Assessment of Tunisian virgin olive oils via synchronized analysis of sterols, phenolic acids, and fatty acids in combination with multivariate chemometrics

Ennouri, Karim; Hlima, Hajer Ben; Ayed, Rayda Ben; Braïek, Olfa Ben; Mazzarello, Maura; Ottaviani, Ennio; Mallouli, Lotfi; Smaoui, Slim

doi:10.1007/s00217-019-03303-2

Cited by 5 publications

(7 citation statements)

References 74 publications

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“…(2) Artificial Neural Networks. e utilization of Artificial Neural Networks for remote sensing categorization is incited by the fact that the human brain is proficient at handling high amounts of information and records from a wide range of sources [99,100], and that scientific renderings of this methodology might be valuable for preparing and analysing picture information. While applied to picture categorization, an Artificial Neural Network is a hugely equal allocated processor made up of basic handling items that gains information from its environment via a self-learning operation, to adaptively build linkages involving the input records, as for example, satellite imagery attributes, and the output records, as for example, target cover groups [101].…”

Section: Categorizationmentioning

confidence: 99%

Usage of Artificial Intelligence and Remote Sensing as Efficient Devices to Increase Agricultural System Yields

Ennouri

Smaoui

Gharbi

et al. 2021

Journal of Food Quality

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Artificial Intelligence is an emerging technology in the field of agriculture. Artificial Intelligence-based tools and equipment have actually taken the agriculture sector to a different level. This new technology has improved crop production and enhanced instantaneous monitoring, processing, and collection. The most recent computerized structures using remote sensing and drones have made a significant contribution to the agro-based domain. Moreover, remote sensing has the capability to support the development of farming applications with the aim of facing this main defy, via giving cyclic records on yield status during studied periods at diverse degrees and for diverse parameters. Various hi-tech, computer-supported structures are created to determine different central factors such as plant detection, yield recognition, crop quality, and several other methods. This paper includes the techniques employed for the analysis of collected information in order to enhance the productivity, forecast eventual threats, and reduce the task load on cultivators.

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Section: Categorizationmentioning

confidence: 99%

Usage of Artificial Intelligence and Remote Sensing as Efficient Devices to Increase Agricultural System Yields

Ennouri

Smaoui

Gharbi

et al. 2021

Journal of Food Quality

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“…Change detection methods based on the EM algorithm achieve higher accuracy than other methods but they require a priori knowledge of the joint probability class [81]. The artificial neural networks approach can provide significant change detection results, particularly when categories are not normally distributed, but it needs a lot of preparation time and it is sensitive to the amount of training data [84]. Artificial neural networks are seen as nonlinear statistical data modelling tools where complex relationships involving inputs and outputs are designed or patterns are found.…”

Section: Change Detectionmentioning

confidence: 99%

Detection of Urban and Environmental Changes via Remote Sensing

2021

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Rapid climate and environmental change at limited, regional, and general scales have been a major concern for researchers in a number of fields, such as topography, economy, environment, and sustainable development. Changes in land cover and land use have taken into account due to potential impacts on soil depletion, amplified runoff , water balance, and climate change. A detailed understanding of the characteristics of land exploitation and land structure is indispensable for the study of their influences on life and nature. In addition, urban extension is a major form of land extraction and land transformation, as it relates to the rise in population and the availability of financial services. Remote sensing records have been shown to be important for reporting and perceiving urban development and transition, and for providing critical information for future growth. Transformation and shift identification are the tools used to recognise distinctions in a land cover by tracking them at various times. In addition, various change identification and detection approaches are routinely tested with the goal of providing the greatest change detection deductions for a particular appliance. This review would aim to establish a practical plan that combines remote sensing techniques, on the one hand, and modelling approaches, on the other, to track land use, to cover changes, and to predict future trends.

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“…Between diverse data mining methods and techniques, the Artificial Neural Network (ANN) technique is one of the most extensively employed methodologies in engineering, particularly when data or information is accessible from several sources, in addition to a priori understanding of explanatory arrangements or developments which is accessible because of capacity of ANN to study complex configurations rapidly [52]. This method was successfully employed in many fields as biology [53][54][55][56], physics [57,58], chemistry [59,60], etc. Besides, the decision tree systems of data mining approaches are more directly adapted for classification, from the time when data symbolising a specified individual are classed through the decision tree construction to be classified directly into a preprogrammed group [61,62].…”

Section: Imagery Treatment and Prediction In Mappingmentioning

confidence: 99%

Remote Sensing: An Advanced Technique for Crop Condition Assessment

Ennouri

Kallel

2019

Mathematical Problems in Engineering

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Actually, cultivators are increasingly arranging innovative high technical and scientific estimations in the aim to enhance agricultural sustainability, effectiveness, and/or plant health. Innovative farming technologies incorporate biology with smart agriculture: computers and devices exchange with one another autonomously in a structured farm management system. Throughout this structure, smart agriculture can be accomplished; cultivators decrease plantation inputs (pesticides and fertilizers) and increase yields via integrated pest management and/or biological control. The emerging concept of remote sensing may provide a framework to systematically consider these issues of smart farming technology and to embed high-tech agriculture better. The impact(s) may be beneficial depending on how tools, such as data mining, and imagery technologies, such as picture treatment and analysis, are applied. Remote sensing technology is discussed in this review and demonstrates its possibility to create novel opportunities for scientists (and agronomists) to explore aspects of biological phenomena that cannot be accessed through usual mechanisms or processes.

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Assessment of Tunisian virgin olive oils via synchronized analysis of sterols, phenolic acids, and fatty acids in combination with multivariate chemometrics

Cited by 5 publications

References 74 publications

Usage of Artificial Intelligence and Remote Sensing as Efficient Devices to Increase Agricultural System Yields

Usage of Artificial Intelligence and Remote Sensing as Efficient Devices to Increase Agricultural System Yields

Detection of Urban and Environmental Changes via Remote Sensing

Remote Sensing: An Advanced Technique for Crop Condition Assessment

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