Integrating biofumigation and phyto-spectral monitoring: Advancing sustainable agricultural practices

Biofumigation is a sustainable agricultural practice that utilizes the biocidal properties of natural compounds, such as isothiocyanates (ITCs) derived from glucosinolates (GSLs), to control soilborne pathogens and improve soil health. This study evaluates the biofumigant potential of various Brassicaceae species, emphasizing their biomass production, GSLs concentrations, and their utility in sustainable soil management under frost-prone conditions in central Spain over two different growing seasons. Additionally, phyto-spectral indices derived from remote sensing technologies, including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Red Edge (NDRE), and Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil Adjusted Vegetation Index (TCARI/OSAVI), were obtained along six flights. Data have been analyzed to optimize the timing of biofumigant incorporation into the soil and assess their correlation with biomass and GSLs content. The findings identify Brassica juncea and B. carinata as the most effective biofumigant species due to their high aliphatic glucosinolate content. In contrast, B. napus showed superior frost tolerance. Vegetation indices demonstrated strong relationships with biomass and GSLs content during key growth stages, with NDRE showing especially good performance under certain developmental conditions. While, regression models obtained with machine learning tools enhanced the predictive accuracy for biomass and GSLs concentrations for the entire growing period, demonstrating their potential for integrating precision agriculture tools into biofumigation practices. This study advances the understanding of biofumigant crop management by incorporating spectral technologies to enhance efficiency and decision-making, aligning with sustainable agriculture and precision farming principles. • NDRE showed strongest correlation with biomass and glucosinolates content. • Remote sensing optimized the timing of biofumigant incorporation. • ML models improved accuracy of glucosinolate predictions in biofumigant biomass.