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Perspektive des landwirtschaftlichen Systems

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Handbuch Digital Farming

Zusammenfassung

Digital Ag hat einen starken Einfluss auf die Praxis der Landwirte. In diesem Abschnitt wird der Einfluss auf verschiedene landwirtschaftliche Systeme erörtert und die digitale Landwirtschaft mit Produktionsfaktoren in Verbindung gebracht.

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Literatur

  1. Brown, P., Bocken, N., & Balkenende, R. (2020). How do companies collaborate for circular oriented innovation?Sustainability, 12(4), 1–21.

    Article  Google Scholar 

  2. Boaz, A., Balendonck, J., Barth, R., Ben-Shahar, O., Edan, Y., Hellström, T., Hemming, J., Kurtser, P., Ringdahl, O., Tielen, T., & van Tuijl, B. 2020). Development of a sweet pepper harvesting robot. Journal of Field Robotics, 37(6). https://doi.org/10.1002/rob.21937.

  3. Blok, P. M., Barth, R., & van den Berg, W. (2016). Machine vision for a selective broccoli harvesting robot. IFAC-PapersOnLine, 49(16), 66–71. https://doi.org/10.1016/j.ifacol.2016.10.013.

    Article  Google Scholar 

  4. Byerlay, R. A. E., Coates, C., Aliabadi, A. A., & Kevan, P. G. (2020). In situ calibration of an uncooled thermal camera for the accurate quantifcation of fower and stem surface temperatures. Thermochimica Acta, 693. https://doi.org/10.1016/j.tca.2020.178779.

  5. Roland Berger GmbH. (2019). Landwirtschaft 4.0 – Digitalisierung als Chance. https://www.rolandberger.com/de/Insights/Publications/Landwirtschaft-4.0-Digitalisierung-als-Chance.html. Zugegriffen: 13. März 2022.

  6. Better Food Ventures. (2020). Farm Tech Landscape. https://betterfoodventures.com/agtech-landscapes/farm-tech-landscape-2020. Zugegriffen: 13. März 2022.

  7. Brückner, B., Geyer, M., & Ziegler, J. (2008). Spargelanbau Grundlagen für eine erfolgreiche Produktion und Vermarktung, 128. Ulmer. ISBN 978-3-8001-4627-7.

    Google Scholar 

  8. Birrell, S., Hughes, J., Cai, J. Y., & Iida, F. (2019). A field-tested robotic harvesting system for iceberg lettuce. Journal of Field Robotics 37(2020), 225–245. https://doi.org/10.1002/rob.21888.

  9. Bellon-Maurel, V., & Huyghe, C. (2016). L’innovation technologique dans l’agriculture. Géoéconomie, 3, 159–180.

    Article  Google Scholar 

  10. Bellon, M. V., & Huyghe, C. (2017). Putting agricultural equipment and digital technologies at the cutting edge of agroecology. OCL, 24(3), D307.

    Article  Google Scholar 

  11. Berducat, M. (2018). Vers la possibilité de repenser la mécanisation agricole en Grandes Cultures grâce à la robotique? Conférence PHLOEME Paris.

    Google Scholar 

  12. Boini, A., Manfrini, L., Bortolotti, G., Corelli-Grappadelli, L., & Morandi, B. (2019). Monitoring fruit daily growth indicates the onset of mild drought stress in apple. Scientia Horticulturae, 256, 108520.

    Google Scholar 

  13. Bouttet, D., & Pierson, P. (2017). Digifermes: Un laboratoire des technologies numériques (446) (S. 38–39).

    Google Scholar 

  14. Brun, F., Siné, M., Gallot, S., Lauga, B., Colinet, J., Cimino., Haezebrouck, T. P., & Besnard, J. (2016). ACTA – Les Instituts Techniques Agricoles: L’accès aux données pour la Recherche et l’Innovation en Agriculture. 978-2-85794-298-6.

    Google Scholar 

  15. BVA Study. https://www.bva-group.com/sondages/agriculture-nouvelles-technologies-on/. Zugegriffen: 5. Juli 2022.

  16. Coulouma, G., Boizard, H., Trotoux, G., Lagacherie, P., & Richard, G. (2006). Effect of deep tillage for vineyard establishment on soil structure: A case study in Southern France. Soil and Tillage Research, 88(1–2), 132–143.

