Practical Applications of Spectral Sensing in Food and Agriculture

A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Applied Chemical Sensors".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 28842

Special Issue Editor

1. Agricultural Engineering Department, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt
2. Institute of Agriculture and Food Research and Technology (IRTA), Finca Camps I Armet S/n, 17121 Monells, Spain
Interests: hyperspectral imaging; imaging spectroscopy; optical sensors; UV-VIS, NIR spectroscopy; image analysis; fluorescence spectroscopy; fluorescence imaging; chemometrics; machine learning

Special Issue Information

Dear Colleagues,

Recent advances in optical and chemical sensors have opened up new opportunities in using spectral sensing and imaging techniques for various applications in food and agriculture sectors. In addition to being very effective in sensing different quality traits of a wide range of different biomaterials, such techniques are usually accompanied by various state-of-the-art machine learning and data mining approaches for classification, detection, and accurate estimation of essential physicochemical properties in nondestructive manners.

This Special Issue of Chemosensors entitled “Practical Applications of Spectral Sensing in Food and Agriculture” will focus on all aspects, development, and practical applications of spectral and optical sensing techniques such as imaging, hyperspectral imaging, multispectral imaging, near-infrared (NIR) spectroscopy, fluorescence spectroscopy, fluorescence imaging, and thermal imaging for sensing quality attributes of different food and agricultural materials. Original research papers related to spectral sensing developments and methodology and experimental verification using these computer-integrated techniques are very welcome. We also welcome articles on the latest data analysis techniques, development algorithms, and in particular those based on machine learning and data mining. Both literature review articles as well as original research papers will be published. Literature review articles should provide an up-to-date overview of the current state-of-the-art in these fields and include main findings and results from other research groups. We look forward to and welcome your participation in this Special Issue.

Prof. Dr. Gamal ElMasry
Guest Editor

Manuscript Submission Information

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Keywords

  • Imaging analysis
  • Hyperspectral imaging
  • Multispectral imaging
  • Near-infrared (NIR) spectroscopy
  • Fluorescence spectroscopy
  • Fluorescence imaging
  • Thermal imaging
  • Raman imaging
  • Chemical sensors
  • Chemical imaging
  • Plant phenotyping
  • Machine learning
  • Data mining
  • Food and agricultural materials (seeds, plants, fruits, vegetables, field crops, meat, poultry, fish, etc.)

Published Papers (4 papers)

