Previous Article in event
Next Article in event
Exploring the Correlation Between Gaseous Emissions and Phenological Phases in Tomato Crops Through Machine Learning
*
1
,
2
,
1
,
3
,
Sandro Gherardi
1
,
1
,
1
Academic Editor: Stefano Mariani
Published:
07 November 2025
by MDPI
in The 12th International Electronic Conference on Sensors and Applications
session Smart Agriculture Sensors
https://doi.org/10.3390/ECSA-12-26543
(registering DOI)
Abstract:
Nowadays, agriculture is facing significant challenges, including climate change. Precision agriculture might address these issues by optimizing resource use and promoting sustainability. In this work, a case study of tomato crop monitoring is presented, employing the large amount of gas sensor data collected over three years (2020–2022) to develop models for phenological phase classification. A k-NN classifier achieved accuracies above 99% across multiple train/test splits, with AUC, sensitivity, specificity, precision and F1-score above 98%. Results demonstrate the feasibility of low-computational-cost systems capable of real-time detection of the transition point between plants’ developmental stages.
Keywords: Machine Learning, Phenological Phases, Precision agriculture, Volatile organic compounds, MOX gas sensors
Sandro Gherardi
,
