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

Agronomy, ISSN: 2073-4395, Vol: 12, Issue: 7
2022
  • 10
    Citations
  • 0
    Usage
  • 32
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    10
    • Citation Indexes
      10
  • Captures
    32

Article Description

The monitoring and determination of peanut maturity are fundamental to reducing losses during digging operation. However, the methods currently used are laborious and subjective. To solve this problem, we developed models to access peanut maturity using images from unmanned aerial vehicles (UAV) and satellites. We evaluated an area of approximately 8 hectares in which a regular grid of 30 points was determined with weekly evaluations starting at 90 days after sowing. Two Artificial Neural Networking (ANN) were used with Radial Basis Function (RBF) and Multilayer Perceptron (MLP) to predict the Peanut Maturity Index (PMI) with the spectral bands available from each sensor. Several vegetation indices were used as input to the ANN, with the data being split 80/20 for training and validation, respectively. The vegetation index, Normalized Difference Red Edge Index (NDRE), was the most precise coefficient of determination (R2 = 0.88) and accurate mean absolute error (MAE = 0.06) for estimating PMI, regardless of the type of ANN used. The satellite with Normalized Difference Vegetation Index (NDVI) could also determine PMI with better accuracy (MAE = 0.05) than the NDRE. The performance evaluation indicates that the RBF and MLP networks are similar in predicting peanut maturity. We concluded that satellite and UAV images can predict the maturity index with good accuracy and precision.

Bibliographic Details

Jarlyson Brunno Costa Souza; Samira Luns Hatum de Almeida; Mailson Freire de Oliveira; Armando Lopes de Brito Filho; Mariana Dias Meneses; Rouverson Pereira da Silva; Adão Felipe Dos Santos

MDPI AG

Agricultural and Biological Sciences

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