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.

Peanut yield estimation models using Machine Learning techniques and Google Earth Engine

“…These characteristics make difficult the analysis of important parameters of the peanut crop, such as the optimal harvest time, which is directly related to pod maturity index (PMI). Studies indicate that the use of SR and ANN tools are strongly capable of predicting important variables of the peanut culture, such as the MIP, helping the producer in the assetivity of the ideal point of harvest, which reduces the quantitative and qualitative losses (Souza et al, 2022). For yields this estimate becomes even more difficult due to the high variability of this variable in the production fields.…”

“…IR's transform spectral bands to a single variable and thus minimize the effects of soil, topography, and viewing angle on the spectral response of the desired feature. No yield estimation model has yet been created for the peanut crop, and the use of IR's along with the use of Artificial Neural Networks can be an alternative to assist the producer in the evaluation of several agronomic parameters of the peanut crop, as is the case of peanut maturity estimation (Santos., 2021, Souza et al, 2022.…”

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.…”

Integrating Satellite and UAV Technologies for Maize Plant Height Estimation Using Advanced Machine Learning

“…Efficient and accurate assessment of plant height is paramount in appraising maize's growth potential, furnishing agronomists with essential insights into plant development for well-informed decision-making regarding field management practices. In recent years, innovative methodologies encompassing remote sensing techniques, unmanned aerial vehicle (UAV) imagery, and the power of machine learning (ML) have been steadily gaining prominence in modern agricultural paradigms [3][4][5][6]. In this context, achieving swift and accurate large-scale estimations of maize plant height and enabling dynamic growth monitoring [7] play a pivotal role in amplifying crop management strategies [8], facilitating evaluations of cultivars in the fields, and empowering informed decision-making among agricultural stakeholders.…”

“…However, the underexplored potential in vegetation indices, which directly correlate with canopy structural inputs and spectral responses [16], has been relatively underutilized in maize plant height estimation. Another significant advancement in agriculture is the integration of machine learning, which has substantially enhanced the processing of extensive data sets and demonstrated precision in estimating critical agronomic parameters [6,17,18].…”