Multi-Sensor Approach for Early Crop Disease Detection: Socio-Economic and Ecological Implications.

Abstract

This study analyses an innovative multi-sensor remote sensing technology for the early and accurate detection of crop diseases, viewing technological integration into agriculture as a socio-technical process. The key objective is to identify the multiple economic, environmental and social costs and benefits incurred by the technology, using a novel methodology (process modelling + ACBA). This research highlights the technological contribution to reducing chemical use, lowering input costs, and improving both environmental and human health. Moreover, the integration facilitates the transition toward bio-based economies by decreasing pesticide reliance, conserving biodiversity, and aligning agricultural practices with long-term sustainability objectives. Conversely, it poses several challenges, including high investments, technical complexity, system interoperability issues, and privacy concerns. It should be noted, however, that the study was conducted in an experimental context, and thus the results are limited. Further research is encouraged to conduct the multi-dimensional impact analysis in diverse contexts to enable sustainable technological integration.