UAV remote sensing of agronomic parameters and yield in chickpea and lentil

Grain legumes cropping has been purposed as a pivotal practise for facing future issues in terms of food security and agroecosystem stability. Despite the importance of such cultures, the literature is lacking in knowledge of monitoring grain legumes performance with remote sensing data. Hence, this study investigated these aspects in chickpea (CH) and lentil (LN), grown in Udine (Italy) during the growing season 2022. Crop dry biomass (BMAG), dry matter content (DMC) and leaf area index (LAI) were correlated with multispectral data acquired by unmanned aerial vehicle (UAV) on seven dates during the growing season. Near-infrared (NIR) band performed as the best proxy of LAI, while for DMC, best correlation was obtained with normalized difference chlorophyll index (NDI). BMAG also was correlated with NDI, and correlation strength improved by implementing the cumulative elaboration of the index. Cumulative indices performed also as proxies of yield; best index was modified green-red vegetation index (MGRVI).