Pontes, L.; Maire, V.; Schellberg, J.; Louault, F. Grass strategies and grassland community responses to environmental drivers: A review. Eur. J. Plant Pathol. 2015, 35, 1297–1318.
Pepitone, J. Hacking the farm: How farmers use “digital agriculture” to grow more crops. CNN Money 2016. Available online: https://money.cnn.com/2016/08/03/technology/climate-corporation-digital-agriculture/index.html (accessed on 3 August 2016).
Grell, M.; Barandun, G.; Asfour, T.; Kasimatis, M.; Collins, A.; Wang, J.; Guder, F. Determining and Predicting Soil Chemistry with a Point-of-Use Sensor Toolkit and Machine Learning Model. bioRxiv 2020, 9, 1–27. [CrossRef]
McBratney, A.; Whelan, B.; Ancev, T. Future Directions of Precision Agriculture. Precis. Agric. 2005, 6, 7–23. [CrossRef]
Whelan, B.M.; McBratney, A.B. Definition and Interpretation of potential management zones in Australia. In Proceedings of the 11th Australian Agronomy Conference, Geelong, Victoria, 2–6 February 2003; pp. 2–6.
Schieffer, J.; Dillon, C. The economic and environmental impacts of precision agriculture and interactions with agro-environmental policy. Precis. Agric. 2015, 16, 46–61. [CrossRef]
Sophocleous, M.; Georgiou, J. Precision agriculture: Challenges in sensors and electronics for real-time soil and plant monitoring. In Proceedings of the IEEE BIOCAS 2017 Biomedical Circuits and Systems Conference, Torino, Italy, 19–21 October 2017; pp. 1–4.
Sophocleous, M. IoT Thick-Film Technology for Underground Sensors in Agriculture. 2016. Available online: https://www.fierceelectronics.com/components/iot-thick-film-technology-for-underground-sensors-agriculture (accessed on 23 November 2021).
Kefauver, S.C.; El-Haddad, G.; Vergara-Diaz, O.; Araus, J.L. RGB picture vegetation indexes for High-Throughput Phenotyping Platforms (HTPPs). In Proceedings of the SPIE Conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, Toulouse, France, 22–24 September 2015.
Ouled Taleb Salah, S.; Duchesne, A.; De Cock, N.; Massinon, M.; Sassi, K.; Abrougui, K.; Lebeau, F.; Dorbolo, S. Experimental investigation of a round jet impacting a disk engraved with radial grooves. Eur. J. Mech. B Fluids 2018, 72, 302–310. [CrossRef]
Ouled Taleb Salah, S.; De Cock, N.; Massinon, M.; Schiffers, B.; Dorbolo, S.; Lebeau, F. Étude des potentialités des systèmes d’application contrôlée des gouttes (CDA) pour les traitements phytosanitaires en céréaliculture (synthèse bibliographique). Biotechnol. Agron. Société Environ. 2016, 20, 287–298. [CrossRef]
Rioboo, R.; Voué, M.; Vaillant, A.; De Coninck, J. Drop impact on porous superhydrophobic polymer surfaces. Langmuir 2008, 24, 14074–14077. [CrossRef]
Boukhalfa, H.; Massinon, M.; Belhamra, M.; Lebeau, F. Contribution of spray droplet pinning fragmentation to canopy retention. Crop Prot. 2014, 56, 91–97. [CrossRef]
Masuka, B.; Araus, J.L.; Das, B.; Sonder, K.; Cairns, J.E. Phenotyping for Abiotic Stress Tolerance in Maize. J. Integr. Plant Biol. 2012, 54, 238–249. [CrossRef] [PubMed]
Araus, J.L.; Cairns, J.E. Field high-throughput phenotyping: The new crop breeding frontier. Trends Plant Sci. 2014, 19, 52–61. [CrossRef] [PubMed]
Bänziger, M.; Edmeades, G.O.; Beck, D.; Bellon, M. Breeding for Drought and Nitrogen Stress Tolerance in Maize: From Theory to Practice; CIMMYT: El Batán, Mexico, 2000; Available online: https://repository.cimmyt.org/bitstream/handle/10883/765/68579. pdf?sequence=1&isAllowed=y (accessed on 23 November 2021).
Carter, G.A. Reflectance wavebands and indices for remote estimation of photosynthesis and stomatal conductance in pine canopies. Remote Sens. Environ. 1998, 63, 61–72. [CrossRef]
Chappelle, E.W.; Kim, M.S.; McMurtrey, J.E., III. Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves. Remote Sens. Environ. 1992, 39, 239–247. [CrossRef]
Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8, 127–150. [CrossRef]
Thenkabail, P.S.; Smith, R.B.; De Pauw, E. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens. Environ. 2000, 71, 158–182. [CrossRef]
Thenkabail, P.S.; Smith, R.B.; De Pauw, E. Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization. Photogramm. Eng. Remote Sens. 2002, 68, 607–622.
