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Sensors Technology and Sensing for Postharvest Quality Management in Agri-Food...
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Sensors Technology and Sensing for Postharvest Quality Management in Agri-Food Chains

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (5 July 2022) | Viewed by 9027

Special Issue Editors

Prof. Dr. Tomislav Jemrić

E-Mail Website
Guest Editor
Division of Horticulture and Landscape Architecture, Department of Pomology, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000 Zagreb, Croatia
Interests: postharvest fruit treatments; physiological disorders of fruits in storage; the effect of preharvest factors on postharvest behavior of fruit; fruit quality; nondestructive methods of fruit quality determination; introduction of less known fruit species
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaoshuan Zhang

E-Mail Website
Guest Editor
Department of Mechatronics at the College of Engineering, Beijing Lab of Food Quality and Safety, China Agricultural University (East Campus), Beijing 100083, China
Interests: sensors (IoT, flexible sensors) and data processing in food supply chain/industrial engineering; live animal management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Postharvest management plays a crucial role in ensuring a safe, economically viable, and sustainable supply of agricultural products to the growing world population. Postharvest losses of fruits and vegetables are estimated to be more than 30%, even in developed countries where adequate infrastructure and technology are widely available. Therefore, it is of crucial importance to speed up the development of innovative technologies that will help to optimize the postharvest chain and decrease postharvest loss. Sensor-based digital technologies are valuable tools with huge potential to achieve this goal in a cost-effective, sustainable, and efficient way.

Therefore, this Special Issue invites the submission of both review and original research articles related to sensors technology and sensing in the post-harvest phase of agri-food chains. This issue is open to contributions covering all aspects from both theoretical and practical points of view (signal acquisition and processing, data mining and fusion from sensors, together with applications in the decision-making process and quality monitoring) for postharvest quality management of agricultural products. It is focused on horticultural products (fruits, vegetables, and ornamentals) and live animal transportation, given their high value and high possibility of postharvest loss/death. However, contributions dealing with other agricultural products are also acceptable if they fit within the primary scope of the Special Issue.

We welcome original research papers and review articles on postharvest sensor technology, their applications, and comparison between different applications and solutions for postharvest problems in the agri-food chain.

Prof. Dr. Tomislav Jemrić
Prof. Dr. Xiaoshuan Zhang
Guest Editors

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 submissions that pass pre-check are 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. Sensors 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 2600 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.

Keywords

  • New Sensor and/or system development for postharvest application
  • Postharvest applications of sensor and sensing technologies
  • Data mining and analysis of data from such postharvest sensors
  • Machine learning approaches involving data from postharvest sensors
  • Postharvest quality monitoring of horticultural products and live animals
  • Optimization of the postharvest chain using sensor technologies

Published Papers (3 papers)

