Automated Canopy Estimator(ACE): Enhancing Crop Modelling and Decision Making in Agriculture. A. D. Coy, D.R. Rankine, M.A. Taylor, and D. C.

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1 Autmated Canpy Estimatr(ACE): Enhancing Crp Mdelling and Decisin Making in Agriculture A. D. Cy, D.R. Rankine, M.A. Taylr, and D. C. Nielsen

2 Outline Mtivatin Evaluatin Results Validatin Cnclusin

3 Mtivatin Hw much f this phtgraph is cvered by crn plants?

4 Mtivatin Several measures f crp develpment can be estimated by determining the amunt f canpy cver (CC); these are useful fr: Determining Nutritinal status Identifying Grwth characteristics Crp identificatin Crp Mdelling

5 Mtivatin Canpy cver is a key input t the AquaCrp mdel Flwchart f AquaCrp indicating the main cmpnents f the sil-plant-atmsphere cntinuum (Stedut, 2008).

6 Mtivatin Estimating CC frm digital phtgraphs is becming increasingly imprtant Using image prcessing sftware, such as Phtshp, it is pssible t segment the green canpy frm the backgrund material This is clearly labur intensive. The issue f subjectivity als arises

7 Mtivatin There exist ther tls fr segmenting images Existing appraches are either: Inaccurate Time cnsuming Subjective, r A cmbinatin f all three The aim was t develp a simple, lw cst, autmated, and accurate methd fr segmenting digital images and estimating canpy cver

8 Existing Appraches Traditinal techniques can be separated int tw grups: threshld based appraches and machine learning appraches Threshlding appraches typically plt a histgram f clur values and attempt t find a value that demarcates green frm backgrund Machine learning appraches use mre sphisticated techniques, such as neural netwrks and ther pattern recgnitin algrithms

9 Existing Appraches A cmparisn f CC estimates (derived frm the pht abve) fr different appraches Apprach Methd Canpy Cver (%) Phtshp Threshld (Grund Truth) 58.5 Clur Index f Vegetatin Extractin (CIVE) Threshld 63.8 Extra Green (ExG) Threshld 33.1 Visual Vegetatin Index (VVI) Threshld 31.0 Green Crp Tracker Threshld 69.5 Bai et al. (2013) Machine Learning 53.7

10 Limitatins f Current Appraches They d nt wrk well when phts taken with multiple cameras are analysed tgether Mst are very sensitive t differences in illuminatin Clsed and pen canpies are difficult t segment Nt all are fully autmated Sme need t be retrained fr new crps

11 ACE Autmated Canpy Estimatr (ACE) is a threshlding apprach that vercmes many f the limitatins listed abve ACE differs frm ther threshlding techniques in tw aspects: The methd f estimating the threshld, and The clur space in which it perfrms segmentatin

12 ACE ACE wrks in the CIE L*a*b* clur space Lab space separates illuminance frm clur infrmatin The a* channel is f interest because it represents clurs n the spectrum frm red t green ACE extracts the a* channel and prcesses it in rder t segment green frm backgrund

13 ACE The prbability distributin f a* values is pltted and the threshld is determined t be the pint n the x-axis that best delineates green and backgrund

14 ACE The threshld isn t always bvius, s ACE estimates a Gaussian mixture mdel frm the data If the distributin is nt strngly bi-mdal, the inflectin pint is detected and used as the threshld

15 Evaluatin Fur separate crps, phtgraphed with tw different cameras are chsen fr evaluatin. The crps are at, flax, crn and rapeseed. The fur are chsen because they have different leaf shapes, sizes, rientatins, a variety f grwth patterns and are phtgraphed under varying lighting cnditins 80 images are included in the evaluatin

16 Results Demnstratin f the segmentatin utput frm ACE Un-segmented Segmented Oat Rapeseed Flax Crn

17 Results - Segmentatin Cmparisn f mean segmentatin accuracy estimated by ACE and eight ther tls Crn Oat Flax Rapeseed µ(%) σ (%) µ(%) σ (%) µ(%) σ (%) µ(%) σ (%) CIVE ExG VVI MS MSCIVE MSExG MSVVI Bai et al ACE

18 Results - Segmentatin A graphical representatin f the cmparisn f segmentatin accuracy estimated by ACE and eight ther tls

19 Results Canpy Cver Cmparisn f grund truth and ACE-estimated canpy cver values fr crn (a), rapeseed (b), flax (c) and at (d) a) b) c) d)

20 Validatin AquaCrp Simulatin TOP - Simulated (line) versus measured (filled circles) canpy cver BOTTOM - Bimass (filled squares) f sweet ptat fr rainfed and irrigated treatments at Devn, Manchester (2012). CC measured using the Green Crp tracker (Liu and Pattey, 2010)

21 Validatin AquaCrp Simulatin TOP - Simulated (line) versus measured (filled circles) canpy cver BOTTOM - Bimass (filled squares) f sweet ptat fr rainfed and irrigated treatments at Devn, Manchester (2012). CC measured using ACE

22 Validatin AquaCrp Simulatin Canpy cver estimates are used in the parameterizatin f Sweet ptat in AquaCrp (Rankine et al., 2015) With CC measured by ACE there is excellent agreement between simulated and measured CC AquaCrp creditably simulates the bimass at bth lcatins and fr the tw treatments using the input f CC estimated by ACE When CC is well estimated, Bimass is well simulated

23 Cnclusins ACE is an accurate methd fr segmenting digital phtgraphs and estimating CC It vercmes many f the limitatins f previus appraches It is an autmated methd fr estimating CC frm digital phtgraphs and is ptentially beneficial fr many applicatins, including crp mdelling.

24 In case yu were wndering Apprach Methd Canpy Cver (%) Phtshp Threshld (Grund Truth) 58.5 Clur Index f Vegetatin Extractin (CIVE) Threshld 63.8 Extra Green (ExG) Threshld 33.1 Visual Vegetatin Index (VVI) Threshld 31.0 Green Crp Tracker Threshld 69.5 Bai et al. (2013) Machine Learning 53.7 ACE Threshld 58.6

25 Availability f ACE ACE is nline:

UFuRT: A Work-Centered Framework and Process for Design and Evaluation of Information Systems

UFuRT: A Work-Centered Framework and Process for Design and Evaluation of Information Systems In: Prceedings f HCI Internatinal 2007 UFuRT: A Wrk-Centered Framewrk and Prcess fr Design and Evaluatin f Infrmatin Systems Jiajie Zhang 1, Keith A. Butler 2 1 University f Texas at Hustn, 7000 Fannin,