Potential use of ground-based sensor technologies for weed detection

Pest Management Science

Volumn 70, Issue 2, 2014, Pages 190-199

  Peteinatos, Gerassimos G ^a   Weis, Martin ^a   Andújar, Dionisio ^a   Rueda Ayala, Victor ^a   Gerhards, Roland ^a  

^a UNIVERSITY OF HOHENHEIM   (Germany)

Author keywords

Distance Sensors; Optical Sensors; Site Specific Weed Management; Weed Detection

Indexed keywords

GROUND-BASED MEASUREMENT; MORPHOLOGY; PHYSIOLOGY; PRECISION AGRICULTURE; SENSOR; WEED CONTROL;

DISTANCE SENSORS; EQUIPMENT; FLUORESCENCE IMAGING; OPTICAL SENSORS; REVIEW; SITE SPECIFIC WEED MANAGEMENT; SOIL; WEED CONTROL; WEED DETECTION;

DISTANCE SENSORS; OPTICAL SENSORS; SITE SPECIFIC WEED MANAGEMENT; WEED DETECTION;

OPTICAL IMAGING; SOIL; WEED CONTROL;

EID: 84892476719     PISSN: 1526498X     EISSN: 15264998     Source Type: Journal    
DOI: 10.1002/ps.3677     Document Type: Review

References (119)

