Optical methods can provide measurements without coming into contact with the sample. In the agrifood sector, this feature can be exploited to measure physical properties of crops. In particular, we focused our research on moisture content and density estimation. These two physical quantities of the crop are extremely important not only to determine future treatments to be performed, e.g. drying methods and processes but, also for estimating the value of the product In this article, we propose a new model for simultaneous estimation of crop moisture content and density, using Fourier transform near-infrared spectroscopy combined with partial least square multivariate methods. The model has been developed considering 140 fresh Medicago sativa samples properly harvested. Moisture content ranged from 9.4% to 83.9% whereas density from 46 kg/m3 to 236 kg/m3. Reference MC was computed according to the American Society of Agricultural and Biological Engineers standard whereas reference density was determined estimating the volume of a sample of known mass. The obtained results indicated that crop moisture content and density information can be recovered from the near-infrared absorption spectrum of the sample with coefficients of determination R2 = 0.925 and R2 = 0.681 for the moisture content and density, respectively. Mean root mean square relative errors of the estimation were 13.8% and 14.4% for the moisture content and density, respectively.

Partial Least Squares Estimation of Crop Moisture and Density by Near-Infrared Spectroscopy / Cassanelli, D.; Lenzini, N.; Ferrari, L.; Rovati, L.. - In: IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT. - ISSN 0018-9456. - 70:(2021), pp. 1-10. [10.1109/TIM.2021.3054637]

Partial Least Squares Estimation of Crop Moisture and Density by Near-Infrared Spectroscopy

Cassanelli D.;Rovati L.
2021

Abstract

Optical methods can provide measurements without coming into contact with the sample. In the agrifood sector, this feature can be exploited to measure physical properties of crops. In particular, we focused our research on moisture content and density estimation. These two physical quantities of the crop are extremely important not only to determine future treatments to be performed, e.g. drying methods and processes but, also for estimating the value of the product In this article, we propose a new model for simultaneous estimation of crop moisture content and density, using Fourier transform near-infrared spectroscopy combined with partial least square multivariate methods. The model has been developed considering 140 fresh Medicago sativa samples properly harvested. Moisture content ranged from 9.4% to 83.9% whereas density from 46 kg/m3 to 236 kg/m3. Reference MC was computed according to the American Society of Agricultural and Biological Engineers standard whereas reference density was determined estimating the volume of a sample of known mass. The obtained results indicated that crop moisture content and density information can be recovered from the near-infrared absorption spectrum of the sample with coefficients of determination R2 = 0.925 and R2 = 0.681 for the moisture content and density, respectively. Mean root mean square relative errors of the estimation were 13.8% and 14.4% for the moisture content and density, respectively.
2021
70
1
10
Partial Least Squares Estimation of Crop Moisture and Density by Near-Infrared Spectroscopy / Cassanelli, D.; Lenzini, N.; Ferrari, L.; Rovati, L.. - In: IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT. - ISSN 0018-9456. - 70:(2021), pp. 1-10. [10.1109/TIM.2021.3054637]
Cassanelli, D.; Lenzini, N.; Ferrari, L.; Rovati, L.
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1236420
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 17
  • ???jsp.display-item.citation.isi??? 14
social impact