Huang, S.; Tang, L.; Hupy, J.P.; Wang, Y.; Shao, G. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J. For. Res. 2021, 32, 1–6. [CrossRef]
Vergara-Diaz, O.; Kefauver, S.C.; Elazab, A.; Nieto-Taladriz, M.T.; Araus, J.L. Grain yield losses in yellow-rusted durum wheat estimated using digital and conventional parameters under field conditions. Crop. J. 2015, 3, 200–210. [CrossRef]
Gracia-Romero, A.; Vergara-Díaz, O.; Thierfelder, C.; Cairns, J.E.; Kefauver, S.C.; Araus, J.L. Phenotyping Conservation Agriculture Management Effects on Ground and Aerial Remote Sensing Assessments of Maize Hybrids Performance in Zimbabwe. Remote Sens. 2018, 10, 349. [CrossRef]
Deshpande, A.; Razmjooy, N.; Estrela, V. Introduction to Computational Intelligence and Super-Resolution. In Computational Intelligence Methods for Super-Resolution in Image Processing Applications; Springer International Publishing: Cham, Switzerland, 2021; pp. 3–23. ISBN 978-3-030-67921-7.
Casadesús, J.; Kaya, Y.; Bort, J.; Nachit, M.M.; Araus, J.L.; Amor, S.; Ferrazzano, G.; Maalouf, F.; Maccaferri, M.; Martos, V. Using vegetation indices derived from conventional digital cameras as selection criteria for wheat breeding in water-limited environments. Ann. Appl. Biol. 2007, 150, 227–236. [CrossRef]
Zaman-Allah, M.; Vergara, O.; Araus, J.L.; Tarekegne, A.; Magorokosho, C.; Zarco-Tejada, P.J.; Cairns, J. Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods 2015, 11, 35. [CrossRef]
Kefauver, S.C.; Vicente, R.; Vergara-Díaz, O.; Fernandez-Gallego, J.A.; Kerfal, S.; Lopez, A.; Melichar, J.P.E.; Serret Molins, M.D.; Araus, J.L. Comparative UAV and Field Phenotyping to Assess Yield and Nitrogen Use Efficiency in Hybrid and Conventional Barley. Front. Plant Sci. 2017, 8, 01733. [CrossRef] [PubMed]
El-Shikha, D.M.; Barnes, E.M.; Clarke, T.R.; Hunsaker, D.J.; Pinter, P.J.; Waller, P.M.; Thompson, T.L. Remote sensing of cotton nitrogen status using the canopy chlorophyll content index (CCCI). Trans. ASABE 2008, 51, 73–82. [CrossRef]
Trussell, H.J.; Vrhel, M.J.; Saber, E. Color image processing. IEEE Signal Process. Mag. 2005, 22, 14–22. [CrossRef]
Hunt, E.R.; Doraiswamy, P.C.; McMurtrey, J.E.; Daughtry, C.S.T.; Perry, E.M.; Akhmedov, B. A visible band index for remote sensing leaf chlorophyll content at the Canopy scale. Int. J. Appl. Earth Obs. Geoinf. 2012, 21, 103–112. [CrossRef]
Hunt, E.R.; Cavigelli, M.; Daughtry, C.S.T.; McMurtrey, J.E.; Walthall, C.L. Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precis. Agric. 2005, 6, 359–378. [CrossRef]
ASTER Spectral Library. Available online: http://speclib.jpl.nasa.gov (accessed on 23 December 2021).
ESA Copernicus Sentinel-2. Available online: https://scihub.copernicus.eu (accessed on 23 December 2021).
Ge, M.S.; Wu, P.T.; Zhu, D.L.; Zhang, L. Analysis of kinetic energy distribution of big gun sprinkler applied to continuous moving hose-drawn traveler. Agric. Water Manag. 2018, 201, 118–132. [CrossRef]
De Cock, N.; Massinon, M.; Ouled Taleb Salah, S.; Mercatoris, B.; RosariaVetran, M.; Lebeau, F. Dynamics of a thin radial liquid flow. Fire Saf. J. 2016, 83, 1–6. [CrossRef]
Borge, N.H.; Leblanc, E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 2000, 76, 156–172. [CrossRef]
Sankaran, S.; Khot, L.R.; Espinoza, C.Z.; Jarolmasjed, S.; Sathuvalli, V.R.; Vandemark, G.J.; Miklas, P.N.; Carter, A.H.; Pumphrey, M.O.; Knowles, R.R.N. Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. Eur. J. Agron. 2015, 70, 112–123. [CrossRef]
Fernandez-Gallego, J.A.; Kefauver, S.C.; Vatter, T.; Gutiérrez, N.A.; Nieto-Taladriz, M.T.; Araus, J.L. Low-cost assessment of grain yield in durum wheat using RGB images. Eur. J. Agron. 2019, 105, 146–156. [CrossRef]
