Correlations Among Vegetation Indices and Peanut Traits During Different Crop Development Stages

Abstract

Active optical sensors have been widely used for the spatial and temporal monitoring of peanut culture because they are accurate, non-destructive methods for rapidly obtaining data. The objective of this study was to determine the optimal stage of crop growth for collecting sensor readings based on correlations between quality indicators. In addition, we compared vegetation indices (Normalized Difference Vegetation Index [NDVI], Normalized Difference Red-Edge Index, [NDRE], and Inverse Ratio Vegetation Index, [IRVI]) by monitoring temporal variability in the peanut crop in order to determine which of them obtained the best reading quality throughout the process. The experiment was performed on the 2016/17 crop in the agricultural area of the municipality of Dumont in the state of São Paulo, Brazil. The experimental design was based on the basic assumptions of statistical quality control and contained 63 sample points in a 30 × 30 m grid. The parameters were evaluated at 30, 45, 60, 75, and 119 days after sowing (DAS) using proximal sensing with GreenSeeker and OptRX sensors. We found that 45 and 60 DAS were the optimal times for monitoring peanut crop variability. For spatiotemporal monitoring of the culture with control charts, NDRE showed the best readings throughout the process when compared to NDVI and IRVI.

Using UAV-Based Photogrammetry to Obtain Correlation between the Vegetation Indices and Chemical Analysis of Agricultural Crops

“…The indices usually originate from computing at least two spectral images, selected in such a manner that the vegetation reflectivity changes become prominent. In the majority of cases, the indices are functionally equivalent, and more than 150 have been presented in the literature to date; however, only a small subset of these rest on a solid biophysical basis or were systematically tested [47][48][49][50][51][52]. Our experiment verifies possible correlations in three proportional indices, computed via a normalized proportion of surface reflectivities.…”

Mapping the Urban Atmospheric Carbon Stock by LiDAR and WorldView-3 Data

“…Before starting the Nearest Neighbour (NN) approach, an index known as Canopy Content Chlorophyll Index (CCCI) is used to separate the grasses from the vegetation class. This index can be calibrated as follows, CCCI = NDRE/NDVI2 where, Normalized Difference Red Edge index [119] NDRE = NIR2 − Red Edge/NIR2 + Red Edge, NDVI2 = NIR2 − Red/NIR2 + Red Then the Nearest Neighbour (NN) algorithm [120] was performed, which is a supervised classification technique that classified all objects in the entire image based on the selected samples and the defined statistics [94]. For the sample selection and algorithm training, the sample plots had been considered.…”

Estimation of the Optimum Size of Plots for Soybean Radiometer Experiments

“…The use of optical proximity sensors is a promising technique for crop management, as it is an accurate and non-destructive method (CARNEIRO et al, 2019, STOCHER et al, 2019, and can be used to detect variations in the leaf area of plants attacked by diseases or deficiency in fertilization, serving as a parameter to estimate damage in production (CEREZINI et al, 2016;KAPP JUNIOR, 2016;BENEDUZZI et al, 2017;SANTOS et al, 2017).…”