Using UAV and Multispectral Images to Estimate Peanut Maturity Variability on Irrigated and Rainfed Fields Applying Linear Models and Artificial Neural Networks

Abstract

Using UAV and multispectral images has contributed to identifying field variability and improving crop management through different data modeling methods. However, knowledge on application of these tools to manage peanut maturity variability is still lacking. Therefore, the objective of this study was to compare and validate linear and multiple linear regression with models using artificial neural networks (ANN) for estimating peanut maturity under irrigated and rainfed conditions. The models were trained (80% dataset) and tested (20% dataset) using results from the 2018 and 2019 growing seasons from irrigated and rainfed fields. In each field, plant reflectance was collected weekly from 90 days after planting using a UAV-mounted multispectral camera. Images were used to develop vegetation indices (VIs). Peanut pods were collected on the same dates as the UAV flights for maturity assessment using the peanut maturity index (PMI). The precision and accuracy of the linear models to estimate PMI using VIs were, in general, greater in irrigated fields with R2 > 0.40 than in rainfed areas, which had a maximum R2 value of 0.21. Multiple linear regressions combining adjusted growing degree days (aGDD) and VIs resulted in decreased RMSE for both irrigated and rainfed conditions and increased R2 in irrigated areas. However, these models did not perform successfully in the test process. On the other hand, ANN models that included VIs and aGDD showed accuracy of R2 = 0.91 in irrigated areas, regardless of using Multilayer Perceptron (MLP; RMSE = 0.062) or Radial Basis Function (RBF; RMSE = 0.065), as well as low tendency (1:1 line). These results indicated that, regardless of the ANN architecture used to predict complex and non-linear variables, peanut maturity can be estimated accurately through models with multiple inputs using VIs and aGDD. Although the accuracy of the MLP or RBF models for irrigated and rainfed areas separately was high, the overall ANN models using both irrigated and rainfed areas can be used to predict peanut maturity with the same precision.

Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks

“…A result was also observed in this work in which the model generated from the satellite NDVI could express the actual variability of PMI while NDRE showed higher sensitivity to high vegetation density. In addition, our results from satellite images and RNA were more accurate than those of the model tested by [10] using NDVI as an input; we found precision levels of 89% (R 2 = 0.89), independently of the RNA tested (Figure 5h). Ref.…”

“…This response was similar to our results. Recently [10], proposed the use of RNA to predict peanut maturity in two conditions, irrigated and dryland fields using UAV and multispectral cameras, and the precision models for both fields were higher than 0.90. In this paper, we found similar model performances, especially using the RBF model as input VI calculated by UAV images (R 2 = 0.91-Figure 6c).…”

“…It is possible to create predictive peanut maturity index (PMI) algorithms using remote sensing and digital technologies. Despite some available studies using these technics, some have demonstrated only linear relationships and model fitting [8][9][10]. Ref.…”