Sorting Olive Batches for the Milling Process Using Image Processing

Puerto, D. Aguilera; Gila, Diego Manuel Martínez; García, Javier Gámez; Ortega, Juan Gómez

doi:10.3390/s150715738

Cited by 35 publications

(21 citation statements)

References 31 publications

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“…We therefore sort to a manual method of marking and calculating the coverage. Automatic detection of coniferous trees was not executed similar to the methods used by Dalponte et al and Puerto et al due to the unavailability of high-level image processing techniques (Dalponte et al 2015, Puerto et al 2015). Nevertheless, it shows promise for automation.…”

Section: Coniferous Coverage Area Estimationmentioning

confidence: 99%

Calculating coniferous tree coverage using unmanned aerial vehicle photogrammetry

2017

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Unmanned aerial vehicles (UAVs) are a new and yet constantly developing part of forest inventory studies and vegetation-monitoring fields. Covering large areas, their extensive usage has saved time and money for researchers and conservationists to survey vegetation for various data analyses. Post-processing imaging software has improved the effectiveness of UAVs further by providing 3D models for accurate visualization of the data. We focus on determining the coniferous tree coverage to show the current advantages and disadvantages of the orthorectified 2D and 3D models obtained from the image photogrammetry software, Pix4Dmapper Pro-Non-Commercial. We also examine the methodology used for mapping the study site, additionally investigating the spread of coniferous trees. The collected images were transformed into 2D black and white binary pixel images to calculate the coverage area of coniferous trees in the study site using MATLAB. The research was able to conclude that the 3D model was effective in perceiving the tree composition in the designated site, while the orthorectified 2D map is appropriate for the clear differentiation of coniferous and deciduous trees. In its conclusion, the paper will also be able to show how UAVs could be improved for future usability.

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Section: Coniferous Coverage Area Estimationmentioning

confidence: 99%

Calculating coniferous tree coverage using unmanned aerial vehicle photogrammetry

2017

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“…With the development of agricultural technology, image processing technology is increasingly applied to detect and classify the quality of agricultural products [1]- [3]. Extensive studies have been performed in various fields like weed identification [4]- [6], crop diseases and insect pests identification and diagnosis [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Maize Leaf Disease Identification Based on Feature Enhancement and DMS-Robust Alexnet

Zhou

et al. 2020

IEEE Access

138

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The identification of maize leaf diseases will meet great challenges because of the difficulties in extracting lesion features from the constant-changing environment, uneven illumination reflection of the incident light source and many other factors. In this paper, a novel maize leaf disease recognition method is proposed. In this method, we first designed a maize leaf feature enhancement framework with the capability of enhancing the features of maize under the complex environment. Then a novel neural network is designed based on backbone Alexnet architecture, named DMS-Robust Alexnet. In the DMS-Robust Alexnet, dilated convolution and multi-scale convolution are combined to improve the capability of feature extraction. Batch normalization is performed to prevent network over-fitting while enhancing the robustness of the model. PRelu activation function and Adabound optimizer are employed to improve both convergence and accuracy. In experiments, it is validated from different perspectives that the maize leaf disease feature enhancement algorithm is conducive to improving the capability of the DMS-Robust Alexnet identification. Our method demonstrates strong robustness for maize disease images collected in the natural environment, providing a reference for the intelligent diagnosis of other plant leaf diseases. INDEX TERMS Image enhancement, dilated convolution, multi-scale convolution, maize leaf disease, convolutional neural network.

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“…Different works have evaluated the use of non-invasive technologies, such as near infrared spectrometers, computer vision, or electronic noses and tongues, to monitor the product qualities at all stages of the production process [ 10 , 11 , 12 , 13 , 14 , 15 ]. Particularly, the application of the near infrared (NIR) technology on the olive oil industry has been reported in [ 16 ].…”

Section: Problem Description and Earlier Workmentioning

confidence: 99%

Non-Invasive Methodology to Estimate Polyphenol Content in Extra Virgin Olive Oil Based on Stepwise Multilinear Regression

Gila¹,

Marchal²,

Ortega³

et al. 2018

Sensors

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Normally the olive oil quality is assessed by chemical analysis according to international standards. These norms define chemical and organoleptic markers, and depending on the markers, the olive oil can be labelled as lampante, virgin, or extra virgin olive oil (EVOO), the last being an indicator of top quality. The polyphenol content is related to EVOO organoleptic features, and different scientific works have studied the positive influence that these compounds have on human health. The works carried out in this paper are focused on studying relations between the polyphenol content in olive oil samples and its spectral response in the near infrared spectra. In this context, several acquisition parameters have been assessed to optimize the measurement process within the virgin olive oil production process. The best regression model reached a mean error value of 156.14 mg/kg in leave one out cross validation, and the higher regression coefficient was 0.81 through holdout validation.

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Sorting Olive Batches for the Milling Process Using Image Processing

Cited by 35 publications

References 31 publications

Calculating coniferous tree coverage using unmanned aerial vehicle photogrammetry

Calculating coniferous tree coverage using unmanned aerial vehicle photogrammetry

Maize Leaf Disease Identification Based on Feature Enhancement and DMS-Robust Alexnet

Non-Invasive Methodology to Estimate Polyphenol Content in Extra Virgin Olive Oil Based on Stepwise Multilinear Regression

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