Real-Time AI-Assisted Push-Broom Hyperspectral System for Precision Agriculture
Igor Neri,
Silvia Caponi,
Francesco Bonacci,
Giacomo Clementi,
Francesco Cottone,
Luca Gammaitoni,
Simone Figorilli,
Luciano Ortenzi,
Simone Aisa,
Federico Pallottino,
Maurizio Mattarelli
Affiliations
Igor Neri
Department of Physics and Geology, University of Perugia, Via A. Pascoli, 06123 Perugia, Italy
Silvia Caponi
Materials Foundry (IOM-CNR), National Research Council, c/o Department of Physics and Geology, Via A. Pascoli, 06123 Perugia, Italy
Francesco Bonacci
Department of Physics and Geology, University of Perugia, Via A. Pascoli, 06123 Perugia, Italy
Giacomo Clementi
Department of Physics and Geology, University of Perugia, Via A. Pascoli, 06123 Perugia, Italy
Francesco Cottone
Department of Physics and Geology, University of Perugia, Via A. Pascoli, 06123 Perugia, Italy
Luca Gammaitoni
Department of Physics and Geology, University of Perugia, Via A. Pascoli, 06123 Perugia, Italy
Simone Figorilli
Consiglio per la Ricerca in Agricoltura e l’Analisi Dell’Economia Agraria (CREA)—Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via della Pascolare 16, Monterotondo, 00015 Rome, Italy
Luciano Ortenzi
Consiglio per la Ricerca in Agricoltura e l’Analisi Dell’Economia Agraria (CREA)—Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via della Pascolare 16, Monterotondo, 00015 Rome, Italy
Simone Aisa
Materials Foundry (IOM-CNR), National Research Council, c/o Department of Physics and Geology, Via A. Pascoli, 06123 Perugia, Italy
Federico Pallottino
Consiglio per la Ricerca in Agricoltura e l’Analisi Dell’Economia Agraria (CREA)—Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via della Pascolare 16, Monterotondo, 00015 Rome, Italy
Maurizio Mattarelli
Department of Physics and Geology, University of Perugia, Via A. Pascoli, 06123 Perugia, Italy
In the ever-evolving landscape of modern agriculture, the integration of advanced technologies has become indispensable for optimizing crop management and ensuring sustainable food production. This paper presents the development and implementation of a real-time AI-assisted push-broom hyperspectral system for plant identification. The push-broom hyperspectral technique, coupled with artificial intelligence, offers unprecedented detail and accuracy in crop monitoring. This paper details the design and construction of the spectrometer, including optical assembly and system integration. The real-time acquisition and classification system, utilizing an embedded computing solution, is also described. The calibration and resolution analysis demonstrates the accuracy of the system in capturing spectral data. As a test, the system was applied to the classification of plant leaves. The AI algorithm based on neural networks allows for the continuous analysis of hyperspectral data relative up to 720 ground positions at 50 fps.