1. Timmermann C, Gerhards R and Kühbauch W, The economic impact of the site-specific weed control. Precis Agric 4:249-260 (2003).
2. Ehlert D, Adamek R and Horn HJ, Laser rangefinder-based measuring of crop biomass under field conditions. Precis Agric 10:395-408 (2009).
3. Christensen S, Søgaard HT, Kudsk P, Nørremark M, Lund I, Nadimi ES, et al, Site-specific weed control technologies. Weed Res 49:233-241 (2009).
4. López-Granados F, Weed detection for site-specific weed management: mapping and real-time approaches. Weed Res 51:1-11 (2011).
5. Weis M, Gutjahr C, Rueda Ayala V, Gerhards R, Ritter C and Schölderle F, Precision farming for weed management: techniques. Gesunde Pflanzen 60:171-181 (2008).
6. Dille JA, Mortensen DA and Young LJ, Predicting weed species occurrence based on site properties and previous year's weed presence. Precis Agric 3:193-207 (2002).
7. Marshall EJP, Field-scale estimates of grass populations in arable land. Weed Res 28:191-198 (1998).
8. Gerhards R and Christensen S, Real-time weed detection, decision making and patch spraying in maize, sugar beet, winter wheat and winter barley. Weed Res 43:385-392 (2003).
9. Cousens R and Mortimer M, Dynamics of Weed Populations (1st edn). Cambridge University Press, Cambridge, UK (1995).
10. Gerhards R, Wyse-Pester DY, Mortensen DA and Johnson GA, Characterizing spatial stability of weed populations using interpolated maps. Weed Sci 45:108-119 (1997).
11. Lindquist JL, Dieleman JA, Mortensen DA, Johnson GA and Wyse Pester DY, Economic importance of managing spatially heterogeneous weed populations. Weed Technol 12:7-13 (1998).
12. Christensen S, Heisel T, Walter AM and Graglia E, A decision algorithm for patch spraying. Weed Res 43:276-284 (2003).
13. Gerhards R and Oebel H, Practical experiences with a system for site-specific weed control in arable crops using real-time image analysis and GPS-controlled patch spraying. Weed Res 46:185-193 (2006).
14. Chapron M, Requena-Esteso M, Boissard P and Assemat L, A method for recognizing vegetal species from multispectral images, in Precision Agriculture 1999. Part 1 of 2nd European Conference on Precision Agriculture, Sheffield, UK, ed. by Stafford J. Sheffield Academic Press, Sheffield, UK, pp. 239-248 (1999).
15. Goel PK, Prasher SO, Patel RM, Smith DL and DiTommaso A, Use of airborne multi-spectral imagery for weed detection in field crops, Trans ASABE 45:443-449 (2002).
16. Metternicht G, Vegetation indices derived from high-resolution airborne videography for precision crop management. Int J Remote Sens 24:2855-2877 (2003).
17. Ustin SL, Roberts DA, Gamon JA, Asner GP and Green RO, Using imaging spectroscopy to study ecosystem processes and properties. BioScience 54:523-534 (2004).
18. Backes M and Jacobi J, Classification of weed patches in quickbird images: verification by ground truth data. EARSeL eProceedings 5:173-179 (2006).
19. Medlin CR, Shaw DR, Gerard PD and LaMastus FE, Using remote sensing to detect weed infestations in Glycine max. Weed Sci 48:393-398 (2000).
20. Bajwa SG and Tian LF, Aerial CIR remote sensing for weed density mapping in a soybean field. Trans Am Soc Agric Eng 44:1965-1974 (2001).
21. Andújar D, Àngela Ribeiro, Fernàndez-Quintanilla C and Dorado J, Accuracy and feasibility of optoelectronic sensors for weed mapping in wide row crops. Sensors 11:2304-2318 (2011).
22. Thorp KR and Tian LF, A review on remote sensing of weeds in agriculture. Precis Agric 5:477-508 (2004).
23. Borregaard T, Nielsen H, Nörgaard L and Have H, Crop-weed discrimination by line imaging spectroscopy. J Agric Eng Res 75:389-400 (2000).
24. Lamb DW and Brown RB (2000). Remote-sensing and mapping of weeds in crops. J Agric Eng Res 78:117-125 (2001).
25. Noble SD, Brown RB and Crowe TG, Design and evaluation of an imaging spectrophotometer incorporating a uniform light source. Rev Sci Instrum 83: 9pp (2012).
26. Weis M and Sökefeld M, Detection and identification of weeds, in Precision Crop Protection: the Challenge and Use of Heterogeneity, Vol. 1 (1st edn), Oerke EC, Gerhards R, Menz G and Sikora RA (eds). Springer, Dordrecht, pp. 119-134 (2010).
27. Gitelson AA and Merzlyak MN, Remote estimation of chlorophyll content in higher plant leaves. Int J Remote Sens 18:2691-2697 (1997).
28. Ustin SL, DiPietro D, Olmstead K, Underwood E and Scheer GJ, Hyperspectral remote sensing for invasive species detection and mapping, in Geoscience and Remote Sensing Symposium, 2002 (IGARSS '02), Vol. 3. IEEE International, pp. 1658-1660 (2002).
