An IoT-based data analysis system: A case study on tomato cultivation under different irrigation regimes
The exploitation of modern technologies in heterogeneous farming scenarios with different crops cultivation is nowadays an effective solution to implement the concept of Smart Agriculture (SA). Following this approach, in this study the tomato plants’ response to different irrigation regimes is investigated through the implementation of an Internet of Things (IoT)-oriented SA data collection and monitoring system. In particular, the experimentation is conducted on tomatoes grown at three different irrigation regimes: namely, at 100%, 60%, and 30% of the Italian irrigation recommendation service, denoted as Irriframe. The proposed platform, denoted as Agriware , is able to: (i) evaluate information from heterogeneous data sources, (ii) calculate agronomic indicators (e.g., Growing Degree Days, GDD), and (iii) monitor on-field parameters (e.g., water consumption). Different plant-related parameters have been collected to assess the response to water stress (e.g., Soil Plant Analysis Development (SPAD), chlorophyll content, fluorescence, and others), along with leaf color and final production evaluations. The obtained results show that the best irrigation regime, in terms of plant health and productivity, corresponds to 60% of Irriframe, allowing significant water savings for the cultivation. • Tomato is a crop sensitive to drought stress: assess costs and water saving. • Three irrigation regimes: 30%, 60%, 100% of Italian recommendation platform Irriframe. • Evaluation of tomato growth, yields and physiological traits for each water regime. • Agriware platform for data integration, indexes calculation, and on-field monitoring. • Water saving up to 40% is achievable for tomato plants.
Authors
- Federico Solari (ORCID: https://orcid.org/0000-0001-8365-965X)
- Giuseppe Vignali (ORCID: https://orcid.org/0000-0002-0129-4085)
- Tommaso Ganino (ORCID: https://orcid.org/0000-0001-5590-2747)
- Gianluigi Ferrari (ORCID: https://orcid.org/0000-0001-6688-0934)
- Luca Davoli (ORCID: https://orcid.org/0000-0002-4396-8885)
- Luca Preite (ORCID: https://orcid.org/0000-0002-4395-503X)
- Margherita Rodolfi
- Martina Galaverni
- Ilaria Marchioni (ORCID: https://orcid.org/0000-0001-5605-7557)
- Deborah Beghé (ORCID: https://orcid.org/0000-0002-0945-5365)
- Laura Belli (ORCID: https://orcid.org/0000-0001-5408-2265)
- Giulia Oddi (ORCID: https://orcid.org/0009-0000-5503-0053)
Institutions
- University of Parma (IT)
Publication Details
- Journal
- Computers and Electronics in Agriculture
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1016/j.compag.2024.109660
- Citations
- 15
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 8.99
Funders
- European Commission