Algorithms to predict moisture content of grain using relative humidity time-series
Autor: | Simon Ghionea, Wenbo Wang, Courosh Mehanian, Charles B. Delahunt, Andrew Miller, Michael Friend, Austin Chan, Anjali Sehrawat |
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Rok vydání: | 2020 |
Předmět: |
Moisture
Thermodynamic equilibrium Humidity 04 agricultural and veterinary sciences medicine.disease_cause 040401 food science 0404 agricultural biotechnology Mold 040103 agronomy & agriculture medicine Range (statistics) 0401 agriculture forestry and fisheries Leverage (statistics) Relative humidity Water content Algorithm |
Zdroj: | GHTC |
Popis: | Post-harvest losses to grain crops are conservatively estimated at 10-20% (ranging up to 40%) in many countries. In particular, grains must be properly dried to avoid spoilage, harmful mycotoxins from mold, and financial loss. Smallholder farmers can thus greatly benefit from a means to assess Moisture Content (MC) in their grain. We describe a two-step algorithm, with very low computational cost, that calculates MC with high accuracy, using Relative Humidity (RH) and Temperature (T) time-series. The time-series do not need to reach equilibrium state, enabling fast (12-minute) time-to-result. The algorithm first curve-fits the RH time-series to estimate asymptotic RH, in order to leverage the physics of the RH-T-MC equilibrium relationship. It then uses regression to estimate MC to within ±1% on ≥95% of samples over a wide range of ambient RH-T conditions, on both Lab and Field samples of 10 different grains. |
Databáze: | OpenAIRE |
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