Probabilistic Bayesian Neural Networks for olive phenology prediction in precision agriculture
Authors: Susanna Marchi D. Guidotti M. Staiano R. Siciliano
Date: 01.09.2024
Ecological Inofrmatics
Abstract
Plant phenology is the study of cyclical events in a plant life cycle such as leaf bud burst, flowering, and fruiting. In this article the problem of olive phenology prediction is addressed through the use of Deep Learning. Although Neural Networks have already been used in this area, to the best of our knowledge, this is the first implementation of Probabilistic Bayesian Neural Networks for olive phenology prediction. This architecture gives particular emphasis to estimating the model uncertainty, both aleatoric and epistemic. The Bayesian Inference method, more precisely the Variational Inference one, is compared with the Monte Carlo Dropout technique, which is known to be a less computationally intensive approximation of Variational Inference. For validation purposes, models performance is compared to the state-of-the-art results.
BIB_text
title = {Probabilistic Bayesian Neural Networks for olive phenology prediction in precision agriculture},
journal = {Ecological Inofrmatics},
pages = {102723},
volume = {82},
keywds = {
Bayesian inference; Modeling neural networks; Monte Carlo Dropout; Olive phenology; Precision agriculture
}
abstract = {
Plant phenology is the study of cyclical events in a plant life cycle such as leaf bud burst, flowering, and fruiting. In this article the problem of olive phenology prediction is addressed through the use of Deep Learning. Although Neural Networks have already been used in this area, to the best of our knowledge, this is the first implementation of Probabilistic Bayesian Neural Networks for olive phenology prediction. This architecture gives particular emphasis to estimating the model uncertainty, both aleatoric and epistemic. The Bayesian Inference method, more precisely the Variational Inference one, is compared with the Monte Carlo Dropout technique, which is known to be a less computationally intensive approximation of Variational Inference. For validation purposes, models performance is compared to the state-of-the-art results.
}
doi = {10.1016/j.ecoinf.2024.102723},
date = {2024-09-01},
}
