Non-destructive low-cost approach for fuzzy classification of tomato images based on firmness prediction using regression

2017 ◽  
Vol 32 (5) ◽  
pp. 3641-3653
Author(s):  
Priti Sehgal ◽  
Nidhi Goel
Keyword(s):  
Low Cost ◽  
Fuzzy Classification ◽  
Cost Approach ◽  
Non Destructive

scholarly journals Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Control Treatment ◽  
Residue Detection ◽  
Non Destructive ◽  
Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

scholarly journals Classification of soybean tempe quality using deep learning

2021 ◽  
Vol 924 (1) ◽  
pp. 012022
Author(s):  
Y Hendrawan ◽  
B Rohmatulloh ◽  
I Prakoso ◽  
V Liana ◽  
M R Fauzy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Low Cost ◽  
Confusion Matrix ◽  
Learning Rate ◽  
Traditional Food ◽  
Quality Classification ◽  
Non Destructive

Abstract Tempe is a traditional food originating from Indonesia, which is made from the fermentation process of soybean using Rhizopus mold. The purpose of this study was to classify three quality levels of soybean tempe i.e., fresh, consumable, and non-consumable using a convolutional neural network (CNN) based deep learning. Four types of pre-trained networks CNN were used in this study i.e. SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The sensitivity analysis showed the highest quality classification accuracy of soybean tempe was 100% can be achieved when using AlexNet with SGDm optimizer and learning rate of 0.0001; GoogLeNet with Adam optimizer and learning rate 0.0001, GoogLeNet with RMSProp optimizer, and learning rate 0.0001, ResNet50 with Adam optimizer and learning rate 0.00005, ResNet50 with Adam optimizer and learning rate 0.0001, and SqueezeNet with RSMProp optimizer and learning rate 0.0001. In further testing using testing-set data, the classification accuracy based on the confusion matrix reached 98.33%. The combination of the CNN model and the low-cost digital commercial camera can later be used to detect the quality of soybean tempe with the advantages of being non-destructive, rapid, accurate, low-cost, and real-time.

A Low-cost Approach for Effective Shape-based Retrieval and Classification of Medical Images

2006 ◽  
Author(s):  
J.C. Felipe ◽  
J.B. Olioti ◽  
A.J.M. Traina ◽  
M.X. Ribeiro ◽  
E.P.M. Sousa ◽  
...  
Keyword(s):  
Medical Images ◽  
Low Cost ◽  
Cost Approach

scholarly journals Learning-based classification of multispectral images for deterioration mapping of historic structures

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Efstathios Adamopoulos
Keyword(s):  
Low Cost ◽  
Spectral Band ◽  
Detailed Knowledge ◽  
Automated Mapping ◽  
Supervised Segmentation ◽  
Historic Structures ◽  
Band Combinations ◽  
Non Destructive

AbstractThe conservation of historic structures requires detailed knowledge of their state of preservation. Documentation of deterioration makes it possible to identify risk factors and interpret weathering mechanisms. It is usually performed using non-destructive methods such as mapping of surface features. The automated mapping of deterioration is a direction not often explored, especially when the investigated architectural surfaces present a multitude of deterioration forms and consist of heterogeneous materials, which significantly complicates the generation of thematic decay maps. This work combines reflectance imaging and supervised segmentation, based on machine learning methods, to automatically segment deterioration patterns on multispectral image composites, using a weathered historic fortification as a case study. Several spectral band combinations and image classification techniques (regression, decision tree, and ensemble learning algorithmic implementations) are evaluated to propose an accurate approach. The automated thematic mapping facilitates the spatial and semantic description of the deterioration patterns. Furthermore, the utilization of low-cost photographic equipment and easily operable digital image processing software adds to the practicality and agility of the presented methodology.

scholarly journals UAV-based training for fully fuzzy classification of Sentinel-2 fluvial scenes

2020 ◽  
Author(s):  
Patrice Carbonneau ◽  
Barbara Belletti ◽  
Marco Micotti ◽  
Andrea Casteletti ◽  
Stefano Mariani ◽  
...  
Keyword(s):  
Neural Networks ◽  
Spatial Resolution ◽  
Low Cost ◽  
Super Resolution ◽  
Training Data ◽  
Fuzzy Classification ◽  
Training Phase ◽  
Small Channels ◽  
Sentinel 2

<p><span>In current fluvial remote sensing approaches, there exists a certain dichotomy between the analysis of small channels at local scales which is generally done with airborne data and the analysis of entire basins at regional and national scales with satellite data. </span><span>One possible solution to this challeng</span><span>e</span><span> is to use low-altitude imagery from low-cost UAVs to provide sub-metric scale class information which can then be used to train fuzzy classification models for entire Sentinel 2 tiles</span><span>. </span><span>The fuzzy classification approach can allow for sub-pixel information and when extended to entire Sentinel 2 tiles, the method therefore develops information at a resolution of less than 10 meters (the best spatial resolution of Sentinel 2 bands) at regional scales. </span><span>In </span><span>this</span><span> contribution, we present </span><span>such </span><span>a method wh</span><span>ere</span><span> UAV </span><span>imagery </span><span>is used </span><span>as the training data for the fully fuzzy classification of</span><span> Sentinel 2 imagery. </span><span>We partition the fluvial corridor in three simple classes: water, dry sediment and vegetation.  Then we manually classify the local UAV imagery into highly accurate class rasters. In order to augment the value of the Sentinel 2 data, we use an established super-resolution method that delivers 10 meter spatial resolution across all 11 Sentinel 2 bands</span><span>. </span><span>We </span><span>then use the sub-metric UAV classifications as training data for the 10 meter super-resolved Sentinel 2 imagery and we</span><span> train </span><span>fuzzy classification </span><span>models using random forests, dense neural networks and convolutional neural networks (CNN). We find that CNN architectures perform best</span> <span>and </span><span>can predict class membership within a pixel of </span><span>a new </span><span>Sentinel 2 </span><span>tile not seen in the training phase</span><span> with a mean error of 0% and an RMS error of 1</span><span>8</span><span>%. Crisp class predictions derived from the fuzzy models range in accuracy from 88% to 9</span><span>9</span><span>%, </span><span>even in the case of tiles never seen in the training phase</span><span>. </span><span>With this approach, it is now possible to deploy a low-cost UAV in order to train a transferable CNN model that can predict </span><span>fuzzy classes at very large scales from freely available Sentinel 2 imagery. </span> <span>This approach can therefore serve as the basis for multi temporal classification and change detection of the Sentinel 2 archives.</span></p>

