Estimation of vegetable crop parameter by multi-temporal UAV-borne images

Remote Sensing

Volumn 10, Issue 5, 2018, Pages

  Moeckel, Thomas ^a   Dayananda, Supriya ^a   Nidamanuri, Rama Rao ^b   Nautiyal, Sunil ^c   Hanumaiah, Nagaraju ^d   Buerkert, Andreas ^a   Wachendorf, Michael ^a  

^a UNIVERSITY OF KASSEL   (Germany)

^b INDIAN INSTITUTE OF SPACE SCIENCE AND TECHNOLOGY   (India)

^c INSTITUTE FOR SOCIAL AND ECONOMIC CHANGE   (India)

^d UNIVERSITY OF AGRICULTURAL SCIENCES   (India)

Author keywords

Biomass; Crop height; Machine learning; Multi spectral; Point clouds; Unmanned aerial vehicles

Indexed keywords

AGRICULTURAL MACHINERY; ANTENNAS; ARTIFICIAL INTELLIGENCE; BIOMASS; ECOLOGY; FRUITS; LEARNING SYSTEMS; UNMANNED AERIAL VEHICLES (UAV); VEGETABLES;

AGRICULTURAL SCIENCE; CROP HEIGHT; MACHINE LEARNING METHODS; MULTI-SPECTRAL; POINT CLOUD; SPATIAL INFORMATIONS; STRUCTURE FROM MOTION; VEGETATION CANOPY;

CROPS;

EID: 85047531441     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10050805     Document Type: Article

References (43)

