Special Issue "Digital Farming, Food, and Environment—A Response to Farm Biosecurity and Food Safety Concerns"
Deadline for manuscript submissions: 10 January 2022.
Interests: hyperspectral imaging; computer vision; post-harvest quality control; remote sensing of environment; automated systems for monitoring animal farms; hyperspectral imaging applications in food systems throughout the food value chains; aquaculture; agriculture; enviroment; machine learning; chemometric; hyperspectral imaging for soil and plant quality measurments; image classification; non-destructive analysis; microscopic hyperspectral analysis
Interests: photogrammetry; 3D computer vision; remote sensing; machine learning; deep learning; automated interpretation of imagery and point clouds
Special Issues, Collections and Topics in MDPI journals
Special Issue in ISPRS International Journal of Geo-Information: 3D Indoor Modelling and Navigation
Special Issue in Infrastructures: Spatial Data Infrastructures
Topical Collection in Sensors: Positioning and Navigation (Closed)
Special Issue in Remote Sensing: Laser Scanning and Point Cloud Processing
Special Issue in Remote Sensing: Laser Scanning and Point Cloud Processing in Urban Environments
Special Issue in Remote Sensing: Point Cloud Processing in Remote Sensing Technology
Interests: soil-plant interactions; hyperspectral imaging; Biochar; research for development; sustainable food production systems
The growing world population and increased demand for food have raised concerns about farm biosecurity and food safety. The need for digital monitoring and management systems that collect and analyse information from environmental sites, farms, animal sheds, food storage areas, and food processing lines in real-time has never been so evident. Digital quality control systems (DQCS) integrate remote and proximal sensor data with the application of machine learning and artificial intelligence algorithms in order to generate real-time information for a specific purpose. DQCS can be used to address biosecurity and safety concerns by reducing human–animal contact, identifying pathogens, detecting environmental pollutants, detecting food impurities and toxins, and differentiating between the preferred plant/animal species and undesirable or potentially dangerous species. This Special Issue aims to gather relevant research that uses digital systems to predict potential risks, help decision-makers to apply site-specific management practices, and decrease the human footprint in the environment.
Dr. Iman Tahmasbian
Dr. Kourosh Khoshelham
Dr. Shahla Hosseini Bai
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Animal health
- Food safety
- Digital agriculture and aquaculture
- Digital quality monitoring systems
- Artificial intelligence
- Hyperspectral imaging
- Machine vision
- Machine learning
- Image classification
- Image processing
- Hyperspectral sensors
- Multispectral sensors
- Environmental pollution
- Near-infrared (NIR) spectroscopy
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: A performance evaluation of Vis/NIR hyperspectral imaging to predict curcumin concentration in fresh turmeric rhizomes
Authors: Michael B. Farrar; Helen M. Wallace; Peter Brooks; Catherine M. Yule; Iman Tahmasbian; Peter K. Dunn; Shahla Hosseini Bai
Affiliation: The problem is that we use lab based hyperspectral camera and they are not actual remote sensing and some refrees may think the paper may not fit journal scopes, Bbut in reality, the use of lab based HSI system falls within the scopes of this Special Issue
Abstract: Hyperspectral image (HSI) analysis has the potential to estimate organic compounds in plants and foods. Curcumin is an important compound used to treat a range of medical conditions. Therefore, a method to rapidly determine rhizomes with high curcumin content on-farm would be of significant advantage for farmers. Curcumin content of rhizomes varies within, and between varieties but current chemical analysis methods are expensive and time consuming. This study compares curcumin in three turmeric (Curcuma longa) varieties and examines the potential for laboratory-based HSI to rapidly predict curcumin using the visible-near infrared (400 – 1000 nm) spectrum. Hyperspectral images (n=152) were captured of the fresh rhizome outer-skin and cut-flesh, using three local varieties (yellow, orange and red). Distribution of curcuminoids and total curcumin was analysed. Partial least squares regression (PLSR) models were developed to predict total curcumin concentrations. Total curcumin and the proportion of three curcuminoids differed significantly among all varieties. Red turmeric had the highest total curcumin concentration (0.83 ± 0.21%) compared with orange (0.37 ± 0.12%) and yellow (0.02 ± 0.02%). PLSR models predicted curcumin using raw spectra of rhizome cut-flesh and pooled data for all three varieties (R2c = 0.83, R2p = 0.55, ratio of prediction to deviation (RPD) = 1.51) and was slightly improved by using images of a single variety (orange) only (R2c = 0.85, R2p = 0.62, RPD = 1.65). However, prediction of curcumin using outer-skin of rhizomes was poor(R2c = 0.64, R2p = 0.37, RPD = 1.28). These models can discriminate between ‘low’ and ‘high’ values and so may be adapted into a two-level grading system. HSI has the potential to help identify turmeric rhizomes with high curcumin concentrations and allow for more efficient refinement into curcumin for medicinal purposes.