Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks
نویسندگان
چکیده
The monitoring and determination of peanut maturity are fundamental to reducing losses during digging operation. However, the methods currently used laborious subjective. To solve this problem, we developed models access using images from unmanned aerial vehicles (UAV) satellites. We evaluated an area approximately 8 hectares in which a regular grid 30 points was determined with weekly evaluations starting at 90 days after sowing. Two Artificial Neural Networking (ANN) were Radial Basis Function (RBF) Multilayer Perceptron (MLP) predict Peanut Maturity Index (PMI) spectral bands available each sensor. Several vegetation indices as input ANN, data being split 80/20 for training validation, respectively. index, Normalized Difference Red Edge (NDRE), most precise coefficient (R2 = 0.88) accurate mean absolute error (MAE 0.06) estimating PMI, regardless type ANN used. satellite Vegetation (NDVI) could also determine PMI better accuracy 0.05) than NDRE. performance evaluation indicates that RBF MLP networks similar predicting maturity. concluded UAV can index good precision.
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ژورنال
عنوان ژورنال: Agronomy
سال: 2022
ISSN: ['2156-3276', '0065-4663']
DOI: https://doi.org/10.3390/agronomy12071512