1. Spectral Weed Detection and Precise Spraying Laboratory of AgroMachinery and Processing Els Vrindts, Dimitrios Moshou, Jan Reumers Herman Ramon, Josse De Baerdemaeker Katholieke Universiteit Research sponsored by IWT and the Belgian Ministry of Small Trade and Agriculture

2. Katholieke Universiteit Leuven Overview Spectral measurements of crops and weeds in laboratory in field Processing of spectral data with neural networks Precise spraying

3. Katholieke Universiteit Leuven Optical detection of weeds Techniques red/NIR detectors (vegetation index) image processing (color, texture, shape) remote sensing of weed patches reflection in visible & NIR light different detection possibilities, different scales Requirements for on-line weed detection: fast & accurate weed detection synchronized with treatment

4. Katholieke Universiteit Leuven Spectral weed detection Factors affecting spectral plant signals leaf reflection, dependent on species and environment, stress, disease canopy & measurement geometry light conditions detector sensitivity

5. Katholieke Universiteit Leuven Spectral analysis of plant leaves in laboratory sample spectrophotometer integrating sphere computer Diffuse Reflectance Spectroscopy of Crop and Weed Leaves Laboratory measurements

6. Katholieke Universiteit Leuven Diffuse Reflectance of a Leaf Laboratory measurements

7. Katholieke Universiteit Leuven Spectral Dataset Laboratory measurements

8. Katholieke Universiteit Leuven Reflectance of crop and weed leaves Laboratory measurements

9. Katholieke Universiteit Leuven Spectral analysis stepwise selection of discriminant wavelengths multivariate discriminant analysis, based on reflectance response at selected wavelengths (dataset a) assuming multivariate normal distribution quadratic discriminant rule classes with different covariance structure testing the discriminant function: classification of spectra from dataset b Laboratory measurements

10. Katholieke Universiteit Leuven Spectral response of beet & weeds Laboratory measurements

11. Katholieke Universiteit Leuven Laboratory measurements Spectral response of maize & weeds

12. Katholieke Universiteit Leuven Spectral response of potato & weeds Laboratory measurements

13. Katholieke Universiteit Leuven Classification results Laboratory measurements

14. Field measurement of crop and weeds Variation in light condition Measurement geometry Detector sensitivity Processing method Signal path Field measurements

15. Katholieke Universiteit Leuven Equipment for field measurement spectrograph + 10-bit CCD, digital camera, computer, 12 V battery and transformer on mobile platform Field measurements

16. Katholieke Universiteit Leuven Equipment - Spectrograph both spatial and spectral information in images Field measurements

17. Katholieke Universiteit Leuven Image data maize, sugarbeet, 11 weeds 2 different days, different light conditions 755 x 484 pixels spatial axis spectral axis Field measurements

18. Katholieke Universiteit Leuven Spectral response of sensor Field measurements

19. Katholieke Universiteit Leuven Data processing spectral resolution: 0.71 nm /pixel plant/soil discrimination with ratio: NIR (745 nm) / red (682 nm) data reduction by calculating average per 2.1 nm, removing noisy ends resulting spectra: 484.8 - 814.6 nm range, 2.1 nm step independent datasets of maize, sugarbeet and weeds Field measurements

20. Katholieke Universiteit Leuven Spectral datasets Field measurements

21. Katholieke Universiteit Leuven Mean canopy reflections Field measurements

22. Canonical analysis of Sugarbeet - weeds Field measurements

23. Canonical analysis of Maize - weeds Field measurements

24. Katholieke Universiteit Leuven Discriminant analysis Sugarbeet Field measurements

25. Katholieke Universiteit Leuven Discriminant analysis Maize Field measurements

26. Graphic comparison datasets Field measurements

27. Graphic comparison datasets Field measurements

28. Katholieke Universiteit Leuven Graphic comparison datasets Field measurements

29. Discriminant analysis ratios Sugarbeet Field measurements

30. Discriminant analysis ratios Maize Field measurements

31. Katholieke Universiteit Leuven Results only spectral info (485-815 nm) classification based on narrow bands in discriminant functions good results in similar light and crop conditions large decrease in performance for other light conditions using ratios of narrow bands improvement, but not sufficient Field measurements