    Article  Google Scholar 

  17. Cerrutti, N., Chaigne, G., Gayrard, M., Emonet, E., & Chabert, A. (2012). Description des systèmes d’exploitation de référence. In Actes du Colloque Pollinov, 23. Poitiers.

    Google Scholar 

  18. Cerescon. http://www.cerescon.com/NL/home. Zugegriffen: 14. Juli 2022.

  19. Charte sur l’utilisation des données agricoles DATA-AGRI. (2018). https://www.data-agri.fr/Asset/Charte_Data-Agri-Utilisation%20des%20donn%C3%A9es%20agricoles.pdf.

  20. Claus, A.-S., Johns, J., Lindena, T., Nieberg, H., & Kuhnert, H. (2020). „Nachhaltigkeitsmodul“, Vortrag im Rahmen des Molkereitreffens in der Pilotphase. Zugegriffen: 18. Febr. 2021.

    Google Scholar 

  21. Cleveland, W. S. (2001). Data science: An action plan for expanding the technical areas of the feld of statistics. International Statistical Review, 69, 21–26.

    Article  Google Scholar 

  22. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209.

    Article  Google Scholar 

  23. Desbourdes, C., Métais, P., & Chavassieux, D. (2017). L’automatisation a le vent en poupe. Perspectives Agricoles, 446, 46–49.

    Google Scholar 

  24. Deepfeld-Connect. www.deepfeld-connect.com. Zugegriffen: 14. Juli 2022.

  25. Deutsches Weininstitut. (2021). Deutscher Wein Statistik. www.deutscheweine.de. Zugegriffen: März 2021.

  26. Dressler. (2013). Innovation management of German wineries: From activity to capacity – An explorative multi-case survey. Wine Economics and Policy 2(1), 19–26.

    Google Scholar 

  27. Dias, P., Tabb, A., & Medeiros, H. (2018). Apple fower detection using deep convolutional networks. Computers in Industry, 99, 17–28. https://doi.org/10.1016/j.compind.2018.03.010.

    Article  Google Scholar 

  28. Dzikiti, S., Verreynne, S. J., Stuckens, J., et al. (2011). Seasonal variation in canopy refectance and its application to determine the water status and water use by citrus trees in the Western Cape. South Africa. Agr Forest Meteorol., 151, 1035–1044.

    Article  Google Scholar 

  29. Dulks. www.dulks.de. Zugegriffen: 14. Juli 2020.

  30. Ecoation. www.ecoation.com. Zugegriffen: 14. Juli 2022.

  31. Engels. www.engelsmachines.nl. Zugegriffen: 14. Juli 2022.

  32. EU Code of conduct on agricultural data sharing by contractual agreement. (2018). https://www.copa-cogeca.eu/img/user/files/EU%20CODE/EU_Code_2018_web_version.pdf.

  33. Eylenbosch D., Fernández Pierna, J. A., Baeten, V., & Bodson, B. (2018). Utilisation de l’imagerie hyperspectrale proche infrarouge combinée aux outils de la chimiométrie dans l’étude de systèmes racinaires. Conférence PHLOEME Paris.

    Google Scholar 

  34. Fountas, S., Carli, G., Sørensen, C. G., Tsiropoulos, Z., Cavalaris, C., Vatsanidou, A, Liaks, B., Canavari, M., Wiebensohn, J., & Tisserye, B. (2015). Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture, 115, 40–50.

    Google Scholar 

  35. Frohman, C. A., de Orduña, M., & Heidinger, R. (2018). The substratostat – an automated near-infrared spectroscopy-based variable-feed system for fed-batch fermentation of grape must. OENO One, 52, 4.

    Article  Google Scholar 

  36. Federal Ministry for Economic Affairs und Energy. (2021). Dossier on skilled professionals for Germany. www.bmwi.de. Zugegriffen: März 2021.

  37. Fernandez R., Montes, H., Surdilovic, J., Surdilovic, D., Gonzales-de-Santos, P., & Armada, M., et al. (2018). Automatic detection of feldgrown cucumbers for robotic harvesting. IEEE Access, 6, 35512–35527. https://doi.org/10.1109/ACCESS.2018.2851376.

