Predictive modelling of allowable storage time for pearl millet using multilayer perception neural network

Abstract

Biotic and abiotic factors interact to damage grains in the storage ecosystem. Monitoring temperature fluctuations and moisture migration is crucial to control their impact on grain quality. Grains with high temperature and moisture content have limited time for post-harvest activities. Hence, it is important to determine the time before spoilage for different grain moisture contents and storage temperatures. The study evaluated the impact of storage variables, specifically moisture content, storage temperature, and storage period, on the parameters associated with grain quality and seed deterioration in pearl millet. A model for predicting the allowable storage time was developed using a feed-forward-back propagation multilayer perception (MLP) neural network. The effectiveness of Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG) algorithms in predicting the safe storage time was evaluated and compared. The BR neural network model showed higher predictability with an R² value greater than 0.98 and low error values. The safe storage guidelines chart and model developed for allowable storage time will be helpful for farmers and grain processing industries including storage hubs to schedule different post-harvest operations of pearl millet with minimal changes in grain quality.