Work

Exploring the Correlation Between Gaseous Emissions and Phenological Phases in Tomato Crops Through Machine Learning

Year: 2025

Type: conference-paper

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 a 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.

DOI: 10.3390/ecsa-12-26543