  38. Fortune Business Insights. (2020). Wine market size and industry analyses by type, favor, distribution channel and regional forecast. www.fortunebusinessinsights.com. Zugegriffen: Jan. 2021.

  39. Fuentes, S., Poblete-Echeverría, C., Ortega-Farias, S., et al. (2014). Automated estimation of leaf area index from grapevine canopies using cover photography, video and computational analysis methods. Australian Journal Grape Wine R., 20, 465–473.

    Article  Google Scholar 

  40. Fraunhofer Gesellschaft. https://www.fraunhofer.de/content/dam/zv/en/press-media/2018/February/ResearchNews/lightweight-robots-harvest-cucumbers.pdf. Zugegriffen: 13. März 2022.

  41. Garford. https://garford.com/de/robocrop-inrow-weeder. Zugegriffen: 13. März 2022.

  42. „Haltungen mit Rindern und Rinderbestand für Mai 2020 und November 2020“, Genesis Online Datenbank. https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Landwirtschaft-Forstwirtschaft-Fischerei/Tiere-Tierische-Erzeugung/Tabellen/betriebe-rinder-bestand.html. Zugegriffen: 18. Febr. 2021.

  43. „41311-0001, Gehaltene Tiere: Deutschland, 2009–2020, Rinder 2 Jahre und älter, Milchkühe“, Genesis Online Datenbank. https://www-genesis.destatis.de/genesis/online. Zugegriffen: 18. Febr. 2021.

  44. „41311-0003, Betriebe: Deutschland, 2009–2020, Rinder 2 Jahre und älter, Milchkühe“, Genesis Online Datenbank. https://www-genesis.destatis.de/genesis/online. Zugegriffen: 18. Febr. 2021.

  45. Geyer, M. (2018). Mechanisation of white asparagus harvest–overview and perspectives. Acta Horticulturae, 1223(Acta Hort 1223), 239–249. https://doi.org/10.17660/ActaHortic.2018.1223.33.

  46. Goncharuk, A. (2016). The challenges of effciency and security of international food value chains. Journal of Applied Management and Investments, 5(4), 241–249.

    Google Scholar 

  47. Goncharuk, A. (2017). Wine value chains: Challenges and prospects. Journal of Applied Management and Investments, 6(1), 11–27.

    Google Scholar 

  48. Gourdain E., Piraux, F., Couleaud, G., Grignon, G., Launay, M., Deudon, D., Gaucher, D., Moreau, F., Le Bris, X. (2018). L’apport des datasciences dans la modélisation des maladies des céréales à paille. Conférence PHLOEME Paris.

    Google Scholar 

  49. Van Henten, E. J., Hemming, J., Van Tuijl, B. A. J., Kornet, J. G., Meuleman, J., Bontsema, J., & Van, E. A. (2002). Os An autonomous robot for harvesting cucumbers in greenhouses. Autonomous Robots 13(3), 241–258.

    Google Scholar 

  50. Hermeler. https://www.hmf-hermeler.de. Zugegriffen: 14. Juli 2022.

  51. Hortisem. www.hortisem.de. Zugegriffen: 14. Juli 2022.

  52. Huang, Y. R., Ren, Z. H., Li, D. M., Liu, X. (2020). Phenotypic techniques and applications in fruit trees: A review. Plant Methods, 16, 107. https://doi.org/10.1186/s13007-020-00649-7.

  53. Internationale Organisation für Rebe und Wein. 2020. State of the world vitivinicultural sector in 2019. Statistic report. www.oiv.int. Zugegriffen: Jan. 2021.

  54. Isaac, H., & Pouyat, M. (2015). Les défs de l’agriculture connectée dans une société numérique, 106. Rennaissance numérique: Livre blanc.

    Google Scholar 

  55. Informationssystem Integrierte Pfanzenproduktion e. V. www.ISIP.de. Zugegriffen: 10. Dez. 2023.

  56. Käthner, J., Ben-Gal, A., Gebbers, R., Peeters, A., Herppich, W. B., & ZudeSasse, M. (2017). Evaluating spatially resolved infuence of soil and tree water status on quality of European plum grown in semi-humid climate. Frontiers in Plant Science, 8, 1053. https://doi.org/10.3389/fpls.2017.01053.

