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Evaluation of grain yield based on digital images of rice canopy

Author:
Liu, Kailou, Li, Yazhen, Han, Tianfu, Yu, Xichu, Ye, Huicai, Hu, Huiwen, Hu, Zhihua
Source:
Plant methods 2019 v.15 no.1 pp. 28
ISSN:
1746-4811
Subject:
Oryza sativa, blue light, canopy, digital images, equations, fertilizer rates, grain yield, green light, models, nitrogen, nitrogen fertilizers, plant density, prediction, red light, rice, China
Abstract:
BACKGROUND: Rice canopy changes are associated with changes in the red light (R), green light (G), and blue light (B) value parameters of digital images. To rapidly diagnose the responses of rice to nitrogen (N) fertilizer application and planting density, a simple model based on digital images was developed for predicting and evaluating rice yield. RESULTS: N application rate and planting density had significant effects on rice yield. Rice yield first increased and then decreased with increasing of N rates, while the rice yield always increased significantly with increasing planting density. The normalized redness intensity (NRI), normalized greenness intensity (NGI), and normalized blueness intensity (NBI) values of the rice canopy varied among stages; however, they were primarily affected by N fertilizer rates, while planting density had no significant effects. Furthermore, the significant relationships of grain yield with NRI and NBI at the late filling stage could be fitted by quadratic equations, but there was no significant relationship observed between grain yield and NGI across all stages. In addition, a field validation experiment showed that the predicted yield based on the fitted quadratic equations was consistent with the measured yield. CONCLUSION: The NRI, NGI, and NBI values of rice canopy were mainly affected by N fertilizer rates, while the planting density had no significant effect. The significant relationships between grain yield with NRI and NBI at the late filling stage could be fitted by quadratic equations. Therefore, the canopy NRI and NBI at the late filling stage as measured by digital photography could be used to predict grain yield in southern China.
Agid:
6344797