Artificial intelligence enables mobile soil analysis for sustainable agriculture
Abstract
For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits greatly from real-time, on-the-spot analysis of soil at low cost. Colorimetric paper sensors are ideal candidates for cheap and rapid chemical spot testing. However, their field application requires previously unattained paper sensor reliability and automated readout and analysis by means of integrated mobile communication, artificial intelligence, and cloud computing technologies. Here, we report such a mobile chemical analysis system based on colorimetric paper sensors that operates under tropical field conditions. By mapping topsoil pH in a field with an area of 9 hectares, we have benchmarked the mobile system against precision agriculture standards following a protocol with reference analysis of compound soil samples. As compared with routine lab analysis, our mobile soil analysis system has correctly classified soil pH in 97% of cases while reducing the analysis turnaround time from days to minutes. Moreover, by performing on-the-spot analyses of individual compound sub-samples in the field, we have achieved a 9-fold increase of spatial resolution that reveals pH-variations not detectable in compound mapping mode. Our mobile system can be extended to perform multi-parameter chemical tests of soil nutrients for applications in environmental monitoring at marginal manufacturing cost.
Keywords
Cite
@article{arxiv.2207.10537,
title = {Artificial intelligence enables mobile soil analysis for sustainable agriculture},
author = {Ademir Ferreira da Silva and Ricardo Luis Ohta and Jaione Tirapu Azpiroz and Matheus Esteves Fereira and Daniel Vitor Marçal and André Botelho and Tulio Coppola and Allysson Flavio Melo de Oliveira and Murilo Bettarello and Lauren Schneider and Rodrigo Vilaça and Noorunisha Abdool and Vanderlei Junior and Wellington Furlaneti and Pedro Augusto Malanga and Mathias Steiner},
journal= {arXiv preprint arXiv:2207.10537},
year = {2022}
}
Comments
Main article: 23 pages and 4 figures. Supplementary Information: 12 pages and 12 figures. Includes DOI for accompanying data and images datasets and URL for github repository of accompanying python code