Federico Cum
A Neural network application for impedance-based plant monitoring: from a development framework towards edge computing.
Rel. Maurizio Martina, Danilo Demarchi, Umberto Garlando. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Elettronica (Electronic Engineering), 2022
|
Preview |
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (7MB) | Preview |
Abstract
During the 21st century, the world is experiencing the most evident effects of climate change, such as desertification, rising temperatures, higher frequency of extreme meteorological events, rising sea levels, and lack of potable water. Furthermore, the world population is constantly growing and is expected to reach almost 10 billion by 2050. In this scenario, agriculture is severely challenged due to the increasing demand for food and the worsening of environmental conditions: new techniques are needed to improve the yield of plantations by saving as many valuable resources, such as water and energy, as possible. The field of smart agriculture is working in this direction, and it is developing technologies that can make farming more efficient and autonomous.
This thesis work aims to develop a baseline technology able to detect the state of the health of a plant autonomously by employing an innovative and promising type of sensor based on plant stem impedance, environmental sensors, and machine learning techniques
Relatori
Anno Accademico
Tipo di pubblicazione
Numero di pagine
Corso di laurea
Classe di laurea
URI
![]() |
Modifica (riservato agli operatori) |