29. Moshou D, Ramon H and De Baerdemaeker J, A weed species spectral detector based on neural networks. Precis Agric 3:209-223 (2002).
30. Asner GP, Knox RG, Green RO and Ungar SG, The Flora mission for ecosystem composition, disturbance and productivity. Technical Report Document 20050180313. Center for AeroSpace Information, NASA, Hanover, MD (2005).
31. Scotford I and Miller P, Applications of spectral reflectance techniques in northern European cereal production: a review. Biosyst Eng 90:235-250 (2005).
32. Weber C, Schinca DC, Tocho JO and Videla F, Passive field reflectance measurements. J Optics A 10: 8pp (2008).
33. Glenn EP, Huete AR, Nagler PL and Nelson SG, Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensors 8:2136-2160 (2008).
34. Fitzgerald GJ, Characterizing vegetation indices derived from active and passive sensors. Int J Remote Sens 31:4335-4348 (2010).
35. Vrindts E and de Baerdemaeker J, Optical discrimination of crop, weed and soil for on-line weed detection, in 1st European Conference on Precision Agriculture, Vol. 1. BIOS Scientific, Oxford, pp. 537-544 (1997).
36. Biller RH, Reduced input of herbicides by use of optoelectronic sensors. J Agric Eng Res 71:357-362 (1998).
37. Marchant JA and Onyango CM, Comparison of a Bayesian classifier with a multilayer feed-forward neural network using the example of plant/weed/soil discrimination. Comput Electron Agric 39:3-22 (2003).
38. Kavdr I, Discrimination of sunflower, weed and soil by artificial neural networks. Comput Electron Agric 44:153-160 (2004 ).
39. Piron A, Leemans V, Kleynen O, Lebeau F and Destain MF, Selection of the most efficient wavelength bands for discriminating weeds from crop. Comput Electron Agric 62:141-148 (2008).
40. Piron A, Leemans V, Lebeau F and Destain M, Improving in-row weed detection in multispectral stereoscopic images. Comput Electron Agric 69:73-79 (2009).
41. Vigneau N, Ecarnot M, Rabatel G and Roumet P, Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in wheat. Field Crops Res 122:25-31 (2011).
42. Brown RB and Noble SD, Site-specific weed management: sensing requirements - what do we need to see? Weed Sci 53:252-258 (2005).
43. Čepl J and Kasal P, Weed mapping - a way to reduce herbicide doses. Potato Res 53:359-371 (2010).
44. Sui R, Thomasson JA, Hanks J and Wooten J, Ground-based sensing system for weed mapping in cotton. Comput Electron Agric 60:31-38 (2008).
45. Felton WL and McCloy KR, Spot spraying: microprocessor-controlled, weed-detecting technology helps save money and the environment. Agric Eng 73:9-12 (1992).
46. Dammer KH and Wartenberg G, Sensor-based weed detection and application of variable herbicide rates in real time. Crop Prot 26:270-277 (2007).
47. Dammer KH, Wartenberg G and Weinhold F, Differentiation of application rate in sensor based herbicide application under heterogenic age structure of weeds, in Weed Biology and Weed Control: 24th German Conference on Weed Biology and Weed Control, ed. by Gerhards R, Belz RG. Eugen Ulmer, Stuttgart-Hohenheim, p. 676 (2008).
48. Link A, Panitzki M and Reusch S, Hydro N-Sensor: tractor-mounted remote sensing for variable nitrogen fertilization, in Proceedings of the 6th International Conference on Precision Agriculture and Other Precision Resources Management, Minneapolis, MN, ed. by Robert PC et al. American Society of Agronomy pp. 1012-1017 (2003).
49. Hong S, Schepers JS, Francis DD and Schlemmer MR, Comparison of ground-based remote sensors for evaluation of corn biomass affected by nitrogen stress. Commun Soil Sci Plant Anal 38:2209-2226 (2007).
50. Tremblay N, Wang Z, Ma BL, Belec C and Vigneault P, A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application. Precis Agric 10:145-161 (2009).
51. Komives T and Reisinger P, Precision weed control in sunflower and maize: experiences from Hungary, in Proceedings of the 25th German Conference on Weed Biology and Weed Control, Vol. 434 of Julius-Kühn-Archiv, ed. by Nordmeyer H and Ulber. Julius-Kühn-Institut, Braunschweig, pp. 207-214 (2012).
52. Young FL, Yenish JP, Launchbaugh GK, McGrew LL and Richard Alldredge J, Postharvest control of Russian thistle (Salsola tragus) with a reduced herbicide applicator in the Pacific Northwest. Weed Technol 22:156-159 (2008).
53. Cerovic ZG, Samson G, Morales F, Tremblay N and Moya I, Ultraviolet-induced fluorescence for plant monitoring: present state and prospects. Agronomie 19:543-578 (1999).
