Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

• Published in: Frontiers in plant science Vol. 13; p. 925986
• Main Authors: Ma, Yiru, Ma, Lulu, Zhang, Qiang, Huang, Changping, Yi, Xiang, Chen, Xiangyu, Hou, Tongyu, Lv, Xin, Zhang, Ze
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 15.06.2022
• Subjects: Plant Science; RGB image; texture feature; UAV; vegetation indices; yield
• ISSN: 1664-462X; 1664-462X
• DOI: 10.3389/fpls.2022.925986

Yield monitoring is an important parameter to evaluate cotton productivity during cotton harvest. Nondestructive and accurate yield monitoring is of great significance to cotton production. Unmanned aerial vehicle (UAV) remote sensing has fast and repetitive acquisition ability. The visible vegetation indices has the advantages of low cost, small amount of calculation and high resolution. The combination of the UAV and visible vegetation indices has been more and more applied to crop yield monitoring. However, there are some shortcomings in estimating cotton yield based on visible vegetation indices only as the similarity between cotton and mulch film makes it difficult to differentiate them and yields may be saturated based on vegetation index estimates near harvest. Texture feature is another important remote sensing information that can provide geometric information of ground objects and enlarge the spatial information identification based on original image brightness. In this study, RGB images of cotton canopy were acquired by UAV carrying RGB sensors before cotton harvest. The visible vegetation indices and texture features were extracted from RGB images for cotton yield monitoring. Feature parameters were selected in different methods after extracting the information. Linear and nonlinear methods were used to build cotton yield monitoring models based on visible vegetation indices, texture features and their combinations. The results show that (1) vegetation indices and texture features extracted from the ultra-high-resolution RGB images obtained by UAVs were significantly correlated with the cotton yield; (2) The best model was that combined with vegetation indices and texture characteristics RF_ELM model, verification set R 2 was 0.9109, and RMSE was 0.91277 t.ha −1 . rRMSE was 29.34%. In conclusion, the research results prove that UAV carrying RGB sensor has a certain potential in cotton yield monitoring, which can provide theoretical basis and technical support for field cotton production evaluation.