Hyperspectral imaging, a non-destructive technique in medicinal and aromatic plant products industry: Current status and potential future applications

Kiani, Sajad; Ruth, Saskia M. van; Minaei, Saeid; Ghasemi‐Varnamkhasti, Mahdi

doi:10.1016/j.compag.2018.06.025

Cited by 33 publications

(11 citation statements)

References 126 publications

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“…The MLP method has been widely used for classification in agricultural research ( Golhani et al, 2018 ; Kiani et al, 2018 ). In our study, four important spectral regions (550–750, 995–1,005, 1,110–1,220, and 1,380–1,470 nm) have been identified by Sp_MLP model as “the best model” with high weed identification performance.…”

Section: Discussionmentioning

confidence: 99%

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

Al-Sarayreh

Irie³

et al. 2021

Front. Plant Sci.

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Weeds can be major environmental and economic burdens in New Zealand. Traditional methods of weed control including manual and chemical approaches can be time consuming and costly. Some chemical herbicides may have negative environmental and human health impacts. One of the proposed important steps for providing alternatives to these traditional approaches is the automated identification and mapping of weeds. We used hyperspectral imaging data and machine learning to explore the possibility of fast, accurate and automated discrimination of weeds in pastures where ryegrass and clovers are the sown species. Hyperspectral images from two grasses (Setaria pumila [yellow bristle grass] and Stipa arundinacea [wind grass]) and two broad leaf weed species (Ranunculus acris [giant buttercup] and Cirsium arvense [Californian thistle]) were acquired and pre-processed using the standard normal variate method. We trained three classification models, namely partial least squares-discriminant analysis, support vector machine, and Multilayer Perceptron (MLP) using whole plant averaged (Av) spectra and superpixels (Sp) averaged spectra from each weed sample. All three classification models showed repeatable identification of four weeds using both Av and Sp spectra with a range of overall accuracy of 70–100%. However, MLP based on the Sp method produced the most reliable and robust prediction result (89.1% accuracy). Four significant spectral regions were found as highly informative for characterizing the four weed species and could form the basis for a rapid and efficient methodology for identifying weeds in ryegrass/clover pastures.

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Section: Discussionmentioning

confidence: 99%

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

Al-Sarayreh

Irie³

et al. 2021

Front. Plant Sci.

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“…Concerning with the production technologies, technology transfer and innovation, the following topics have become increasingly interesting and attract quite considerable attention of researchers working in the MAPs industry: distillation of MAPs for extraction of essential oils (Munir et al, 2014), production of MAPs on the farm with solar distillation system (Munir and Hensel, 2010), the impact of technological processing and vaccination on the antioxidant capacity of MAPs from harvest to packaging (Giao, Pereira, Pintado & Malcata, 2013), the effect of heavy metals on secondary metabolite biosynthesis, oxidative status and phytoextraction in MAPs in polluted environment (Lajayer, Ghorbanpour and Nikabadi, 2017), Hyperspectral Imaging (HSI) as a nondestructive technique in MAPs industry (Kiani, van Ruth and Minaei, 2018), establishing the global volatile profile of commercial virgin olive oil and virgin olive oils flavoured with MAPs (garlic, rosemary, oregano and thyme), using two various analytical methods, liquid-liquid microextraction (LLME) and headspace solid-phase microextraction (HS-SPME) mixed with gas chromatography-quadrupole mass spectrometry (GC-qMS) (Perestrelo, Silva, Silva & Câmara, 2017), the effect of intercropping of MAPs with organic approach on resource use efficiency in arecanut based intercropping system (Sujatha, Bhat, Kannan & Balasimha, 2011), epigenetic and epigenomic regulations in MAPs (Hao and Xiao, 2018), the growth of cultivated crops, irrigation, fertilization and plant protection; washing, cooling, cutting, distillation, steam, irradiation, drying, fumigation, sorting, packaging and storage after harvesting (Maikhuri et al, 2017;Novak, 2014;Sharma and Kala, 2018), volatile organic compounds (VOCs), gas chromatography and mass spectroscopy, signal processing with sensors and advanced data processing algorithms (Kiani, Minaei & Ghasemi-Varnamkhasti, 2016), use of synthetic insecticides such as organophosphates, carbamates, pyrethroids and neonicotinoids in the protection of MAPs (Gahukar, 2018), the seasonal effect of MAPs on the polyphenolic composite of antioxidant and anti-inflammatory properties (Piccolella, Crescente, Pacifico & Pacifico, 2018) (Overwalle, 2006). Therefore, some arrangements have been made in this framework in the development of commercial relations in MAPs industry (European Commission and TRAFFIC, 2013).…”