  57. King, A. (2017). Technology: The future of agriculture. Nature, 544, S21–S23. https://doi.org/10.1038/544S21.

    Article  CAS  Google Scholar 

  58. Koshy, S.-S., Nagaraju, Y., Palli, S., Prasad, Y., & Pola, N. (2014). Wireless sensor network based forewarning models for pests and diseases in agriculture: A case study on groundnut. International Journal of Advanced Research and Technology, 3, 74–82.

    Google Scholar 

  59. Lachia, N. (2018). Numérique et Conseil en Grandes Cultures. http://agrotic.org/observatoire/wp-content/uploads/2018/07/20180130_ObsDossierGC.pdf.

  60. Greta Langer. (2020). Der digitale Pfad der deutschen Milchwirtschaft – Ein Überblick. https://www.milchtrends.de/fleadmin/milchtrends/5_Aktuelles/2020-10.pdf. Zugegriffen: 18. Febr. 2021.

  61. Lauga, B. (2017). Un tableau de bord pour un pilotage plus effcace 446, 40.

    Google Scholar 

  62. Lauga, B. (2018). Faire émerger de nouveaux services pour l’agriculteur dans une chaine de confance gérant les consentements d’accès aux données des exploitations. Conférence PHLOEME Paris.

    Google Scholar 

  63. Lindena, T., Claus, A. S., Johns, J., Nieberg, H., & „QM-Nachhaltigkeitsmodul Milch – es geht weiter!“. (2020). https://media.diemayrei.de/57/722557.pdf. Zugegriffen: 18. Febr. 2021.

  64. Lecoeur, J., & Moureaux, B. (2020). Adaptation aux aléas : Identifer les leviers d’action en intégrant la modélisation et le Big Data. Perspectives Agricoles, 475, 42–43.

    Google Scholar 

  65. Lu, R. F., Van Beers, R., Saeys, W., Li, C. Y., & Cen, H. Y. (2020). Measurement of optical properties of fruits and vegetables: A review. Postharvest Biology and Technology, 111003.

    Google Scholar 

  66. Firma Kress-Landtechnik. https://www.kress-landtechnik.eu/de/produkte/robovator.php. Zugegriffen: 14. Juli 2022.

  67. Moller, M., Alchanatis, V., Cohen, Y., et al. (2006). Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. Journal of Experimental Botany, 58, 827–838.

    Article  Google Scholar 

  68. Mazaud, C. (2017). «À chacun son métier», les agriculteurs face à l’of numérique. Sociologies pratiques, 1(34), 39–47.

    Article  Google Scholar 

  69. Mehlhose, C., Busch, G., & Spiller, A. (2020). Tierwohl im Molkereiproduktregal – Neue Herausforderungen für Erzeuger und Molkereien. https://www.milchtrends.de/fleadmin/milchtrends/5_Aktuelles/2020_04_Tierwohl_Moproregal_fnal.pdf. Zugegriffen: 18. Febr. 2021.

  70. Mésséan, A., Bernard, H., Turckheim, E. (2009). Concevoir et construire la décision: Démarches en agriculture, agroalimentaires et espace rural, Editions Quae.

    Google Scholar 

  71. Malveaux, C., Hall, S. G., Price, R. (2014). Using drones in agriculture: Unmanned aerial systems for agricultural remote sensing applications. Proceedings of the 2014 annual meeting of the American Society of Agricultural and Biological Engineers from July, 13th to July, 16th in Montreal, Canada, S. 1–10.

    Google Scholar 

  72. Micheloni, C. (2017). Diseases und pests in viticulture. Starting paper of the EIP-AGRI Focus Group. https://ec.europa.eu. Zugegriffen: Dez. 2020.

  73. „Deutsche Milchindustrie in Zahlen 2010–2019“, MIV. https://milchindustrie.de/wp-content/uploads/2020/04/Milchmarkt-in-Zahlen_2010-2019_Homepage_neu.pdf. Zugegriffen: 18. Febr. 2021.

  74. „Strategie 2030 der deutschen Milchwirtschaft“, MIV. https://milchindustrie.de/wp-content/uploads/2020/04/Milchmarkt-in-Zahlen_2010-2019_Homepage_neu.pdf. Zugegriffen: 18. Febr. 2021.

