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Implementation of a Digital Tree to Optimise the Technical and Environmental Performance of Crop Protection Equipment: A PRIN Project to Promote More Sustainable Mechanisation in Olive Orchards

Conference Paper
Publication Date:
2025
Short description:
Implementation of a Digital Tree to Optimise the Technical and Environmental Performance of Crop Protection Equipment: A PRIN Project to Promote More Sustainable Mechanisation in Olive Orchards / Bernardi, B., Benalia, S., Abenavoli, L.M., Costa, G., Ortale, R., Macri, D., Forestiero, A., Manetto, G., Longo, D., Privitera, S., Cerruto, E.. - 586:(2025), pp. 319-327. (International Mid-Term Conference of the Italian Association of Agricultural Engineering, MID-TERM AIIA 2024 ita 2024) [10.1007/978-3-031-84212-2_40].
abstract:
Olive growing represents the most emblematic agricultural production of the Mediterranean Basin. Plant protection is a key intervention without which it would be difficult to achieve high productivity in the field. Although progress, strategies, and tools have reached a noticeable technological level regarding the development of active substances, the same cannot be stated for agrochemical application equipment. In this context, this paper describes the PRIN project “Implementation of a Digital Tree to Optimize Technical and Environmental Performances of Crop Protection Equipment (IM GROOT)”, which aims to investigate the quality and sustainability of mechanical application of agrochemicals in olive orchards, considering spray distribution evenness and environmental impact. Particularly, the project focuses on the development of an innovative smart tool, a “Digital Tree,” which will allow the prediction of spray behavior under different field conditions. This requires accurate knowledge of sprayer mechanical features, their functioning and settings, field and crop characteristics, as well as climatic conditions. Once the “Digital Tree” is built, it will be possible to optimize olive plant protection for a more accurate, targeted, and sustainable agrochemical application through the appropriate choice of operational and managerial parameters, considering both technical and environmental aspects.
Iris type:
4.1 Contributo in Atti di convegno
Keywords:
Machine learning; Olive; Smart technologies; Spray quality; Spraying equipment; Sustainability
List of contributors:
Bernardi, B.; Benalia, S.; Abenavoli, L. M.; Costa, G.; Ortale, R.; Macri, D.; Forestiero, A.; Manetto, G.; Longo, D.; Privitera, S.; Cerruto, E.
Authors of the University:
ABENAVOLI Lorenzo Maria Massimo
BERNARDI Bruno
Benalia Souraya
Handle:
https://iris.unirc.it/handle/20.500.12318/157226
Book title:
Lecture Notes in Civil Engineering
Published in:
LECTURE NOTES IN CIVIL ENGINEERING
Series
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URL

https://link.springer.com/chapter/10.1007/978-3-031-84212-2_40
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