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Gorji, R., Skvaril, J. & Odlare, M. (2024). Applications of optical sensing and imaging spectroscopy in indoor farming: A systematic review. Spectrochimica Acta Part A - Molecular and Biomolecular Spectroscopy, 322, Article ID 124820.
Open this publication in new window or tab >>Applications of optical sensing and imaging spectroscopy in indoor farming: A systematic review
2024 (English)In: Spectrochimica Acta Part A - Molecular and Biomolecular Spectroscopy, ISSN 1386-1425, E-ISSN 1873-3557, Vol. 322, article id 124820Article in journal (Refereed) Published
Abstract [en]

As demand for food continues to rise, innovative methods are needed to sustainably and efficiently meet thegrowing pressure on agriculture. Indoor farming and controlled environment agriculture have emerged aspromising approaches to address this challenge. However, optimizing fertilizer usage, ensuring homogeneousproduction, and reducing agro-waste remain substantial challenges in these production systems. One potentialsolution is the use of optical sensing technology, which can provide real-time data to help growers makeinformed decisions and enhance their operations. optical sensing can be used to analyze plant tissues, evaluatecrop quality and yield, measure nutrients, and assess plant responses to stress. This paper presents a systematicliterature review of the current state of using spectral-optical sensors and hyperspectral imaging for indoorfarming, following the PRISMA 2020 guidelines. The study surveyed existing studies from 2017 to 2023 toidentify gaps in knowledge, provide researchers and farmers with current trends, and offer recommendations andinspirations for possible new research directions. The results of this review will contribute to the development ofsustainable and efficient methods of food production.

Place, publisher, year, edition, pages
Pergamon-Elsevier Science LTD, 2024
Keywords
Indoor farming, Controlled environment agriculture, Optical sensing, Spectral-optical sensors, Hyperspectral imaging
National Category
Physical Sciences
Identifiers
urn:nbn:se:mdh:diva-68096 (URN)10.1016/j.saa.2024.124820 (DOI)001275662700001 ()39032229 (PubMedID)2-s2.0-85198994797 (Scopus ID)
Funder
Vinnova
Available from: 2024-07-22 Created: 2024-07-22 Last updated: 2024-08-07Bibliographically approved
Gorji, R., Skvaril, J. & Odlare, M. (2024). Determining Moisture Content of Basil Using Handheld Near-Infrared Spectroscopy. Horticulturae, 10(4), 336-336
Open this publication in new window or tab >>Determining Moisture Content of Basil Using Handheld Near-Infrared Spectroscopy
2024 (English)In: Horticulturae, E-ISSN 2311-7524, Vol. 10, no 4, p. 336-336Article in journal (Refereed) Published
Abstract [en]

Accurate and rapid determination of moisture content is essential in crop production and decision-making for irrigation. Near-infrared (NIR) spectroscopy has been shown to be a promising method for determining moisture content in various agricultural products, including herbs and vegetables. This study tested the hypothesis that NIR spectroscopy is effective in accurately measuring the moisture content of Genovese basil (Ocimum basilicum L.), with the objective of developing a respective calibration model. Spectral data were obtained from a total of 120 basil leaf samples over a period of six days. These included freshly harvested and detached leaves, as well as those left in ambient air for 1–6 days. Five spectra were taken from each leaf using a handheld NIR spectrophotometer, which covers the first and second overtones of the NIR spectral region: 950–1650 nm. After the spectral acquisition, the leaves were weighed for fresh mass and then put in an oven for 72 h at 80 °C to determine the dry weight and calculate the reference moisture content. The calibration model was developed using multivariate analysis in MATLAB, including preprocessing and regression modeling. The data obtained from 75% of the samples were used for model training and 25% for validation. The final model demonstrates strong performance metrics. The root mean square error of calibration (RMSEC) is 2.9908, the root mean square error of cross-validation (RMSECV) is 3.2368, and the root mean square error of prediction (RMSEP) reaches 2.4675. The coefficients of determination for calibration (R2C) and cross-validation (R2CV) are consistent, with values of 0.829 and 0.80, respectively. The model’s predictive ability is indicated by a coefficient of determination for prediction (R2P) of 0.86. The range error ratio (RER) stands at 11.045—highlighting its predictive performance. Our investigation, using handheld NIR spectrophotometry, confirms NIR’s usefulness in basil moisture determination. The rapid determination offers valuable insights for irrigation and crop management.

National Category
Agricultural Science
Identifiers
urn:nbn:se:mdh:diva-66364 (URN)10.3390/horticulturae10040336 (DOI)001210006600001 ()2-s2.0-85191572101 (Scopus ID)
Available from: 2024-04-04 Created: 2024-04-04 Last updated: 2024-05-08Bibliographically approved
Organisations
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ORCID iD: ORCID iD iconorcid.org/0000-0002-4528-4989

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