scholarly journals Classification of large green chilli maturity using deep learning

2021 ◽  
Vol 924 (1) ◽  
pp. 012009
Author(s):  
Y Hendrawan ◽  
B Rohmatulloh ◽  
I Prakoso ◽  
V Liana ◽  
M R Fauzy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Small Molecules ◽  
Classification Accuracy ◽  
Low Cost ◽  
Confusion Matrix ◽  
Learning Rate ◽  
Maturity Index ◽  
Maturity Stage ◽  
Non Destructive

Abstract Chili (Capsicum annuum L.) is the source of various nutraceutical small molecules, such as ascorbic acid (vitamin C), carotenoids, tocopherols, flavonoids, and capsinoids. The purpose of this study was to classify the maturity stage of large green chili into three maturity levels, i.e. maturity 1 (maturity index 1 / 34 days after anthesis (DAA)), maturity 2 (maturity index 3 / 47 DAA), and maturity 3 (maturity index 5 / 60 DAA) by using convolutional neural networks (CNN) based deep learning and computer vision. Four types of pre-trained networks CNN were used in this study i.e. SqueezeNet, GoogLeNet, ResNet50, and AlexNet. From the overall sensitivity analysis results, the highest maturity classification accuracy of large green chili was 93.89% which can be achieved when using GoogLeNet with SGDmoptimizer and learning rate of 0.00005. However, in further testing using testing-set data, the highest classification accuracy based on confusion matrix was reaching 91.27% when using the CNN SqueezeNet model with RMSProp optimizer and a learning rate of 0.0001. The combination of the CNN model and the low-cost digital commercial camera can later be used to detect the maturity of large green chili with the advantages of being non-destructive, rapid, accurate, low-cost, and real-time.

Fault Localization of Power-Ground Short via Signal Injection and Oscilloscope Technique

2010 ◽  
Author(s):  
Binh Nguyen
Keyword(s):  
Low Cost ◽  
Short Circuit ◽  
Fault Localization ◽  
Fault Isolation ◽  
Test Apparatus ◽  
Good Working ◽  
Signal Injection ◽  
Set Up ◽  
Non Destructive ◽  
Short Circuit Condition

Abstract For those attempting fault isolation on computer motherboard power-ground short issues, the optimal technique should utilize existing test equipment available in the debug facility, requiring no specialty equipment as well as needing a minimum of training to use effectively. The test apparatus should be both easy to set up and easy to use. This article describes the signal injection and oscilloscope technique which meets the above requirements. The signal injection and oscilloscope technique is based on the application of Ohm's law in a short-circuit condition. Two experiments were conducted to prove the effectiveness of these techniques. Both experiments simulate a short-circuit condition on the VCC3 power rail of a good working PC motherboard and then apply the signal injection and oscilloscope technique to localize the short. The technique described is a simple, low cost and non-destructive method that helps to find the location of the power-ground short quickly and effectively.

scholarly journals The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Supakorn Harnsoongnoen ◽  
Nuananong Jaroensuk
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Low Cost ◽  
Measuring System ◽  
Coefficient Of Determination ◽  
Water Displacement ◽  
Non Invasive ◽  
Future Potential ◽  
Non Destructive ◽  
Egg Freshness

AbstractThe water displacement and flotation are two of the most accurate and rapid methods for grading and assessing freshness of agricultural products based on density determination. However, these techniques are still not suitable for use in agricultural inspections of products such as eggs that absorb water which can be considered intrusive or destructive and can affect the result of measurements. Here we present a novel proposal for a method of non-destructive, non-invasive, low cost, simple and real—time monitoring of the grading and freshness assessment of eggs based on density detection using machine vision and a weighing sensor. This is the first proposal that divides egg freshness into intervals through density measurements. The machine vision system was developed for the measurement of external physical characteristics (length and breadth) of eggs for evaluating their volume. The weighing system was developed for the measurement of the weight of the egg. Egg weight and volume were used to calculate density for grading and egg freshness assessment. The proposed system could measure the weight, volume and density with an accuracy of 99.88%, 98.26% and 99.02%, respectively. The results showed that the weight and freshness of eggs stored at room temperature decreased with storage time. The relationship between density and percentage of freshness was linear for the all sizes of eggs, the coefficient of determination (R2) of 0.9982, 0.9999, 0.9996, 0.9996 and 0.9994 for classified egg size classified 0, 1, 2, 3 and 4, respectively. This study shows that egg freshness can be determined through density without using water to test for water displacement or egg flotation which has future potential as a measuring system important for the poultry industry.

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