Spatial variability of biophysical multispectral indexes under heterogeneity and anisotropy for precision monitoring

Abstract

The study aimed to characterize the spatial structure of variability of biophysical indexes of vegetation through images obtained by Unmanned Aerial Vehicles under strong heterogeneity and anisotropy, using geostatistical procedures. Plots with different types and densities of culture were evaluated in a didactic vegetable garden. Five vegetation indexes obtained from aerial multispectral camera images were evaluated parallel with geostatistical analysis and anisotropy investigation for multiscale spatial modeling. For the studied domain, geometric anisotropy was identified for the biometric indexes. The spherical model presented a better fit when anisotropy was not considered, whereas the exponential model had the best performance in the anisotropic analysis. Contrasting targets were better identified in multispectral images and considering anisotropy. The Soil-Adjusted Vegetation Index is recommended for similar applications.

UAV-Based Classification of Intercropped Forage Cactus: A Comparison of RGB and Multispectral Sample Spaces Using Machine Learning in an Irrigated Area

“…For widespread adoption in agriculture, it is essential to rely on measured data and integrate sources to ensure practical robustness [10]. Unmanned Aerial Vehicles (UAVs) equipped with multispectral (MS) sensors offer several benefits in precision agriculture, enabling the acquisition of high-resolution data that capture the spatial variability of attributes and crops [11][12][13]. Cao et al [14] compared both RGB and multispectral imagery from UAV to map Stay Green (SG) phenotyping of diversified wheat germplasm.…”

“…Although visible Red-Green-Blue (RGB) images could be valuable information, spectral indices containing red edge or near-infrared band were more effective for proper crop classification. Unmanned Aerial Vehicles (UAVs) in agriculture allow for capturing aerial images with high spatial resolution due to their low flight altitude [12,[15][16][17].…”

“…Utilizing spectral responses and crop classification allows adjusting management strategies for enhancing productivity, particularly in the case of intercropped cultivations. Despite recent research employing multispectral UAV remote sensing, exemplified by the study conducted by Lourenço et al [12] for highly heterogeneous fields, and the integration of machine learning for agricultural mapping through remote sensing, as demonstrated in the work of Iqbal et al [22], investigations that jointly incorporate artificial intelligence and UAV images for crop classification are still in their early stages in the literature. Specifically, there is a significant need for Machine Learning (ML) applications to interpret multispectral and RGB images acquired by UAVs for the automatic classification of intercropped forage cacti in irrigated areas of the Brazilian semiarid.…”