Section: Resultsmentioning

confidence: 99%

The Driving Force Effect of Standardisation and Accreditation in Medicinal and Aromatic Plants Industry

Aliu¹,

Çilginoğlu²,

Aliu³

2019

Kastamonu Üniversitesi Orman Fakültesi Dergisi

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Aim of study: The purpose of this study is to examine the standardisation and accreditation processes of the corporations and suppliers operating in the Medicinal and Aromatic Plants (MAPs) industry. Material and methods: Case study research, market research, sectoral observations and document analyses were conducted within the scope of research methodology. Primarily, micro and meso-level comparative analyses were performed and then macro-level comparative analyses were made by taking into consideration the arguments in MAPs industry. Dominant design approach was applied to the investigation in a systematic manner. Main results: One of the main objectives of this investigation is to take a look at the transformation that occurred in the MAPs industry which has a significant share in the global markets by taking into consideration the advanced technology and innovation of the "Historical Spice Road." Thus, the route that is extending from India to the Balkans was taken into account. The strategic importance of Central and Eastern European Countries and SouthEastern European Countries in this route was emphasised in a systematic manner. Technology transfer and trade volumes amongst corporations operating in the "Modern Spice Route" were included in the study. Research highlights: The study highlights the effects of standardisation and accreditation processes on intellectual and industrial property rights while technology transfer is being carried out. Many applications in various countries were compared with operations and actions in MAPs industry in the Balkans.

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“…Since a human nose can discriminate among species, sensor arrays (also referred to as electronic noses) might offer a cost-efficient, convenient, and fast alternative [17,18]. For several decades, e-Noses with different sensing materials have been successfully used: peptides as biosensors [19], molecularly imprinted polystyrene (MIP) membranes for bio-mimicry of terpenes [20], electrochemical sensor array for food quality assessment [21], and metal oxide semiconductor (MOS) sensors to discriminate among medicinal plants based on emissions of their VOCs [22][23][24][25][26][27]. Quartz crystal microbalance (QCM)-based sensor arrays have also been used to differentiate among plants from the Lamiaceae family [20,28].…”

Section: Introductionmentioning

confidence: 99%

Sniff Species: SURMOF-Based Sensor Array Discriminates Aromatic Plants beyond the Genus Level

Okur

Zhang

et al. 2021

Chemosensors

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Lamiaceae belong to the species-richest family of flowering plants and harbor many species that are used as herbs or in medicinal applications such as basils or mints. The evolution of this group has been driven by chemical speciation, mainly volatile organic compounds (VOCs). The commercial use of these plants is characterized by adulteration and surrogation to a large extent. Authenticating and discerning this species is thus relevant for consumer safety but usually requires cumbersome analytics, such as gas chromatography, often coupled with mass spectroscopy. Here, we demonstrate that quartz-crystal microbalance (QCM)-based electronic noses provide a very cost-efficient alternative, allowing for fast, automated discrimination of scents emitted from the leaves of different plants. To explore the range of this strategy, we used leaf material from four genera of Lamiaceae along with lemongrass, which is similarly scented but from an unrelated outgroup. To differentiate the scents from different plants unambiguously, the output of the six different SURMOF/QCM sensors was analyzed using machine learning (ML) methods together with a thorough statistical analysis. The exposure and purging of data sets (four cycles) obtained from a QCM-based, low-cost homemade portable e-Nose were analyzed using a linear discriminant analysis (LDA) classification model. Prediction accuracy with repeated test measurements reached values of up to 0%. We show that it is possible not only to discern and identify plants at the genus level but also to discriminate closely related sister clades within a genus (basil), demonstrating that an e-Nose is a powerful device that can safeguard consumer safety against dangers posed by globalized trade.

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Hyperspectral imaging, a non-destructive technique in medicinal and aromatic plant products industry: Current status and potential future applications

Cited by 33 publications

References 126 publications

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

The Driving Force Effect of Standardisation and Accreditation in Medicinal and Aromatic Plants Industry

Sniff Species: SURMOF-Based Sensor Array Discriminates Aromatic Plants beyond the Genus Level

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