1. Malambo, L.; Popescu, S.C.; Murray, S.C.; Putman, E.; Pugh, N.A.; Horne, D.W.; Richardson, G.; Sheridan, R.; Rooney, W.L.; Avant, R.; et al. Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery. Int. J. Appl. Earth Observ. Geoinf. 2018, 64, 31-42
2. Zhang, C.; Kovacs, J.M. The application of small unmanned aerial systems for precision agriculture: A review. Precis. Agric. 2012, 13, 693-712
3. Selsam, P.; Schaeper, W.; Brinkmann, K.; Buerkert, A. Acquisition and automated rectification of high-resolution RGB and near-IR aerial photographs to estimate plant biomass and surface topography in arid agro-ecosystems. Exp. Agric. 2017, 53, 144-157
4. Yu, N.; Li, L.; Schmitz, N.; Tian, L.F.; Greenberg, J.A.; Diers, B.W. Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform. Remote Sens. Environ. 2016, 187, 91-101
5. Park, S.; Ryu, D.; Fuentes, S.; Chung, H.; Hernández-Montes, E.; O'Connell, M. Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV). Remote Sens. 2017, 9, 828
6. Johnson, C.K.; Mortensen, D.A.; Wienhold, B.J.; Shanahan, J.F.; Doran, J.W. Site-specific management zones based on soil electrical conductivity in a semiarid cropping system. Agron. J. 2003, 95, 303-315
7. Lati, R.N.; Filin, S.; Eizenberg, H. Estimating plant growth parameters using an energy minimization-based stereovision model. Comput. Electron. Agric. 2013, 98, 260-271
8. Madec, S.; Baret, F.; de Solan, B.; Thomas, S.; Dutartre, D.; Jezequel, S.; Hemmerlé, M.; Colombeau, G.; Comar, A. High-throughput phenotyping of plant height: Comparing unmanned aerial vehicles and ground LiDAR estimates. Front. Plant Sci. 2017, 8, 2002
9. Hoffmeister, D.; Waldhoff, G.; Korres, W.; Curdt, C.; Bareth, G. Crop height variability detection in a single field by multi-temporal terrestrial laser scanning. Precis. Agric. 2016, 17, 296-312
10. Tilly, N.; Hoffmeister, D.; Cao, Q.; Huang, S.; Lenz-Wiedemann, V.; Miao, Y.; Bareth, G. Multitemporal crop surface models: Accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice. J. Appl. Remote Sens. 2014, 8, 083671
11. Fricke, T.; Richter, F.; Wachendorf, M. Assessment of forage mass from grassland swards by height measurement using an ultrasonic sensor. Comput. Electron. Agric. 2011, 79, 142-152
12. Bendig, J.; Bolten, A.; Bennertz, S.; Broscheit, J.; Eichfuss, S.; Bareth, G. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sens. 2014, 6, 10395-10412
13. Leberl, F.; Irschara, A.; Pock, T.; Meixner, P.; Gruber, M.; Scholz, S.; Wiechert, A. Point clouds. Photogramm. Eng. Remote Sens. 2010, 76, 1123-1134
14. Tumbo, S.D.; Salyani, M.; Whitney, J.D.; Wheaton, T.A.; Miller, W.M. Investigation of laser and ultrasonic ranging sensors for measurements of citrus canopy volume. Appl. Eng. Agric. 2002, 18, 367-372
15. Li, W.; Niu, Z.; Chen, H.; Li, D. Characterizing canopy structural complexity for the estimation of maize LAI based on ALS data and UAV stereo images. Int. J. Remote Sens. 2017, 38, 2106-2116
16. Weiss, M.; Baret, F. Using 3D point clouds derived from UAV RGB imagery to describe vineyard 3D macro-structure. Remote Sens. 2017, 9, 111
17. Schirrmann, M.; Giebel, A.; Gleiniger, F.; Pflanz, M.; Lentschke, J.; Dammer, K.-H. Monitoring agronomic parameters of winter wheat crops with low-cost UAV imagery. Remote Sens. 2016, 8, 706
18. Parkes, S.D.; McCabe, M.F.; Al-Mashhawari, S.K.; Rosas, J. Reproducibility of crop surface maps extracted from Unmanned Aerial Vehicle (UAV) derived Digital Surface Maps. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII; Neale, C.M.U., Maltese, A., Eds.; SPIE: Bellingham, WA, USA, 2016
19. Prasad, J.V.N.S.; Rao, C.S.; Srinivas, K.; Jyothi, C.N.; Venkateswarlu, B.; Ramachandrappa, B.K.; Dhanapal, G.N.; Ravichandra, K.; Mishra, P.K. Effect of ten years of reduced tillage and recycling of organic matter on crop yields, soil organic carbon and its fractions in Alfisols of semiarid tropics of southern India. Soil Tillage Res. 2016, 156, 131-139
20. Lowe, D.G. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 2004, 60, 91-110
21. Snavely, N.; Seitz, S.M.; Szeliski, R. Modeling the world from internet photo collections. Int. J. Comput. Vis. 2008, 80, 189-210
22. Westoby, M.J.; Brasington, J.; Glasser, N.F.; Hambrey, M.J.; Reynolds, J.M. 'Structure-from-Motion' photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology 2012, 179, 300-314
23. Mesas-Carrascosa, F.-J.; Torres-Sánchez, J.; Clavero-Rumbao, I.; García-Ferrer, A.; Peña, J.-M.; Borra-Serrano, I.; López-Granados, F. Assessing optimal flight parameters for generating accurate multispectral orthomosaicks by uav to support site-specific crop management. Remote Sens. 2015, 7, 12793-12814
24. Röder, M.; Hill, S.; Latifi, H. Best Practice Tutorial: Technical Handling of the UAV 'DJI Phantom 3 Professional' and Processing of the Acquired Data; Department of Remote Sensing, University of Würzburg: Würzburg, Germany, 2017
25. Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8, 127-150
26. Motohka, T.; Nasahara, K.N.; Oguma, H.; Tsuchida, S. Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sens. 2010, 2, 2369-2387
27. Silva, C.A.; Hudak, A.T.; Klauberg, C.; Vierling, L.A.; Gonzalez-Benecke, C.; de Padua Chaves Carvalho, S.; Rodriguez, L.C.E.; Cardil, A. Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data. Carbon Balance Manag. 2017, 12, 13
28. Næsset, E.; Økland, T. Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sens. Environ. 2002, 79, 105-115
29. Roussel, J.-R.; Auty, D. lidR: Airborne LiDAR Data Manipulation and Visualization for Forestry Applications. Available online: https://CRAN.R-project.org/package=lidR (accessed on 18 May 2018)
30. R Core Team. R: A Language and Environment for Statistical Computing; R Core Team: Vienna, Austria, 2016; Available online: https://www. R-project.org/(accessed on 18 May 2018)
31. Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5-32
32. Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn. 1995, 20, 273-297
33. Liaw, A.;Wiener, M. Classification and Regression by randomForest. R News 2002, 2, 18-22
34. Meyer, D.; Dimitriadou, E.; Hornik, K.; Weingessel, A.; Leisch, F. e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien, 2015. Available online: https://CRAN. Rproject. org/package=e1071 (accessed on 18 May 2018)
35. Li, W.; Niu, Z.; Chen, H.; Li, D.;Wu, M.; Zhao, W. Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecol. Indic. 2016, 67, 637-648
36. Garcia-Gutierrez, J.; Martínez-álvarez, F.; Troncoso, A.; Riquelme, J.C. A comparative study of machine learning regression methods on LiDAR data: A case study. In International Joint Conference SOCO'13-CISIS'13-ICEUTE'13; Herrero, á., Baruque, B., Klett, F., Abraham, A., Snášel, V., de Carvalho, A.C.P.L.F., Bringas, P.G., Zelinka, I., Quintián, H., Corchado, E., Eds.; Springer International Publishing: Cham, Switzerland, 2014; pp. 249-258
37. Horning, N. Random forests: An algorithm for image classification and generation of continuous fields data sets. In Proceeding of the International Conference on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences; Hanoi University of Mining and Geology: Hanoi, Vietnam, 2010
38. Niederheiser, R.; Rutzinger, M.; Bremer, M.; Wichmann, V. Dense image matching of terrestrial imagery for deriving high-resolution topographic properties of vegetation locations in alpine terrain. Int. J. Appl. Earth Observ. Geoinf. 2018, 66, 146-158
39. Cunliffe, A.M.; Brazier, R.E.; Anderson, K. Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sens. Environ. 2016, 183, 129-143
40. Iqbal, F.; Lucieer, A.; Barry, K.; Wells, R. Poppy Crop Height and Capsule Volume Estimation from a Single UAS Flight. Remote Sens. 2017, 9, 647
41. Bendig, J.; Yu, K.; Aasen, H.; Bolten, A.; Bennertz, S.; Broscheit, J.; Gnyp, M.L.; Bareth, G. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Observ. Geoinf. 2015, 39, 79-87
42. Moeckel, T.; Safari, H.; Reddersen, B.; Fricke, T.;Wachendorf, M. Fusion of ultrasonic and spectral sensor data for improving the estimation of biomass in grasslands with heterogeneous sward structure. Remote Sens. 2017, 9, 98
43. Safari, H.; Fricke, T.; Reddersen, B.; Moeckel, T.;Wachendorf, M. Comparing mobile and static assessment of biomass in heterogeneous grassland with a multi-sensor system. J. Sens. Sens. Syst. 2016, 5, 301-312