Federico Cum

Ph.D. candidate in Ingegneria Elettrica, Elettronica E Delle Comunicazioni , 38^th cycle (2022-2025)
Department of Electronics and Telecommunications (DET)

Docente esterno e/o collaboratore didattico
Department of Electronics and Telecommunications (DET)

federico.cum@polito.it

Profile

PhD

Research topic

Monitoring of agricultural crops through the use of drones and innovative IoT sensors

Tutors

Danilo Demarchi
Christian Di Benedetto

Research interests

Analog, Power and Mixed-Signal Circuits and Embedded Systems

Big Data, Machine Learning, Neural Networks and Data Science

Biography

I am an electronic engineer graduated from the Polytechnic of Turin. I am passionate about working in the field of embedded systems and machine learning. During my master thesis I had the opportunity to get in touch with the agrifood sector, and now during my PhD I will focus on the integration of IoT sensors for agriculture aboard drones. I am excited to continue to grow professionally and to contribute my expertise in electronics and emerging technologies to the agricultural sector.

Teaching

Teachings

Master of Science

Circuits and electrical engineering and Electronic systems (modulo di Electronic systems). A.A. 2024/25, AGRITECH ENGINEERING. Collaboratore del corso
Circuits and electrical engineering and Electronic systems (modulo di Electronic systems). A.A. 2023/24, AGRITECH ENGINEERING. Collaboratore del corso

Publications

Latest publications View all publications in Porto@Iris

Cum, Federico; Calvo, Stefano; Sanginario, Alessandro; Garlando, Umberto (2024)
Evaluation of Machine Learning Models for Water Stress Detection Using Stem Impedance. In: IEEE TRANSACTIONS ON AGRIFOOD ELECTRONICS., pp. 1-9. ISSN 2771-9529
Contributo su Rivista
Cum, Federico; Calvo, Stefano; Demarchi, Danilo; Garlando, Umberto (2023)
Machine Learning Models Comparison for Water Stress Detection Based on Stem Electrical Impedance Measurements. In: 2023 IEEE Conference on AgriFood Electronics (CAFE), Torino (Italy), 25-27 September 2023, pp. 108-112. ISBN: 979-8-3503-2711-3
Contributo in Atti di Convegno (Proceeding)
Barezzi, M; Cum, F; Garlando, U; Martina, M; Demarchi, D (2022)
On the impact of the stem electrical impedance in neural network algorithms for plant monitoring applications. In: 2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Perugia, Italy, 03-05 November 2022, pp. 131-135. ISBN: 978-1-6654-6998-2
Contributo in Atti di Convegno (Proceeding)