.</p