54. Krause GH and Weis E, Chlorophyll fluorescence and photosynthesis: the basics. Annu Rev Plant Physiol Plant Mol Biol 42:313-349 (1991).
55. Jansen M, Gilmer F, Biskup B, Nagel KA, Rascher U, Fischbach A, et al, Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants. Funct Plant Biol 36:902-914 (2009).
56. Chappelle EW, Wood FM Jr, Newcomb WW and McCurtrey JE III, Laser-induced fluorescence of green plants. 3: LIF spectral signatures of five major plant types. Appl Opt 24:74-80 (1985).
57. Tyystjärvi E, Nørremark M, Mattila H, Keränen M, Hakala-Yatkin M, Ottosen C-O, et al, Automatic identification of crop and weed species with chlorophyll fluorescence induction curves. Precis Agric 12:546-563 (2011).
58. Longchamps L, Panneton B, Samson G, Leroux G and Thériault R, Discrimination of corn, grasses and dicot weeds by their UV-induced fluorescence spectral signature. Precis Agric 11:181-197 (2009).
59. Tyystjärvi E, Koski A, Keränen M and Nevalainen O, The Kautsky curve is a built-in barcode. Biophys J 77:1159-1167 (1999).
60. Gerhards R, Weis M, Gutjahr C, Schulz J and Jancker H, Untersuchungen zur automatisierten Unkrauterkennung in Kulturpflanzenbeständen mit Hilfe des Sensorsystems MiniVeg, in 17th Workshop Computer-Bildanalyse in der Landwirtschaft, Vol. 78 of Bornimer Agrartechnische Berichte. ATB - Leibniz-Institut für Agrartechnik Potsdam-Bornim e.V. Ihinger Hof, Potsdam-Bornim and Renningen, pp. 29-40 (2012).
61. Subramanian V, Burks TF and Arroyo AA, Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation. Comput Electron Agric 53:130-143 (2006).
62. Reusch S, Use of ultrasonic transducers for on-line biomass estimation in winter wheat, in European Conference on Precision Agriculture: JIAC 2009 Book of abstracts, ed. by Lokhorst C, Huijsmans JFM and De Louw RPM, Wageningen Academic Publishers, Wageningen, pp. 169-175 (2009).
63. Andújar D, Escolà A, Dorado J and Fernández-Quintanilla C, Weed discrimination using ultrasonic sensors. Weed Res 51:543-547 (2011).
64. Zhang L and Grift TE, A LIDAR-based crop height measurement system for Miscanthus giganteus. Comput Electron Agric 85:70-76 (2012).
65. Rosell JR and Sanz R, A review of methods and applications of the geometric characterization of tree crops in agricultural activities. Comput Electron Agric 81:124-141 (2012).
66. Dworak V, Selbeck J and Ehlert D, Ranging sensors for vehicle-based measurement of crop stand and orchard parameters: a review. Trans ASABE 54:1497-1510 (2011).
67. Llorens J, Gil E, Llop J and Escolà A, Ultrasonic and LIDAR sensors for electronic canopy characterization in vineyards: advances to improve pesticide application methods. Sensors 11:2177-2194 (2011).
68. Saeys W, Lenaerts B, Craessaerts G and Baerdemaeker JD, Estimation of the crop density of small grains using LiDAR sensors. Biosyst Eng 102:22-30 (2009).
69. Andújar D, Escolà A, Rosell-Polo JR, Fernández-Quintanilla C and Dorado J, Potential of a terrestrial LiDAR-based system to characterize weed vegetation in maize crops. Comput Electron Agric 92:11-15 (2013).
70. Busemeyer L, Mentrup D, Möller K, Wunder E, Alheit K, Hahn V, et al, BreedVision: a multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors 13:2830-2847 (2013).
71. Andújar D, Weis M and Gerhards R, An ultrasonic system for weed detection in cereal crops. Sensors 12:17343-17357 (2012).
72. Andújar D, Weis M and Gerhards R, Ultrasonic measurements for weed detection in cereal crops, in Proceedings of the First RHEA International Conference on Robotics and Associated High-Technologies and Equipment for Agriculture, Pisa, Italy, ed. by Peruzzi A. Pisa University Press, pp. 287-292 (2012).
73. Lee WS, Alchanatis V, Yang C, Hirafuji M, Moshou D and Li C, Sensing technologies for precision specialty crop production. Comput Electron Agric 74:2-33 (2010).
74. Sökefeld M, Gerhards R, Oebel H and Therburg RD, Image acquisition for weed detection and identification by digital image analysis, in 6th European Conference on Precision Agriculture (ECPA), ed. by Stafford JV. Wageningen Academic Publishers, Wageningen, pp. 523-529 (2007).
75. Rabatel G, Gorretta N and Labb S, Getting simultaneous red and near infrared bands from a single digital camera for plant monitoring applications, in Proceedings of the International Conference of Agricultural Engineering (CIGR-AgEng 2012), p. C1122.