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Research

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23 pages, 6259 KiB  
Article
Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics
by Mohamed Farag Taha, Alwaseela Abdalla, Gamal ElMasry, Mostafa Gouda, Lei Zhou, Nan Zhao, Ning Liang, Ziang Niu, Amro Hassanein, Salim Al-Rejaie, Yong He and Zhengjun Qiu
Chemosensors 2022, 10(2), 45; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors10020045 - 25 Jan 2022
Cited by 27 | Viewed by 6794
Abstract
In the aquaponic system, plant nutrients bioavailable from fish excreta are not sufficient for optimal plant growth. Accurate and timely monitoring of the plant’s nutrient status grown in aquaponics is a challenge in order to maintain the balance and sustainability of the system. [...] Read more.
In the aquaponic system, plant nutrients bioavailable from fish excreta are not sufficient for optimal plant growth. Accurate and timely monitoring of the plant’s nutrient status grown in aquaponics is a challenge in order to maintain the balance and sustainability of the system. This study aimed to integrate color imaging and deep convolutional neural networks (DCNNs) to diagnose the nutrient status of lettuce grown in aquaponics. Our approach consists of multi-stage procedures, including plant object detection and classification of nutrient deficiency. The robustness and diagnostic capability of proposed approaches were evaluated using a total number of 3000 lettuce images that were classified into four nutritional classes—namely, full nutrition (FN), nitrogen deficiency (N), phosphorous deficiency (P), and potassium deficiency (K). The performance of the DCNNs was compared with traditional machine learning (ML) algorithms (i.e., Simple thresholding, K-means, support vector machine; SVM, k-nearest neighbor; KNN, and decision Tree; DT). The results demonstrated that the deep proposed segmentation model obtained an accuracy of 99.1%. Also, the deep proposed classification model achieved the highest accuracy of 96.5%. These results indicate that deep learning models, combined with color imaging, provide a promising approach to timely monitor nutrient status of the plants grown in aquaponics, which allows for taking preventive measures and mitigating economic and production losses. These approaches can be integrated into embedded devices to control nutrient cycles in aquaponics. Full article
(This article belongs to the Special Issue Practical Applications of Spectral Sensing in Food and Agriculture)
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15 pages, 2363 KiB  
Article
Prediction of Trained Panel Sensory Scores for Beef with Non-Invasive Raman Spectroscopy
by Jamie Cafferky, Raquel Cama-Moncunill, Torres Sweeney, Paul Allen, Andrew Cromie and Ruth M. Hamill
Chemosensors 2022, 10(1), 6; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors10010006 - 25 Dec 2021
Cited by 2 | Viewed by 2075
Abstract
The objective of this study was to investigate Raman spectroscopy as a tool for the prediction of sensory quality in beef. Raman spectra were collected from M. longissimus thoracis et lumborum (LTL) muscle on a thawed steak frozen 48 h post-mortem. [...] Read more.
The objective of this study was to investigate Raman spectroscopy as a tool for the prediction of sensory quality in beef. Raman spectra were collected from M. longissimus thoracis et lumborum (LTL) muscle on a thawed steak frozen 48 h post-mortem. Another steak was removed from the muscle and aged for 14 days before being assessed for 12 sensory traits by a trained panel. The most accurate coefficients of determination of cross validation (R2CV) calibrated within the current study were for the trained sensory panel textural scores; particularly tenderness (0.46), chewiness (0.43), stringiness (0.35) and difficulty to swallow (0.33), with practical predictions also achieved for metallic flavour (0.52), fatty after-effect (0.44) and juiciness (0.36). In general, the application of mathematical spectral pre-treatments to Raman spectra improved the predictive accuracy of chemometric models developed. This study provides calibrations for valuable quality traits derived from a trained sensory panel in a non-destructive manner, using Raman spectra collected at a time-point compatible with meat management systems. Full article
(This article belongs to the Special Issue Practical Applications of Spectral Sensing in Food and Agriculture)
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25 pages, 2707 KiB  
Article
Integration of Spectral Reflectance Indices and Adaptive Neuro-Fuzzy Inference System for Assessing the Growth Performance and Yield of Potato under Different Drip Irrigation Regimes
by Salah Elsayed, Salah El-Hendawy, Mosaad Khadr, Osama Elsherbiny, Nasser Al-Suhaibani, Yaser Hassan Dewir, Muhammad Usman Tahir, Muhammad Mubushar and Waleed Darwish
Chemosensors 2021, 9(3), 55; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors9030055 - 12 Mar 2021
Cited by 11 | Viewed by 2303
Abstract
Simultaneous and timely assessment of growth and water status-related plant traits is critical for precision irrigation management in arid regions. Here, we used proximal hyperspectral sensing tools to estimate biomass fresh weight (BFW), biomass dry weight (BDW), canopy water content (CWC), and total [...] Read more.
Simultaneous and timely assessment of growth and water status-related plant traits is critical for precision irrigation management in arid regions. Here, we used proximal hyperspectral sensing tools to estimate biomass fresh weight (BFW), biomass dry weight (BDW), canopy water content (CWC), and total tuber yield (TTY) of two potato varieties irrigated with 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Plant traits were assessed remotely using published and newly constructed vegetation and water spectral reflectance indices (SRIs). We integrated genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS) models to predict the measured traits based on all SRIs. The different plant traits and SRIs varied significantly (p < 0.05) between the three irrigation regimes for the two varieties. The values of plant traits and majority SRIs showed a continuous decrease from the 100% ETc to the 50% ETc. Water-SRIs performed better than vegetation-SRIs for estimating the four plant traits. Almost all indices of the two SRI types had a weak relationship with the four plant traits (R2 = 0.00–0.37) under each irrigation regime. However, the majority of vegetation-SRIs and all water-SRIs showed strong relationships with BFW, CWC, and TTY (R2 ≥ 0.65) and moderate relationships with BDW (R2 ≥ 0.40) when the data of all irrigation regimes and varieties were analyzed together for each growing season or the data of all irrigation regimes, varieties, and seasons were combined together. The ANFIS-GA model predicted plant traits with satisfactory accuracy in both calibration (R2 = 1.0) and testing (R2 = 0.72–0.97) modes. The results indicate that SRI-based ANFIS models can improve plant trait estimation. This analysis also confirmed the benefits of applying GA to ANFIS to estimate plant responses to different growth conditions. Full article
(This article belongs to the Special Issue Practical Applications of Spectral Sensing in Food and Agriculture)
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Review

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26 pages, 5789 KiB  
Review
Recent Advances of Smart Systems and Internet of Things (IoT) for Aquaponics Automation: A Comprehensive Overview
by Mohamed Farag Taha, Gamal ElMasry, Mostafa Gouda, Lei Zhou, Ning Liang, Alwaseela Abdalla, David Rousseau and Zhengjun Qiu
Chemosensors 2022, 10(8), 303; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors10080303 - 01 Aug 2022
Cited by 17 | Viewed by 16417
Abstract
Aquaponics is an innovative, smart, and sustainable agricultural technology that integrates aquaculture (farming of fish) with hydroponics in growing vegetable crops symbiotically. The correct implementation of aquaponics helps in providing healthy organic foods with low consumption of water and chemical fertilizers. Numerous research [...] Read more.
Aquaponics is an innovative, smart, and sustainable agricultural technology that integrates aquaculture (farming of fish) with hydroponics in growing vegetable crops symbiotically. The correct implementation of aquaponics helps in providing healthy organic foods with low consumption of water and chemical fertilizers. Numerous research attempts have been directed toward real implementations of this technology feasibly and reliably at large commercial scales and adopting it as a new precision technology. For better management of such technology, there is an urgent need to use the Internet of things (IoT) and smart sensing systems for monitoring and controlling all operations involved in the aquaponic systems. Thence, the objective of this article is to comprehensively highlight research endeavors devoted to the utilization of automated, fully operated aquaponic systems, by discussing all related aquaponic parameters aligned with smart automation scenarios and IoT supported by some examples and research results. Furthermore, an attempt to find potential gaps in the literature and future contributions related to automated aquaponics was highlighted. In the scope of the reviewed research works in this article, it is expected that the aquaponics system supported with smart control units will become more profitable, intelligent, accurate, and effective. Full article
(This article belongs to the Special Issue Practical Applications of Spectral Sensing in Food and Agriculture)
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