A systematic review on precision agriculture applied to sunflowers, the role of hyperspectral imaging

Abstract

Sunflower is an annual species of the Asteraceae family, and it occupies a relevant position in the world market business as one of the most important oilseed crops. Given the current geopolitical situation and climate change, the agri-food supply chain of sunflower is in crisis. In this context, precision agriculture, especially remote sensing, can address demands for more production and greater sustainability. The aim of the present systematic review is to evaluate the available scientific literature on precision agriculture applied to sunflower crop, specifically the use of hyperspectral data to calculate vegetation indices or create crop growth models. The systematic review follows specific guidelines and a well-described review protocol. A total of 104 studies were included in the review, starting from raw search in different data sources (Scopus, Web of Science, Springer Link, and Science Direct) and following with the application of inclusion criteria. Results focused on the following main topics: crop management (i.e., management zones, yield prediction, vegetation indices correlations), sunflower crop growth monitoring (i.e., identify different growth stages and vegetation parameters), weed management, and industrial applications. The role of hyperspectral sensors has been thoroughly investigated to help choose ideal wavelengths related to vegetation indices. Future research should prioritise water stress management, time-saving evaluation of new sunflower hybrids, and crop growth models.

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