76. Dworak V, Selbeck J, Dammer KH, Hoffmann M, Zarezadeh AA and Bobda C, Strategy for the development of a smart NDVI camera system for outdoor plant detection and agricultural embedded systems. Sensors 13:1523-1538 (2013).
77. Woebbecke DM, Meyer GE and von Bargen K, Shape features for identifying young weeds using image analysis. Trans Am Soc Agric Eng 38:271-281 (1995).
78. Woebbecke DM, Meyer GE, von Bargen K and Mortensen DA, Color indices for weed identification under various soil, residue and lighting conditions. Trans Am Soc Agric Eng 38:259-269 (1995).
79. Zhang NC, Effective criteria for weed identification in wheat fields using machine vision. Trans Am Soc Agric Eng 38:965-974 (1995).
80. Burks TF, Shearer SA, Green JD and Heath JR, Influence of weed maturity levels on species classification using machine vision. Weed Sci 50:802-811 (2002).
81. Slaughter DC, Giles DK and Downey D, Autonomous robotic weed control systems: a review. Comput Electron Agric 61:63-78 (2008).
82. Weis M and Gerhards R, Feature extraction for the identification of weed species in digital images for the purpose of site-specific weed control, in 6th European Conference on Precision Agriculture (ECPA), ed. by Stafford JV. Wageningen Academic Publishers, Wageningen, pp. 537-545 (2007).
83. Rumpf T, Römer C, Weis M, Sökefeld M, Gerhards R and Plümer L, Sequential support vector machine classification for small-grain weed species discrimination with special regard to Cirsium arvense and Galium aparine. Comput Electron Agric 80:89-96 (2012).
84. Swain KC, Nørremark M, Jørgensen RN, Midtiby HS and Green O, Weed identification using an automated active shape matching (AASM) technique. Biosyst Eng 110:450-457 (2011).
85. Persson M and Åstrand B, Classification of crops and weeds extracted by active shape models. Biosyst Eng 100:484-497 (2008).
86. Pastrana J, Active shape models with focus on overlapping problems applied to plant detection and soil pore analysis. [Online]. PhD thesis, Naturwissenschaftliche Fakultät, Gottfried Wilhelm Leibniz Universität Hannover (2012). Available: http://www.bgt-hannover.de/homepagethesis/pastrana_phd_2012.pdf [26 July 2012].
87. Franz E, Gebhardt E and Unklesbay KB, Shape description of completely-visible and partially-occluded leaves for identifying plants in digital images. Trans Am Soc Agric Eng 34:673-681 (1991).
88. Søgaard HT, Weed classification by active shape models. Biosyst Eng 91:271-281 (2005).
89. Shahbudin S, Hussain A, Samad SA, Mustafa MM and Ishak AJ, Optimal feature selection for SVM based weed classification via visual analysis, in TENCON 2010 - IEEE Region 10 Conference, pp. 1647-1650 (2010).
90. van Evert FK, Polder G, van Der Heijden GWAM, Kempenaar C and Lotz LAP, Real-time vision-based detection of Rumex obtusifolius in grassland. Weed Res 49:164-174 (2009).
91. Ishak AJ, Hussain A and Mustafa MM, Weed image classification using Gabor wavelet and gradient field distribution. Comput Electron Agric 66:53-61 (2009).
92. Meyer EG, Mehta T, Kocher MF, Mortensen DA and Samal A, Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Trans ASAE, J Series 41:1189-1197 (1998).
93. Agri Con GmbH, Universität Hohenheim. Intelligenter optischer Sensor für den teilflächenspezifischen Herbizideinsatz im Online-Verfahren (H-Sensor) [Online]. Asentics (2012). Available: http://h-sensor.de/ [3 July 2012].
94. Dimensions Agri AS, DAT sensor website (2010). [Online]. Available: http://www.dimensionsagri.com/ [3 July 2012].
95. Midtiby HS, Laursen MS, Jørgensen RN and Krüger N, MODICOVI - monocot and dicot coverage ratio vision based method for real time estimation of canopy coverage ratio between cereal and dicotyledon weeds. in Proceedings of the 12th International Conference of Agricultural Engineering (CIGR 2012), Valencia, Spain, p. C1016 (2012).
96. Slaughter DC, Giles DK, Fennimore SA and Smith RF, Multispectral machine vision identification of lettuce and weed seedlings for automated weed control. Weed Technol 2008;22:378-384.
97. Dammer KH, Intress J, Beuche H, Selbeck J and Dworak V, Discrimination of Ambrosia artemisiifolia and Artemisia vulgaris by hyperspectral image analysis during the growing season. Weed Res 53:146-156 (2013).
98. Menegat A, Kaiser Y, Stephan A, Ni H and Gerhards R, Chlorophyll fluorescence microscreening as a rapid detection method for herbicide resistance in grass weeds in north China plain winter wheat production systems and beyond, in 23rd Asian-Pacific Weed Science Society Conference - Weed Management in a Changing World. QueenslAustralia, pp. 350-358 (2011).
