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Multi-Sensor Systems for Food and Agricultural Applications

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

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 17864

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Special Issue Editors

Dr. Cecilia Jimenez

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Instituto de Microelectronica de Barcelona (IMB-CNM) CSIC, Campus UAB, 08193 Cerdanyola del Valles, Spain
Interests: electrochemical sensors; sensors based on silicon and derived materials; microanalytical systems; sensor’s applications in food, environment and clinical analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Stella Vallejos Vargas

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Guest Editor

1. IMB-CNM (CSIC), Instituto de Microelectrónica de Barcelona, Barcelona, Spain
2. CEITEC, Brno University of Technology, Brno, Czech Republic
Interests: sensors; micro/nanofabrication; functional nanomaterials; chemical vapor deposition
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Maria Luz Rodriguez-Mendez

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Guest Editor

Department of Química Física y Química Inorgánica, Escuela de Ingenierías Industriales, University of Valladolid, Paseo del Cauce, 59, 47011 Valladolid, Spain
Interests: electrochemical sensors; chemically modified with electrocatalytic materials and nanomaterials; biomimetic biosensors dedicated to the detection of components of foods; antioxidants; organic acids; fatty acids, etc; electronic tongues based on nanostructured biosensors for the assessment of the organoleptic characteristics of wines and milks

Special Issue Information

Dear Colleagues,

The growing interest in high-quality and sustainable production in agriculture and the food industry has promoted the development of more automated and accurate analytical systems. Additionally, optimized process control is essential to address safety rules and maintain the commercial viability of an end product. This involves, among other things, a rapid assessment of the (bio) chemical and physical properties of raw materials, process flows and end products.

In this context, the use of multi-sensor analysis systems combined with new chemometric tools or machine learning techniques can provide great advantages. These systems can offer global information on the system, recognizing quantitative and qualitative composition, and provide a warning in the case of deviations or events, for example, in line production systems.

This Special Issue will provide a unique opportunity for researchers to publish their original achievements related to the design, characterization and validation of multi-sensor systems for food and agricultural applications.

This Special Issue welcomes both original research and review articles.

Dr. Cecilia Jimenez
Dr. Stella Vallejos Vargas
Prof. Dr. Maria Luz Rodriguez-Mendez
Guest Editors

If you want to learn more information or need any advice, you can contact the Special Issue Editor Bell Ding via <Bell.ding@mdpi.com>

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

(bio) sensors
multi-sensor systems
chemometrics
machine learning tools
neural networks
food analysis and control
agricultural control

Published Papers (7 papers)

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Research

16 pages, 1652 KiB

Open AccessArticle

Preliminary Studies on Detection of Fusarium Basal Rot Infection in Onions and Shallots Using Electronic Nose

by Malgorzata Labanska, Sarah van Amsterdam, Sascha Jenkins, John P. Clarkson and James A. Covington

Sensors 2022, 22(14), 5453; https://doi.org/10.3390/s22145453 - 21 Jul 2022

Cited by 14 | Viewed by 2416

Abstract

The evaluation of crop health status and early disease detection are critical for implementing a fast response to a pathogen attack, managing crop infection, and minimizing the risk of disease spreading. Fusarium oxysporum f. sp. cepae, which causes fusarium basal rot disease, is considered one of the most harmful pathogens of onion and accounts for considerable crop losses annually. In this work, the capability of the PEN 3 electronic nose system to detect onion and shallot bulbs infected with F. oxysporum f. sp. cepae, to track the progression of fungal infection, and to discriminate between the varying proportions of infected onion bulbs was evaluated. To the best of our knowledge, this is a first report on successful application of an electronic nose to detect fungal infections in post-harvest onion and shallot bulbs. Sensor array responses combined with PCA provided a clear discrimination between non-infected and infected onion and shallot bulbs as well as differentiation between samples with varying proportions of infected bulbs. Classification models based on LDA, SVM, and k-NN algorithms successfully differentiate among various rates of infected bulbs in the samples with accuracy up to 96.9%. Therefore, the electronic nose was proved to be a potentially useful tool for rapid, non-destructive monitoring of the post-harvest crops. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Food and Agricultural Applications)

