Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index

Abstract

Estimating the crop leaf area index (LAI) accurately is very critical in agricultural remote sensing, especially in monitoring crop growth and yield prediction. The development of unmanned aerial vehicles (UAVs) has been significant in recent years and has been extensively applied in agricultural remote sensing (RS). The vegetation index (VI), which reflects spectral information, is a commonly used RS method for estimating LAI. Texture features can reflect the differences in the canopy structure of rice at different growth stages. In this research, a method was developed to improve the accuracy of rice LAI estimation during the whole growing season by combining texture information based on wavelet transform and spectral information derived from the VI. During the whole growth period, we obtained UAV images of two study areas using a 12-band Mini-MCA system and performed corresponding ground measurements. Several VI values were calculated, and the texture analysis was carried out. New indices were constructed by mathematically combining the wavelet texture and spectral information. Compared with the corresponding VIs, the new indices reduced the saturation effect and were less sensitive to the emergence of panicles. The determination coefficient (R2) increased for most VIs used in this study throughout the whole growth period. The results indicated that the estimation accuracy of LAI by combining spectral information and texture information was higher than that of VIs. The method proposed in this study used the spectral and wavelet texture features extracted from UAV images to establish a model of the whole growth period of rice, which was easy to operate and had great potential for large-scale auxiliary rice breeding and field management research.

Construction of a Winter Wheat Comprehensive Growth Monitoring Index Based on a Fuzzy Degree Comprehensive Evaluation Model of Multispectral UAV Data

“…However, spectral information tends to saturate when vegetation canopy coverage is high and is easily affected by soil background reflectance in sparse plant areas. Additionally, previous research has shown that the texture information derived from UAV images can accurately predict winter wheat biomass [49,50]. Therefore, in this study, we introduced texture features based on vegetation indexes to construct an inversion model for comprehensive growth indicators of winter wheat that includes biomass.…”

Non-Destructive Monitoring of Peanut Leaf Area Index by Combing UAV Spectral and Textural Characteristics

“…In contrast to the spectral reflectance that focuses on the crop's internal optical responses, texture features reflect the external morphological characteristics of the crop, offering rich spatial structure information on the crop canopy [20]. Therefore, the capability of texture features in capturing the dynamic changes in crop canopy growth makes it suitable for improving the precision of LAI estimations.…”

“…Although numerous methods have been developed and provide promising LAI estimation results for rice [20,26], potato [28,29], maize [30,31], and other crops by combining both VI and TF to date, it is still remarkably challenging due to the following aspects. First, despite the combination of both, VI and TF have been demonstrated to have important value for monitoring LAIs of multiple crops; as far as we are aware, fewer studies specifically address the effect of both vegetation indices and texture features on the peanut LAI estimation since differences in the canopy structures of different crops might lead to changes in texture characteristics as well as spectral absorption and reflection characteristics.…”

Assessment of Soybean Lodging Using UAV Imagery and Machine Learning

“…Hence, this study aimed to investigate the potential of quantifying lodging scores of soybean breeding lines using UAV-based imagery and machine learning methods. The textural image features provide Supplementary Information about the object properties, which can help the heterogeneous crop field assessment [ 45 ]. The specific objectives were: (1) to develop an RGB image texture feature-based soybean lodging classification model using machine learning algorithms and (2) to assess the classification accuracies among different classification models.…”