  75. Maes, W., & Steppe, K. (2019). Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends in Plant Science, 24, 152–164. https://doi.org/10.1016/j.tplants.2018.11.007.

    Article  CAS  Google Scholar 

  76. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. https://bitcoin.org. Zugegriffen: März 2021.

  77. Nordey, T., Davrieux, F., & Léchaudel, M. (2019). Predictions of fruit shelf life and quality after ripening: Are quality traits measured at harvest reliable indicators? Postharvest Biology and Technology, 153, 52–60.

    Google Scholar 

  78. Naiture. https://www.naiture.org/. Zugegriffen: 14. Juli 2022.

  79. Neubauer. https://www.neubauer-automation.de/. Zugegriffen: 14. Juli 2022.

  80. Newtec. www.newtec.com. Zugegriffen: 14. Juli 2022.

  81. Paschold, P.-J., Kleber, J., & Mayer, N. (2009). „Bewässerungssteuerung bei Gemüse im Freiland“, Landbauforschung, Sonderheft 328.

    Google Scholar 

  82. Penzel, M., Lakso, A. N., Tsoulias, N., & Zude-Sasse, M. (2020). Carbon consumption of developing fruit and individual tree’s fruit bearing capacity of ‘RoHo 3615’ and ‘Pinova’ apple. International AgroPhysics, 34, 409–423. https://doi.org/10.31545/intagr/127540.

  83. Qin, J. W., Kim, M. S., Chao, K. L., Dhakal, S., Cho, B. K., Lohumi, S., Mo, C. Y., Peng, Y. K., & Huang, M. (2019). Advances in Raman spectroscopy and imaging techniques for quality and safety inspection of horticultural products. Postharvest Biology and Technology, 149, 101–117.

    Google Scholar 

  84. Qi, F., Zhu, X., Mang, G., Kadoch, M., Li, W. (2019). UAV network and IoT in the sky for future smart cities. IEEE Network, 33, 96–101.

    Google Scholar 

  85. Rauch. www.rauch.de. Zugegriffen: 10. Dez. 2023.

  86. Ram. www.ram-group.com. Zugegriffen: 14. Juli 2022.

  87. Romero-Trigueros, C., Bayona Gambín, J. M., Nortes Tortosa, P. A., Alarcón Cabañero, J. J., & Nicolás Nicolás, E. (2019). Determination of crop water stress index by infrared thermometry in grapefruit trees irrigated with saline reclaimed water combined with defcit irrigation. Remote Sensing, 11, 757.

    Google Scholar 

  88. Rojas-Downing, M. M., Nejadhashemi, A. P., Harrigan, T., & Woznicki, S. A. (2017). Climate change and livestock: Impacts, adaptation, and mitigation. Climate Risk Management, 16, 145–163. https://doi.org/10.1016/j.crm.2017.02.001. Zugegriffen: 18. Febr. 2021.

  89. Roberto, M. (2011). The changing structure of the global wine industry. International Business and Economics Research Journal, 2(9), 1–14.

    Google Scholar 

  90. Sablayrolles, J.-M. (2009). Control of alcoholic fermentation in winemaking: Current situation and prospect. Food Research International, 42(4), 418–424.

    Article  CAS  Google Scholar 

  91. Soenen, B., Bessard Duparc, P., Laberdesque, M., Deswarte, J. C., Bouthier, A., Laurent, F., Le Bris, X. (2018a). Piloter conjointement la fertilisation azotée et l’irrigation par couplage d’observations sol/plante avec le modèle de culture CHN. Conférence PHLOEME Paris.

    Google Scholar 

  92. de Solan, B., Baret, F., Thomas, S., Madec, S., Comar, A., Beauchêne, K., & Fournier, A. (2018). Systèmes de phénotypage haut-débit au champ, méthodes associées et premiers résultats. Conférence PHLOEME Paris 2018.

    Google Scholar 

  93. Soenen B., Closset, M., Nonnard, A., & Le Bris, X. (2018b). Le pilotage de l’azote sur blé dans le service FARMSTAR. Conférence PHLOEME Paris.

