Working Paper Article Version 1 This version is not peer-reviewed

Wheat Biomass Estimation in Different Growth Stage Based on Color and Texture Features of UAV Images

Version 1 : Received: 25 December 2019 / Approved: 26 December 2019 / Online: 26 December 2019 (12:27:49 CET)

How to cite: Liu, Y.; Yang, T.; Chen, C.; Liu, T.; Sun, C.; Wu, W.; Yao, Z.; Wang, D.; Li, R.; Huo, Z. Wheat Biomass Estimation in Different Growth Stage Based on Color and Texture Features of UAV Images. Preprints 2019, 2019120355 Liu, Y.; Yang, T.; Chen, C.; Liu, T.; Sun, C.; Wu, W.; Yao, Z.; Wang, D.; Li, R.; Huo, Z. Wheat Biomass Estimation in Different Growth Stage Based on Color and Texture Features of UAV Images. Preprints 2019, 2019120355

Abstract

In order to realize rapid and nondestructive monitoring of wheat biomass in field, field experiments based on different densities, nitrogen fertilizer and variety treatments were studied. RGB images of wheat in the main growth stage were obtained by UAV, and wheat color and texture feature indices were obtained by image processing, and wheat biomass was obtained by field sampling in the same period. Then the relationship between different color and texture feature indices and wheat biomass was analyzed to select the color and texture feature index suitable for wheat biomass estimation. The results showed that there was a high correlation between image color index and wheat biomass in different stages, and most of them reached a very significant correlation level. However, the correlation between image texture feature index and wheat biomass was poor, only a few indexes reached significant or extremely significant correlation level. Based on the above results, the color indices with the highest correlation to wheat biomass or the combining indices of color and texture feature in different growth stage were used to construct estimation model of wheat biomass. The models were validated using independently measured biomass data, and the correlation between simulated and measured values reached the significant level, RMSE were smaller. This indicated that the estimated results by the models were reliable and accurate. It also showed that the estimation models of wheat biomass combined with color and texture feature indices of UAV image were better than the single color index models. The results would provide a new method for real-time monitoring of wheat field growth and biomass estimation.

Subject Areas

wheat; UAV image; color index; texture feature index; biomass

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