Hyperspectral imaging for rice cultivation: Applications, methods and challenges

Abstract

Hyperspectral imaging has become a valuable remote sensing tool due to the development of advanced remote acquisition systems with high spatial and spectral resolution, and the continuous developments on more efficient computing resources to handle the high volume of data. For this reason, hyperspectral image analysis has found important uses in precision agriculture, where the health status of crops in various stages of the production process can be assessed from their spectral signatures. This has similarly been the case for rice cultivation, which represents one of the most valuable crops worldwide in terms of gross production value, global consumption rates, and food security reserves. To maximize the productivity of this activity and minimize economic and food crop losses, various precision agriculture techniques to optimize yields by managing production inputs and monitoring plant health have been developed. Such applications include landcover classification, cultivar identification, nitrogen level assessment, chlorophyll content estimation and the identification of various factors, such as the presence of pests, weeds, disease or pollutants. The current work highlights and summarizes various aspects of interest of the main studies on hyperspectral imaging applications for rice cultivation. For instance, several tables summarize the most relevant work on the application of hyperspectral imaging for rice cultivation based on their acquisition methods, spectral region, rice species, and inferred magnitudes, among other parameters. In addition, we identify challenges across the field that limit the widespread deployment of hyperspectral imaging applications. Among these challenges, adequate modeling of various dynamic local factors and their influence on the analysis is a main concern. The main objective of this review is to provide a reference for future works that addresses the main challenges, and accelerate the development of deployable end user technologies to meet current global Sustainable Development Goals, in a manner that is resilient towards the increasingly dynamic growing conditions of rice plants expected by global climate change.

Keywords

• precision agriculture
• hyperspectral
• remote sensing
• rice
• spectral analysis