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10 pages, 2305 KiB

Open AccessArticle

Rapid and Non-Destructive Analysis of Corky Off-Flavors in Natural Cork Stoppers by a Wireless and Portable Electronic Nose

by José Pedro Santos, Isabel Sayago, José Luis Sanjurjo, María Soledad Perez-Coello and María Consuelo Díaz-Maroto

Sensors 2022, 22(13), 4687; https://doi.org/10.3390/s22134687 - 21 Jun 2022

Cited by 2 | Viewed by 1320

Abstract

This article discusses the use of a handheld electronic nose to obtain information on the presence of some aromatic defects in natural cork stoppers, such as haloanisoles, alkylmethoxypyrazines, and ketones. Typical concentrations of these compounds (from 5 to 120 ng in the cork samples) have been measured. Two electronic nose prototypes have been developed as an instrumentation system comprise of eight commercial gas sensors to perform two sets of experiments. In the first experiment, a quantitative approach was used whist in the second experiment a qualitative one was used. Machine learning algorithms such as k-nearest neighbors and artificial neural networks have been used in order to test the performance of the system to detect cork defects. The use of this system tries to improve the current aromatic defect detection process in the cork stopper industry, which is done by gas chromatography or human test panels. We found this electronic nose to have near 100 % accuracy in the detection of these defects. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Food and Agricultural Applications)

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14 pages, 883 KiB

Open AccessArticle

Soil Moisture a Posteriori Measurements Enhancement Using Ensemble Learning

by Bogdan Ruszczak and Dominika Boguszewska-Mańkowska

Sensors 2022, 22(12), 4591; https://doi.org/10.3390/s22124591 - 17 Jun 2022

Cited by 4 | Viewed by 1757

Abstract

This work aimed to assess the recalibration and accurate characterization of commonly used smart soil-moisture sensors using computational methods. The paper describes an ensemble learning algorithm that boosts the performance of potato root moisture estimation and increases the simple moisture sensors’ performance. It was prepared using several month-long everyday actual outdoor data and validated on the separated part of that dataset. To obtain conclusive results, two different potato varieties were grown on 24 separate plots on two distinct soil profiles and, besides natural precipitation, several different watering strategies were applied, and the experiment was monitored during the whole season. The acquisitions on every plot were performed using simple moisture sensors and were supplemented with reference manual gravimetric measurements and meteorological data. Next, a group of machine learning algorithms was tested to extract the information from this measurements dataset. The study showed the possibility of decreasing the median moisture estimation error from 2.035% for the baseline model to 0.808%, which was achieved using the Extra Trees algorithm. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Food and Agricultural Applications)

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18 pages, 7730 KiB

Open AccessArticle

NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals

by Keerthi Chadalavada, Krithika Anbazhagan, Adama Ndour, Sunita Choudhary, William Palmer, Jamie R. Flynn, Srikanth Mallayee, Sharada Pothu, Kodukula Venkata Subrahamanya Vara Prasad, Padmakumar Varijakshapanikar, Chris S. Jones and Jana Kholová

Sensors 2022, 22(10), 3710; https://doi.org/10.3390/s22103710 - 13 May 2022

Cited by 16 | Viewed by 5419

Abstract

Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R² ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R² = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Food and Agricultural Applications)

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16 pages, 1314 KiB

Open AccessArticle

Differentiation of Livestock Internal Organs Using Visible and Short-Wave Infrared Hyperspectral Imaging Sensors

by Cassius E. O. Coombs, Brendan E. Allman, Edward J. Morton, Marina Gimeno, Neil Horadagoda, Garth Tarr and Luciano A. González

Sensors 2022, 22(9), 3347; https://doi.org/10.3390/s22093347 - 27 Apr 2022

Cited by 1 | Viewed by 1973

Abstract

Automatic identification and sorting of livestock organs in the meat processing industry could reduce costs and improve efficiency. Two hyperspectral sensors encompassing the visible (400–900 nm) and short-wave infrared (900–1700 nm) spectra were used to identify the organs by type. A total of 104 parenchymatous organs of cattle and sheep (heart, kidney, liver, and lung) were scanned in a multi-sensory system that encompassed both sensors along a conveyor belt. Spectral data were obtained and averaged following manual markup of three to eight regions of interest of each organ. Two methods were evaluated to classify organs: partial least squares discriminant analysis (PLS-DA) and random forest (RF). In addition, classification models were obtained with the smoothed reflectance and absorbance and the first and second derivatives of the spectra to assess if one was superior to the rest. The in-sample accuracy for the visible, short-wave infrared, and combination of both sensors was higher for PLS-DA compared to RF. The accuracy of the classification models was not significantly different between data pre-processing methods or between visible and short-wave infrared sensors. Hyperspectral sensors, particularly those in the visible spectrum, seem promising to identify organs from slaughtered animals which could be useful for the automation of quality and process control in the food supply chain, such as in abattoirs. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Food and Agricultural Applications)