32. Katholieke Universiteit Leuven Improving results influence of light conditions adaption of classification rule determining light condition and applying appropriate calibration/LUT spectral inputs that are less affected by environment measuring irradiance, calculating reflectance other classification methods Field measurements

33. Katholieke Universiteit Leuven Neural network for classification Comparison of different NN techniques for classification Self-Organizing Map (SOM) neural network for classification used in a supervised way for classification neurons of the SOM are associated with local models achieves fast convergence and good generalisation. Crop-weed classification

34. Neural lattice (A) Input Space (V) SOMMLP class s3(k) first hidden layer s2(k)s4(k)s1(k) …. second hidden layer weights PNN ADVANTAGES Learns with reduced amounts of data Fast Learning Visualisation Retrainable DISADVANTAGES Discrete output ADVANTAGES Good extrapolation DISADVANTAGES Slow Learning Local minima Needs a lot of data ADVANTAGES Fast Learning Retrainable DISADVANTAGES Needs all training data during operation Needs a lot of data Crop-weed classification Neural network for classification

35. Crop-weed classification Comparison between methods MLP: Multi-Layer Perceptron, PNN: Probabilistic N Network, SOM: Self- Organizing Map, LVQ: Learning Vector Qantization, LLM: Local Linear Mapping Moshou et al., 1998, AgEng98, Oslo Moshou et al., 2001, Computers and Electronics in Agriculture 31 (1): 5-16

36. Katholieke Universiteit Leuven MLP: Multi-Layer Perceptron, PNN: Probabilistic N Network, SOM: Self- Organizing Map, LVQ: Learning Vector Qantization, LLM: Local Linear Mapping Comparison between methods Crop-weed classification

37. Katholieke Universiteit Leuven Crop-weed classification MLP: Multi-Layer Perceptron, PNN: Probabilistic N Network, SOM: Self- Organizing Map, LVQ: Learning Vector Qantization, LLM: Local Linear Mapping Comparison between methods

38. Katholieke Universiteit Leuven The strongest point is the local representation of the data accompanied by a local updating algorithm Local updating algorithms assure much faster convergence than global updating algorithms (e.g. backpropagation for MLPs) Because of the topologically preserving character of the SOM, the proposed classification method can deal with missing or noisy data, outperforming “optimal” classifiers (PNN) The proposed method has been tested and gave superior results compared to a variety statistical and neural classifiers Crop-weed classification Conclusions on LLM SOM technique

39. Precision spraying through controlled dose application Unwanted variations in dose caused by horizontal and vertical boom movements Precision treatment

40. Katholieke Universiteit Leuven Active horizontal stabilisation of spray boom Validation with ISO 5008 track movement of spray boom tip with and without controller 051015202530 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 Time (s) Distance (m) Precision treatment

41. Katholieke Universiteit Leuven Vertical stabilisation of spray boom Slow-active system for slopes Resulting boom movement Precision treatment

42. Katholieke Universiteit Leuven On-line selective weed treatment Indoor test of on-line weed detection and treatment Precision treatment

43. Katholieke Universiteit Leuven Sensor: Spectral line camera Classification: Probabilistic neural network Program in Labview with c-code Image acquisition frequence: 10 images/sec, travel speed: 30cm/sec, segmentation with NDVI ( > 0.3) Off-line training of NN, On-line classification Decision to spray: > 20 weed pixels and > 35% of vegetation is weed Spray boom with PWM nozzles and controller, provided by Teejet Technologies Indoor test of on-line weed detection and treatment Precision treatment

44. Indoor test of on-line weed detection and treatment Color image and spectral image Precision treatment

45. Katholieke Universiteit Leuven Indoor test - Results Comparison of nozzle activation with weed positions Precision treatment

46. Katholieke Universiteit Leuven Indoor test - Results camera nozzle weed Experimental set up - separate weed classes (4) did not improve crop-weed classification -Correct detection of nearly all weeds - Only 6 % redundant spraying of crop - Up to 70 % reduction of herbicide use Precision treatment