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17 pages, 4740 KiB  
Article
Classification Learning of Latent Bruise Damage to Apples Using Shortwave Infrared Hyperspectral Imaging
by Jean Frederic Isingizwe Nturambirwe, Willem Jacobus Perold and Umezuruike Linus Opara
Sensors 2021, 21(15), 4990; https://0-doi-org.brum.beds.ac.uk/10.3390/s21154990 - 22 Jul 2021
Cited by 15 | Viewed by 2798
Abstract
Bruise damage is a very commonly occurring defect in apple fruit which facilitates disease occurrence and spread, leads to fruit deterioration and can greatly contribute to postharvest loss. The detection of bruises at their earliest stage of development can be advantageous for screening [...] Read more.
Bruise damage is a very commonly occurring defect in apple fruit which facilitates disease occurrence and spread, leads to fruit deterioration and can greatly contribute to postharvest loss. The detection of bruises at their earliest stage of development can be advantageous for screening purposes. An experiment to induce soft bruises in Golden Delicious apples was conducted by applying impact energy at different levels, which allowed to investigate the detectability of bruises at their latent stage. The existence of bruises that were rather invisible to the naked eye and to a digital camera was proven by reconstruction of hyperspectral images of bruised apples, based on effective wavelengths and data dimensionality reduced hyperspectrograms. Machine learning classifiers, namely ensemble subspace discriminant (ESD), k-nearest neighbors (KNN), support vector machine (SVM) and linear discriminant analysis (LDA) were used to build models for detecting bruises at their latent stage, to study the influence of time after bruise occurrence on detection performance and to model quantitative aspects of bruises (severity), spanning from latent to visible bruises. Over all classifiers, detection models had a higher performance than quantitative ones. Given its highest speed in prediction and high classification performance, SVM was rated most recommendable for detection tasks. However, ESD models had the highest classification accuracy in quantitative (>85%) models and were found to be relatively better suited for such a multiple category classification problem than the rest. Full article
(This article belongs to the Special Issue Sensors Technology and Sensing for Postharvest Quality Management in Agri-Food Chains)
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17 pages, 757 KiB  
Article
Prediction of Fruity Aroma Intensity and Defect Presence in Virgin Olive Oil Using an Electronic Nose
by Pablo Cano Marchal, Chiara Sanmartin, Silvia Satorres Martínez, Juan Gómez Ortega, Fabio Mencarelli and Javier Gámez García
Sensors 2021, 21(7), 2298; https://0-doi-org.brum.beds.ac.uk/10.3390/s21072298 - 25 Mar 2021
Cited by 22 | Viewed by 2991
Abstract
The organoleptic profile of a Virgin Olive Oil is a key quality parameter that is currently obtained by human sensory panels. The development of an instrumental technique capable of providing information about this profile quickly and online is of great interest. This work [...] Read more.
The organoleptic profile of a Virgin Olive Oil is a key quality parameter that is currently obtained by human sensory panels. The development of an instrumental technique capable of providing information about this profile quickly and online is of great interest. This work employed a general purpose e-nose, in lab conditions, to predict the level of fruity aroma and the presence of defects in Virgin Olive Oils. The raw data provided by the e-nose were used to extract a set of features that fed a regressor to predict the level of fruity aroma and a classifier to detect the presence of defects. The results obtained were a mean validation error of 0.5 units for the prediction of fruity aroma using lasso regression; and 88% accuracy for the defect detection using logistic regression. Finally, the identification of two out of ten specific sensors of the e-nose that can provide successful results paves the way to the design of low-cost specific electronic noses for this application. Full article
(This article belongs to the Special Issue Sensors Technology and Sensing for Postharvest Quality Management in Agri-Food Chains)
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19 pages, 1489 KiB  
Article
Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data
by Dejan Ljubobratović, Marko Vuković, Marija Brkić Bakarić, Tomislav Jemrić and Maja Matetić
Sensors 2022, 22(15), 5791; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155791 - 03 Aug 2022
Cited by 5 | Viewed by 1816
Abstract
To date, many machine learning models have been used for peach maturity prediction using non-destructive data, but no performance comparison of the models on these datasets has been conducted. In this study, eight machine learning models were trained on a dataset containing data [...] Read more.
To date, many machine learning models have been used for peach maturity prediction using non-destructive data, but no performance comparison of the models on these datasets has been conducted. In this study, eight machine learning models were trained on a dataset containing data from 180 ‘Suncrest’ peaches. Before the models were trained, the dataset was subjected to dimensionality reduction using the least absolute shrinkage and selection operator (LASSO) regularization, and 8 input variables (out of 29) were chosen. At the same time, a subgroup consisting of the peach ground color measurements was singled out by dividing the set of variables into three subgroups and by using group LASSO regularization. This type of variable subgroup selection provided valuable information on the contribution of specific groups of peach traits to the maturity prediction. The area under the receiver operating characteristic curve (AUC) values of the selected models were compared, and the artificial neural network (ANN) model achieved the best performance, with an average AUC of 0.782. The second-best machine learning model was linear discriminant analysis with an AUC of 0.766, followed by logistic regression, gradient boosting machine, random forest, support vector machines, a classification and regression trees model, and k-nearest neighbors. Although the primary parameter used to determine the performance of the model was AUC, accuracy, F1 score, and kappa served as control parameters and ultimately confirmed the obtained results. By outperforming other models, ANN proved to be the most accurate model for peach maturity prediction on the given dataset. Full article
(This article belongs to the Special Issue Sensors Technology and Sensing for Postharvest Quality Management in Agri-Food Chains)
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