    Google Scholar 

  94. de Solan, B., Deudon, O., & Leprince, F. (2017). L’internet des objects impacte tout le secteur agricole (446), 42–45.

    Google Scholar 

  95. Schwinn, M., Durner, D., Wacker, M., Delgado, A., & Fischer, U. (2019). Impact of fermentation temperature on required heat dissipation, growth and viability of yeast, on sensory characteristics and on the formation of volatiles in Riesling. Australian Journal of Grape and Wine Research, 25, 173–184.

    Google Scholar 

  96. Sanz, R., Llorens, J., Escola, A., Arno, J., Planas, S., Roman, C., & Rosell-Polo, J. R. (2018). LIDAR and non-LIDAR-based canopy parameters to estimate the leaf area in fruit trees and vineyard. Agricultural and Forest Meteorology, 260, 229–239. https://doi.org/10.1016/j.agrformet.2018.06.017.

  97. Santos, J.-A., Fraga, H., Malheiro, A.-C., Moutinho-Pereira, J., Dinis, L.-T., Correia, C., Moriondo, M., Leolini, L., Dibari, C., Costafreda-Aumedes, S., Kartschall, T., Menz, C., Molitor, D., Junk, J., Beyer, M., & Schultz, H.-R. (2020). A review of the potential climate change impacts and adaptation options for European viticulture. Applied Sciences, 10(3092), 1–28.

    Google Scholar 

  98. Siné, M., Gourdain, E., & Pinochet, X. (2017). La dynamique des startup agricoles. Perspectives Agricoles, 446, 52–53.

    Google Scholar 

  99. Siné, M., Haezebrouck, T. P., & Emonet, E. (2015). API-AGRO: An open data and open API platform to promote interoperability standards for Farm Services and Ag Web Applications. AGRÁRINFORMATIKA/Journal of Agricultural Infomatics, 6(4), 56–64.

    Google Scholar 

  100. Sansoni, G., Trebeschi, M., & Docchio, F. (2009). State-of-The-Art and applications of 3D imaging sensors in industry, cultural heritage, medicine, and criminal investigation. Sensors (Basel), 9(1), 568–601.

    Article  Google Scholar 

  101. de Solan, B., Thomas, S., Deshayes, G., Labrosse, J., Li, W., Piquemal, P., Porrez, P., Bouttet, D., Deudon, O., Jézéquel, S., Braun, P., Aubertin, F., Vanhoye, A., Vivens, C., Velumani, K., Baret, F., Comar, A., Leprince, F., & Siné, M. (2020). Modèle de culture et mesure par capteurs: Quelle complémentarité pour l’aide à la décision? Conférence PHLOEME Paris 2020.

    Google Scholar 

  102. Steenks. www.steenks-service.de. Zugegriffen: 14. Juli 2022.

  103. Sundmaeker, H., Verdouw, C. Wolfert, S., & Pérez Freire, L. (2016). „Internet of food an farm 2020“, Digitising the industry – Internet of things connecting physical, digital and virtual worlds (S. 129–151).

    Google Scholar 

  104. Sweet pepper harvesting robot. www.sweeper-robot.eu/. Zugegriffen: 13. März 2022.

  105. Shamshiri, R., Weltzien, C., Hameed, I., Yule, I., Grift, T., Balasundram, S., Pitonakova, L., Ahmad, D., & Chowdhary, G. (2018). Research and development in agricultural robotics: A perspective of digital farming. International Journal of Agricultural and Biological Engineering, 11(4), 1–14. https://www.ijabe.org/index.php/ijabe/article/view/4278/1737.

  106. Toqué C., Cadoux, S., Pierson, P., Flenet, B. C., Angevin, F., Gate, P. (2015). SYPPRE: A project to promote innovations in arable crop production mobilizing farmers and stakeholders and including co-design, ex-ante evaluation and experimentation of multi-service farming systems matching with regional challenges. 5th International Symposium for Farming Systems Design. Montpellier – France.

    Google Scholar 

  107. Tsoulias, N., Fountas, S., & ZudeSasse, M. (2022). Tree growth modelling by means of LiDAR laser scanner. Biosystems Engineering, 182–199.

