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

Abstract

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.

Comparing Regression and Classification Models to Estimate Leaf Spot Disease in Peanut (Arachis hypogaea L.) for Implementation in Breeding Selection

“…Successful applications of HTP and ML methods in plant breeding for the prediction of important agronomic traits including disease identification have been reported in several crops including tomato (Solanum lycopersicum L.) [29], maize (Zea mays L.) [30], radish (Raphanus sativus L.) [31], and sugar beet (Beta vulgaris) [32]. In peanut breeding, HTP and ML methods have been applied for agronomic traits such as plant height [33], leaf area [34], and pod maturity [35], but these ML methods have barely been applied for selection for LLS resistance.…”