Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery

Monteiro, Sildomar T.; Minekawa, Yohei; Kosugi, Yukio; Akazawa, Tsuneya; Oda, Kunio

doi:10.1016/j.isprsjprs.2006.12.002

Cited by 88 publications

(42 citation statements)

References 23 publications

(23 reference statements)

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“…Some application areas in agriculture are data prediction (Šťastný et al, 2011), classification of agricultural products (Shahin et al, 2002;Khalesi et al, 2012;Dousti et al, 2013;Reshadsedghi et al, 2014), food and crop analysis (El-Sanhoty et al, 2006;Monteiro et al, 2007), drying process (Khazaei et al, 2013), precise weed detection and weed seeds identification (Granitto…”

Section: Introductionmentioning

confidence: 99%

Prediction of Physical Parameters of Pumpkin Seeds Using Neural Network

Demır

Eski

Kuş

et al. 2017

Not Bot Horti Agrobo

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The design of the machines and equipment used in harvest and post-harvest processing should be compatible with the physical, mechanical and rheological characteristics of the fruits and vegetables. In machine design for agricultural products, several characteristics of relevant products and seeds should be known ahead. Designers can either measure all these design parameters one by one, or they may use intelligent systems to estimate such parameters. Neural networks (NNs) are new computational tools that provide a quick and accurate means of physical properties prediction of agricultural materials, and have been shown to perform well in comparison with traditional methods. In this research, some physical properties of pumpkin (Cucurbita pepo L.) seeds, including linear dimensions, volume, surface and projected area, geometric mean diameter and sphericity were calculated tridimensional in lab conditions. Then, prediction of these parameters was carried out using NNs. The research was divided into two parts; experimental investigation and simulation analysis with NNs. Back Propagation Neural Network (BPNN) and Radial Basis Neural Network (RBNN) structures were employed to estimate physical parameters of the pumpkin seeds. The Root Mean Squared Error (RMSE) was 0.6875 for BPNN and 0.0025 for RBNN structures. The RBNN structure was superior in prediction and could be used as an alternative approach to conventional methods.

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Section: Introductionmentioning

confidence: 99%

Prediction of Physical Parameters of Pumpkin Seeds Using Neural Network

Demır

Eski

Kuş

et al. 2017

Not Bot Horti Agrobo

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show abstract

“…The chemical imaging spectroscopy is applied to various fields, from astronomy to agriculture (Baranowski et al, 2008, Monteiro et al, 2007, V. Smail, 2006, from the pharmaceutical industry (Lyon et al 2002, Roggo et al, 2005 to medicine (Ferris et al 2001, Zheng et al, 2004. But in recent years, has also found use for quality control and safety in food (Gowen et al, 2007b).…”

Section: Image Analysismentioning

confidence: 99%

Chemometrics in Food Technology

Guidetti¹,

Beghi²,

Giovenzana³

2012

Chemometrics in Practical Applications

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“…Wavelength in the "staring imager" configuration is typically moderated using a tuneable filter, Acousto-optic Tuneable Filters (AOTFs) and Liquid Crystal Tuneable Filters (LCTFs) are the two most predominantly employed. AOTFs have been used in the construction of commercially available NIR-CI systems [15]. The main advantages of AOTFs are good transmission efficiency, fast scan times and large spectral range.…”

Section: The Hyperspectral Imaging (Hsi)mentioning

confidence: 99%

Hyperspectral Sensing Techniques Applied to Bio-masses Characterization: The Olive Husk Case

Bonifazi¹,

Serranti²

2011

Computer and Computing Technologies in Agriculture IV

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Abstract. Olive husk (OH) quality, in respect of constituting particles characteristics (olive stones and pulp residues as result after pressing), represents an important issue. OH particles size class distribution and composition play, in fact, an important role for OH utilization as: organic amendment, bio-mass, food ingredient, plastic filler, abrasive, raw material in the cosmetic sector, dietary animal supplementation, etc. . OH is characterised by a strong variability according to olive characteristics and olive oil production process. Actually it does not exist any strategy able to quantify OH chemicalphysical attributes versus its correct utilisation adopting simple, efficient and low costs analytical tools. Furthermore the possibility to perform its continuous monitoring, without any samples collection and analysis at laboratory scale, could strongly enhance OH utilization, with a great economic and environmental benefits. In this paper an analytical approach, based on HyperSpectral Imaging (HSI) is presented. HSI allows to perform, also on-line, a full quantification of OH characteristics in order to qualify this product for its further re-use, with particular reference as bio-mass. HSI was applied to different samples of OH, characterized by different moisture, different residual pulp content and different size class distributions. Results are presented and critically evaluated.

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Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery

Cited by 88 publications

References 23 publications

Prediction of Physical Parameters of Pumpkin Seeds Using Neural Network

Prediction of Physical Parameters of Pumpkin Seeds Using Neural Network

Chemometrics in Food Technology

Hyperspectral Sensing Techniques Applied to Bio-masses Characterization: The Olive Husk Case

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