Human limits in machine learning: prediction of potato yield and disease using soil microbiome data.

ABSTRACT:

Background

The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potential of machine learning models for understanding the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant performance from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network.

Results

Prediction improves when we add environmental features, such as soil properties and microbial density, along with microbiome data. Different preprocessing strategies show that human decis

SUBMITTER: Aghdam R 

PROVIDER: S-EPMC11600749 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature