
International Journal of Computer & Software Engineering Volume 1 (2016), Article ID 1:IJCSE-103, 8 pages
https://doi.org/10.15344/2456-4451/2016/103
https://doi.org/10.15344/2456-4451/2016/103
Research Article
Development and Preliminary Tests of a Crop Monitoring Mobile Lab Based on a Combined use of Optical Sensors
References
- Bietresato M, Vidoni R, Gasparetto A, Mazzetto F (2015) Design and first tests of a vision system on a tele-operated vehicle for monitoring the canopy vigour status in orchards. Near Surface Geoscience. View
- Bietresato M, Carabin G, Vidoni R, Gasparetto A, Mazzetto F (2016) Evaluation of a lidar-based 3d-stereoscopic vision system for cropmonitoring applications. Comput Electron Agr 124: 1-13. View
- Lee K, Ehsani R, Castle W (2010) A laser scanning system for estimating wind velocity reduction through tree windbreaks. J Comput Electron Agr 73: 1-6. View
- Martin-Sanz A, Caminero C, Jing R, Flavell AJ, Perez de la Vega M (2011) Genetic diversity among spanish pea (pisum sativum l.) landraces, pea cultivars and the world pisum sp. core collection assessed by retrotransposon-based insertion polymorphisms (rbips). Spanish J Agr Res 9: 166-178. View
- Rosell JR, Sanz R (2012) A review of methods and applications of the geometric characterization of tree crops in agricultural activities. Comput Electron Agri 81: 124-141. View
- Sanz R, Rosell JR, Lorens J, Gil E, Planas S (2013) Relationship between tree row lidar-volume and leaf area density for fruit orchards and vineyards obtained with a lidar 3d dynamic measurement system. Agr Forest Meteorol 172: 153-162. View
- Newnham G, Siggins A, Blanchi R, Culvenor D, Leonard J (2012) Exploiting three dimensional vegetation structure to map wildland extent. Remote Sens Environ 123: 155-162. View
- Rossel Polo J, Sanz R, Lorens J, Arn J, Escol A, et al.(2009) A tractormounted scanning lidar for the non-destructive measurement of vegetative volume and surface area of tree row plantations: A comparison with conventional destructive measurements. Biosyst Eng 102: 128-134. View
- Keightley K, Bawden(2010) 3D volumetric modeling of grapevine biomass using tripod lidar. Comput Electron Agr 74: 305-312. View
- Bucksch A, Fleck S (2009) Automated detection of branch dimensions in woody skeletons of leafless fruit tree canopies. SilviLaser. View
- Raumonen P, Kaasalainen M, kerblom M, Kaasalainen S, Kaartinen H, et al. ( 2013) Fast automatic precision tree models from terrestrial laser scanner data. Remote Sensing 5: 491-520. View
- Cote J-F, Widlowski J-L, Fournier R, Verstraete M (2009) The structural and radiative consistency of three-dimensional tree reconstructions from terrestrial lidar. Remote Sens Environ 113: 1067-1081. View
- Moorthy K, Babu S, Satheesh S (2007) Temporal heterogeneity in aerosol characteristics and the resulting radiative impact at a tropical coastal station-part 1: microphysical and optical properties. Annal Geophy 25: 2293–2308. View
- Fieber K, Davenport J, Ferryman J, Gurney R, Tanase M, et al. (2013) Waveform and discrete lidar effective lai estimates: sensitivity analysis. SilviLaser. View
- Walklate P, Cross J, Richardson G, Murray R, Baker D (2002) Comparison of different spray volume deposition models using lidar measurements of apple orchards. Biosys Eng 82: 253-267. View
- Auat Cheein F, Guivant J (2014) Slam-based incremental convex hull processing approach for treetop volume estimation. Comput Electron Agr 102: 19-30. View
- Mendez B, Vivoni L, Robles-Morua A, Mascaro G, Yepez E (2014) A modeling approach reveals differences in evapotranspiration and its partitioning in two semiarid ecosystems in northwest mexico. Water Resour Res 50: 322-932. View
- Jaeger-Hansen C, Dhring K (2012) Electric agricultural robot with multilayer- control. Int Conf Agr Eng. View
- Rosell J, Llorens J, Sanz R, Arn J, Ribes-Dasi M, et al. (2009) Obtaining the three-dimensional structure of tree orchards from remote 2d terrestrial lidar scanning. Agricult Forest Meteorol 149: 1505-1515. View
- Calcante A, Mena A, Mazzetto F (2012) Evaluation of “ground sensing” optical sensors for diagnosis of plasmopara viticola on vines. Spanish J Agr Res 10: 619–630. View
- Mazzetto F, Calcante A, Mena A, Vercesi A (2010) Integration of optical and analogue sensors for monitoring canopy health and vigour in precision viticulture. Precision Agr 11: 636–649. View
- Povh F, de Paula G, Anjos W (2014) Optical sensors applied in agricultural crops. Optical Sensors - New Development Practical Applications. View
- Fortes M, Prieto H, Garcia-Martin A, Cordoba A, Martinez L, et al. (2015) Using ndvi and guided sampling to develop yield prediction maps of processing tomato crop. Spanish J Agr Res 13: 2171–9292. View
- Mazzetto F, Calcante A, Mena A, Vercesi A (2009) Development and first tests of a mobile lab combining optical and analogical sensors for crop monitoring in precision viticulture. Precision Agr 9: 31-38. View
- Romaneckas K, Zinkevicius R, Steponavicius D, Maziliauskas A, Butkus V (2015) Principles of precision agriculture in on-farm spring wheat fertilization experiment. Eng Rural Develop. View
- Albanese M, Pugliese A, Subrahmanian V (2013) Fast activity detection: Indexing for temporal stochastic automaton-based activity models. Knowl Data Eng IEEE Transa 25: 360-373. View
- D'Auria D, Persia F (2014) A framework for real-time evaluation of medical doctors performances while using a cricothyrotomy simulator. In Data Manag Tech App Springer 182-198. View
- D’Auria D, Persia F (2014) Automatic evaluation of medical doctors’ performances while using a cricothyrotomy simulator. In Inform Reuse Integrat (IRI). IEEE 15th Int Conf IEEE 514–519. View
- D'Auria D, Persia F (2014) Discovering expected activities in medical context scientific databases. Proceed 3rd Int Conf Data Manag Tech App 446–453. View
- Persia F, D’Auria D (2014) An application for finding expected activities in medical context scientific databases. 77–88. View
- Rouse J, Haas R, Schell D, Deering JA (1974) Monitoring vegetation systems in the great plains with erts. Third Earth Resourc Tech Satell Syp 1: 309-317. View
- Barnes E, Clarke T, Richards S, Colaizzi J, Haberland PD, et al. (2000) Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data. The 5th Int Conf Precision Agr 1-15. View
- Ristorto G, Mazzetto F, Guglieri G, Quagliotti F (2015) Monitoring performances and cost estimation of multirotor unmanned aerial systems in precision farming. Unmanned Aircraft Systems 502-509. View
- Polo R, Sanz R, Llorens J, Arn J, Escol ARM, et al. (2009) A tractor-mounted scanning lidar for the non-destructive measurement of vegetative volume and surface area of tree-row plantations: A comparison with conventional destructive measurements. Biosyst Eng 102: 128-134. View
- Moscato V, Picariello A, Persia F, Penta A (2009) A system for automatic image categorization. in Semant Comput ICSC’09. IEEE Int Conf 624-629. View