Canopy coverage of wheat measured with high accuracy using the HSV colour model and relative depth estimation model, MiDaS

Abstract

Canopy coverage calculated from 2D images effectively estimates several parameters, such as dry weight above the ground, stem number, and risk of lodging, without destroying the crops. This study aimed to clarify how to accurately calculate canopy coverage, regardless of environmental conditions, using various colour models and a relative depth estimation model, MiDaS. For wheat cultivated in 2022/23, the canopy coverage at the initiation of the stem elongation stage (CC30), calculated using only the HSV colour model, was overestimated because of the presence of weeds. However, when the heatmap generated using MiDaS was combined with the results extracted from the HSV colour model, it showed a CC30 similar to the accurate CC30 calculated by hand drawing, regardless of the wheat variety, sowing pattern, and location of the image taken, with an RMSE as low as 3.48. The CC30 calculated by combining the HSV colour model and MiDaS was significantly positively correlated with the number of stems, dry weight above ground, and nitrogen content above ground at the initiation of the stem elongation stage of ‘Sanukinoyume 2009’ grown in 2021/22 and 2022/23 (n = 72, r > 0.798). The combination of the HSV colour model and MiDaS effectively and accurately calculates canopy coverage regardless of environmental conditions and can be used for growth diagnosis and variable nitrogen fertiliser application after the initiation of the stem elongation stage.

Keywords

• Wheat
• MiDaS
• canopy coverage
• sensing
• HSV colour model