99. Åstrand B and Baerveldt AJ, Plant recognition and localization using context information, in Proceedings of Mechatronics and Robotics 2004 (MechRob 2004), Special Session: Autonomous Machines in Agriculture. Sascha Eysoldt, Aachen, Germany, pp. 1191-1196 (2004).
100. Nordmeyer H, Patchy weed distribution and site-specific weed control in winter cereals. Precis Agric 7:219-231 (2006).
101. Dammer KH, Böttger H, Wartenberg G and Rosenau R, Echtzeitregelung der Applikationsmenge bei der Herbizidanwendung mit Hilfe eines Kamerasensors, in Proceedings of the 25th German Conference on Weed Biology and Weed Control, ed. by Nordmeyer H, Ulber L. Vol. 434 of Julius-Kühn-Archiv. Julius-Kühn-Institut, Braunschweig, Germany, pp. 191-198 (2012).
102. European Parliament, Council of the EU, Directive 2009/128/EC of the European Parliament and of the Council of 21st October 2009 establishing a framework for Community action to achieve the sustainable use of pesticides (Text with EEA relevance). Offic J EU L 309:71-86 (2009).
103. Jensen JE, Weed control: presence and future - the Danish view, in Proceedings of the 22nd German Conference on Weed Biology and Weed Control (J Plant Dis Prot, Special Issue XIX), German Phytomedical Society (DPG). Eugen Ulmer KG, Stuttgart-Hohenheim, Germany, pp. 19-26 (2004).
104. Andújar D, Ribeiro A, Fernández-Quintanilla C, Dorado J, Reliability of a visual recognition system for detection of johnsongrass (Sorghum halepense) in corn. Weed Technol 25:645-651 (2011).
105. Lee WS, Slaughter DC and Giles DK, Robotic weed control system for tomatoes. Precis Agric 1:95-113 (1999).
106. Søgaard H and Lund I, Application accuracy of a machine vision-controlled robotic micro-dosing system. Biosyst Eng 96:315-322 (2007).
107. Giles DK, Herbicide micro-dosing for weed control in field-grown processing tomatoes. Appl Eng Agric 20:735-744 (2004).
108. Søgaard HT and Lund I, Investigation of the accuracy of a machine vision based robotic micro-spray system, in 5th European Conference on Precision Agriculture, Uppsala, Sweden, ed. by Stafford JV. Wageningen Academic Publishers, Waganingen, pp. 613-619 (2005).
109. Thessler S, Kooistra L, Teye F, Huitu H and Bregt AK, Geosensors to support crop production: current applications and user requirements. Sensors 11:6656-6684 (2011).
110. Lamm RD, Slaughter DC and Giles DK, Precision weed control system for cotton. Trans Am Soc Agric Eng 45:231-238 (2002).
111. Weis M, Andújar D, Peteinatos GG and Gerhards R, Improving the determination of plant characteristics by fusion of four different sensors, in Precision Agriculture '13, Lleida, Catalonia, Spain, ed. by Stafford J. Wageningen Academic Publishers, Wageningen, pp. 63-69 (2013).
112. Šeatović D, Rolf G, Anken T and Holpp M, 3D object recognition, localization and treatment of Rumex obtusifolius in its natural environment, in Proceedings of the 1st International Conference on Machine Control and Guidance, ETH Zürich, Switzerled. by Ingensand H and Stempfhuber W (2008).
113. Adamchuk VI, Rossel RAV, Sudduth KA and Schulze Lammers P, Sensor fusion for precision agriculture, in Sensor Fusion: Foundation and Applications. Intech, Rijeka, Croatia, pp. 27-40 (2011).
114. Sui R and Thomasson JA, Ground-based sensing system for cotton nitrogen status determination. Trans ASABE 49:1983-1991 (2006).
115. Keller M, Zecha C, Weis M, Link-Dolezal J, Gerhards R and Claupein W, Competence center SenGIS: exploring methods for multisensor data acquisition and handling for interdisciplinary research, in 8th European Conference on Precision Agriculture, ed. by Stafford JV. ECPA, Ampthill, UK/Czech Centre for Science and Society, Prague, Czech Republic, pp. 491-500 (2011).
116. Malenovský Z, Mishra KB, Zemek F, Rascher U and Nedbal L. Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence. J Exp Bot 60:2987-3004 (2009).
117. Blackmore BS, Griepentrog H, Fountas S and Gemtos T, A specification for an autonomous crop production mechanization system. Agric Eng Int 9:1-24 (2007).
118. Åstrand B and Baerveldt AJ, An agricultural mobile robot with vision-based perception for mechanical weed control. Auton Robot 13:21-35 (2002).
119. Jensen K, Nielsen SH, Jørgensen RN, Jæger-Hansen CL, Bøgild A, Jacobsen NJ, et al, A low cost modular robotics tool carrier for precision agriculture research, in 11th International Conference on Precision Agriculture, Indianapolis, IN (2012).