A survey of high resolution image processing techniques for cereal crop growth monitoring

This paper presents a survey of image processing techniques proposed in the literature for extracting key cereal crop growth metrics from high spatial resolution, typically proximal images. The descriptive crop growth metrics considered are: crop canopy cover, above ground biomass, leaf area index (including green area index), chlorophyll content, and growth stage. The paper includes an overview of relevant fundamental image processing techniques including camera types, colour spaces, colour indexes, and image segmentation. The descriptive crop growth metrics are defined. Reference methods for ground-truth measurement are described. Image processing methods for metric estimation are described in detail. The performance of the methods is reviewed and compared. The survey reveals limitations in image processing techniques for cereal crop monitoring such as lack of robustness to lighting conditions, camera position, and self-obstruction. Directions for future research to improve performance are identified.