    Google Scholar 

  108. Tsoulias, N., Gebbers, R., & ZudeSasse, M. (2020). Using data on soil ECa, soil water properties, and response of tree root system for spatial water balancing in an apple orchard. Precision Agriculture, 21, 522–548. https://doi.org/10.1007/s11119-019-09680-8

    Article  Google Scholar 

  109. Trapp, M., Hörner, G., & Kubiak, R. (2003). Functional landscape characterisation with object-oriented image analysis for a GIS-based local risk assessment. In Proceedings of the XII Symposium on Pesticide Chemistry from June 4th to June 6th, 2003 in Piacenza, Italy (S. 649–655).

    Google Scholar 

  110. Tomasso, L. (2019). Analyse juridique contractuelle des données de l’agriculture numérique. https://numerique.acta.asso.fr/multipass-analyse-juridique-contractuelle-des-donnees-de-lagriculture-numerique/.

  111. Touzard, J.-M. (2010). Innovation systems and the competition between regional vineyards. In Proceedings of the Innovation and Sustainable Development in Agriculture Symposium from June, 28th to July, 1st, 2010 in Montpellier, France (S. 1–13).

    Google Scholar 

  112. Tsoulias, N., Paraforos, D. S., Fountas, S., & Zude-Sasse, M. (2019). Estimating canopy parameters based on the stem position in apple trees using a 2D LiDAR. Agronomy, 9, 740. https://doi.org/10.3390/agronomy9110740.

  113. Visser. www.visser.eu. Zugegriffen: 10. Dez. 2023.

  114. Visar. www.visar-europe.com. Zugegriffen: 14. Juli 2022.

  115. Wassan, J. T. (2016). Big data paradigm for healthcare sector. In A. Aggarwal (Ed.), Managing big data integration in the public sector (S. 169–186).

    Google Scholar 

  116. Walsh, K. B., Blasco, J., ZudeSasse, M., & Xudong, S. (2020). Review: Visible-NIR ‘point’ spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use. Postharvest Biology and Technology, 168, 111246. https://doi.org/10.1016/j.postharvbio.2020.111246.

  117. Agrarmeteorologie Rheinland-Pfalz. www.wetter.rlp.de. Zugegriffen: 13. März 2022.

  118. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming – a review. Agricultural Systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023. Zugegriffen: 18. Febr. 2021.

  119. Wu, D., Phinn, S., Johansen, K., Robson, A., Muir, J., & Searle, C. (2018). Estimating changes in leaf area, leaf area density, and vertical leaf area profle for mango, avocado, and macadamia tree crowns using terrestrial laser scanning. Remote Sensing, 10, 1750. https://doi.org/10.3390/rs10111750.

  120. Wolfert, J., Sørensen, G., & Goense, D. (2014). A future internet collaboration platform for safe and healthy food from farm to fork, global conference (SRII), 2014 annual SRII. IEEE, San Jose, CA, USA, 2014, 266–273.

    Google Scholar 

  121. Wang, Z. L., Underwood, J., & Walsh, K. B. (2018). Machine vision assessment of mango orchard fowering. Computers and Electronics in Agriculture, 51, 501–511. https://doi.org/10.1016/j.compag.2018.06.040.

    Article  Google Scholar 

  122. Wyma. https://www.wymasolutions.com/. Zugegriffen: 14. Juli 2022.

  123. Xue, J., Fan, Y., Su, B., & Fuentes, S. (2019). Assessment of canopy vigor information from kiwifruit plants based on a digital surface model from unmanned aerial vehicle imagery. International Journal of Agricultural and Biological Engineering, 12(1), 165–171.

    Google Scholar 

  124. Zude-Sasse, M., Fountas, S., Gemtos, T. A., & Abu-Khalaf, N. (2016). Applications of precision agriculture in horticultural crops – A REVIEW. European Journal of Horticultural Science, 2016(81):78–90. https://doi.org/10.17660/eJHS.2016/81.2.2.

  125. Zhao, Q., & Hastie, T. (2019). Causal interpretations of black-box models. Journal of Business & Economic Statistics, 2019, 1–10.

    Google Scholar 

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Green, T. et al. (2023). Perspektive des landwirtschaftlichen Systems. In: Dörr, J., Nachtmann, M. (eds) Handbuch Digital Farming. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-67086-6_5

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