Work

Physics-Informed Neural Network for prediction of post-harvest firmness of avocados

Year: 2025

Type: article

Abstract: This study employs physics-informed neural networks (PINNs) to model avocado firmness during ripening, based on a physiological framework where firmness loss is governed by enzymatic cell wall degradation, with kinetics modulated by the metabolic rate. PINNs are trained on literature datasets from diverse cultivars and countries under constant storage conditions, revealing comparable climacteric ripening kinetics. The approach is extended to dynamic storage scenarios using a time/temperature/gas-conditions superposition principle. Variations due to orchard origin, climate, and harvest time are captured through differences in the initial pre-climacteric state, which cannot be described by a single state variable.

Source: Postharvest Biology and Technology

DOI: 10.1016/j.postharvbio.2025.113898

Authors: R.G.M. van der Sman, X. Guo, R. Pedreschi

Institutions: Wageningen University & Research, Pontificia Universidad Católica de Valparaíso, Millenium Institute Center for Genome Regulation

Language: English

Topic: Postharvest Quality and Shelf Life Management

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: hybrid

Estimated APC paid: $4,280