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17 pages, 6521 KiB

Open AccessArticle

In-Field Wheat Reflectance: How to Reach the Organ Scale?

by Sébastien Dandrifosse, Alexis Carlier, Benjamin Dumont and Benoît Mercatoris

Sensors 2022, 22(9), 3342; https://doi.org/10.3390/s22093342 - 27 Apr 2022

Cited by 4 | Viewed by 1587

Abstract

The reflectance of wheat crops provides information on their architecture or physiology. However, the methods currently used for close-range reflectance computation do not allow for the separation of the wheat canopy organs: the leaves and the ears. This study details a method to achieve high-throughput measurements of wheat reflectance at the organ scale. A nadir multispectral camera array and an incident light spectrometer were used to compute bi-directional reflectance factor (BRF) maps. Image thresholding and deep learning ear detection allowed for the segmentation of the ears and the leaves in the maps. The results showed that the BRF measured on reference targets was constant throughout the day but varied with the acquisition date. The wheat organ BRF was constant throughout the day in very cloudy conditions and with high sun altitudes but showed gradual variations in the morning under sunny or partially cloudy sky. As a consequence, measurements should be performed close to solar noon and the reference panel should be captured at the beginning and end of each field trip to correct the BRF. The method, with such precautions, was tested all throughout the wheat growing season on two varieties and various canopy architectures generated by a fertilization gradient. The method yielded consistent reflectance dynamics in all scenarios. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Food and Agricultural Applications)

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20 pages, 11530 KiB

Open AccessArticle

In-Field Detection of American Foulbrood (AFB) by Electric Nose Using Classical Classification Techniques and Sequential Neural Networks

by Beata Bąk, Jarosław Szkoła, Jakub Wilk, Piotr Artiemjew and Jerzy Wilde

Sensors 2022, 22(3), 1148; https://doi.org/10.3390/s22031148 - 02 Feb 2022

Cited by 3 | Viewed by 1932

Abstract

American foulbrood is a dangerous bee disease that attacks the sealed brood. It quickly leads to the death of bee colonies. Efficient diagnosis of this disease is essential. As specific odours are produced when larvae rot, it was investigated whether an electronic nose can distinguish between colonies affected by American foulbrood and healthy ones. The experiment was conducted in an apiary with 18 bee families, 9 of which showed symptoms of the disease confirmed by laboratory diagnostics. Three units of the Beesensor V.2 device based on an array of six semiconductor TGS gas sensors, manufactured by Figaro, were tested. Each copy of the device was tested in all bee colonies: sick and healthy. The measurement session per bee colony lasted 40 min and yielded results from four 10 min measurements. One 10-min measurement consisted of a 5 min regeneration phase and a 5 min object-measurement phase. For the experiments, we used both classical classification methods such as k-nearest neighbour, Naive Bayes, Support Vector Machine, discretized logistic regression, random forests, and committee of classifiers, that is, methods based on extracted representative data fragments. We also used methods based on the entire 600 s series, in this study of sequential neural networks. We considered, in this study, six options for data preparation as part of the transformation of data series into representative results. Among others, we used single stabilised sensor readings as well as average values from stable areas. For verifying the quality of the classical classifiers, we used the 25-fold train-and-test method. The effectiveness of the tested methods reached a threshold of 75 per cent, with results stable between 65 and 70 per cent. As an element to confirm the possibility of class separation using an artificial nose, we used applied visualisations of classes. It is clear from the experiments conducted that the artificial nose tested has practical potential. Our experiments show that the approach to the problem under study by sequential network learning on a sequence of data is comparable to the best classical methods based on discrete data samples. The results of the experiment showed that the Beesensor V.2 along with properly selected classification techniques can become a tool to facilitate rapid diagnosis of American foulbrood under field conditions. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Food and Agricultural Applications)

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