NIR Computer Vision for Rapid Measurements of Some Physical Properties of Mangoes

2020 ◽  
Vol 7 (04) ◽  
Author(s):  
KRISHNA KUMAR PATEL ◽  
ABHIJIT KAR ◽  
MOHAMMAD ALI KHAN
Keyword(s):  
Computer Vision ◽  
Physical Properties ◽  
Near Infrared ◽  
Human Vision ◽  
Coefficient Of Determination ◽  
Harvest Management ◽  
Nutritional Values ◽  
Post Harvest ◽  
The Mean ◽  
Rapid Measurements

Quality assessment through quick sorting and grading of mangoes is today’s need, for not only to enhance the post harvest management process but also to minimize the post harvest losses and to recover more economic and nutritional values. Near infrared (NIR) based computer vision (CV) which is very close to human vision was used for rapid measurements of some physical properties of mangoes (Chausa, Dashehari). Lab View s/w was used to process the image and development of algorithm steps for analysis of the image properties. Fruit’s diameters were measured and compared with manual measurements using paired t-test, the 95% limits of agreement (Bland-Altman plot) and both measurements correlated using regression analysis. The results obtained were consistent and the correlations of the mean dimensions between both methods were found to be noticeably high. Coefficient of determination (R2) for all diameters was found between 0.951 and 0.90. Various fruit’s shape attributes important in design of harvest and post harvest equipment were also evaluated using NIR-CV technique. The present research, thus, could be the basis for the development of real time CV systems which would be near to human vision.

scholarly journals An automatic and non-intrusive hybrid computer vision system for the estimation of peel thickness in Thomson orange

2019 ◽  
Vol 16 (4) ◽  
pp. e0204
Author(s):  
Hossein Javadikia ◽  
Sajad Sabzi ◽  
Juan I. Arribas
Keyword(s):  
Computer Vision ◽  
Mean Square Error ◽  
Food Industry ◽  
Vision System ◽  
Optimization Technique ◽  
Orange Peel ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Hybrid Particle Swarm Optimization ◽  
The Mean

Orange peel has important flavor and nutrition properties and is often used for making jam and oil in the food industry. For previous reasons, oranges with high peel thickness are valuable. In order to properly estimate peel thickness in Thomson orange fruit, based on a number of relevant image features (area, eccentricity, perimeter, length/area, blue component, green component, red component, width, contrast, texture, width/area, width/length, roughness, and length) a novel automatic and non-intrusive approach based on computer vision with a hybrid particle swarm optimization (PSO), genetic algorithm (GA) and artificial neural network (ANN) system is proposed. Three features (width/area, width/length and length/area ratios) were selected as inputs to the system. A total of 100 oranges were used, performing cross validation with 100 repeated experiments with uniform random samples test sets. Taguchi’s robust optimization technique was applied to determine the optimal set of parameters. Prediction results for orange peel thickness (mm) based on the levels that were achieved by Taguchi’s method were evaluated in several ways, including orange peel thickness true-estimated boxplots for the 100 orange database and various error parameters: the sum square error (SSE), the mean absolute error (MAE), the coefficient of determination (R2), the root mean square error (RMSE), and the mean square error (MSE), resulting in mean error parameter values of R2=0.854±0.052, MSE=0.038±0.010, and MAE=0.159±0.023, over the test set, which to our best knowledge are remarkable numbers for an automatic and non-intrusive approach with potential application to real-time orange peel thickness estimation in the food industry.

The dynamics of physical properties, seed moisture content, market economics and post harvest management of six bean varieties (Phaseolus vulgaris) grown in the sub Saharan region in Africa

2014 ◽  
Vol 3 (1) ◽  
pp. 101-109
Author(s):  
Josphert Ngui Kimatu ◽  
Wincaster Makoani Mutuli ◽  
Jane Wanja Mbiri ◽  
Benard Mweu ◽  
Nahashon Musimba ◽  
...  
Keyword(s):  
Moisture Content ◽  
Physical Properties ◽  
Economic Losses ◽  
Eastern Region ◽  
Harvest Management ◽  
Economic Returns ◽  
Seed Moisture Content ◽  
Post Harvest ◽  
Grain Moisture ◽  
Sub Saharan

Managing grain moisture content is important because maximum economic return can be achieved by marketing at a certain moisture level of grain. Post-harvest management dictates that grains must be dried to certain levels to avoid development of fungal and insect problems, respiration and germination. However, over drying can also a lead to economic losses. Most farmers are aware of fungal development in moist grains but few are aware that they make less profit by over drying. Moreover, there are also bean varieties which genetically retain more water than others and hence can be safer and have more economic returns compared to others. But, this also should be matched to the rain pattern in a growing region. We compared six varieties of beans (KAT B1, KAT B9, and Kakunzu (KKZ), Rose Coco (GLP2/RCC), Kenya Tamu and KAT X56) grown in the South Eastern region of Kenya and found significant differences in dry moisture content, physical properties and grain weights. The Rose Coco and KAT X56 varieties had the highest moisture content retention but the KKZ variety the lowest. This explained why KKZ is favoured more by famers in arid areas with less rain during fruit maturation.

Human Vision and Computer Vision

1985 ◽  
Vol 30 (1) ◽  
pp. 47-47
Author(s):  
Herman Bouma
Keyword(s):  
Computer Vision ◽  
Human Vision

scholarly journals Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 39-55
Author(s):  
Rodgers Makwinja ◽  
Seyoum Mengistou ◽  
Emmanuel Kaunda ◽  
Tena Alemiew ◽  
Titus Bandulo Phiri ◽  
...  
Keyword(s):  
Time Series ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Annual Fish ◽  
Unit Effort ◽  
The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

scholarly journals IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1631
Author(s):  
Bruno Guilherme Martini ◽  
Gilson Augusto Helfer ◽  
Jorge Luis Victória Barbosa ◽  
Regina Célia Espinosa Modolo ◽  
Marcio Rosa da Silva ◽  
...  
Keyword(s):  
Technology Acceptance ◽  
Mean Square Error ◽  
Ease Of Use ◽  
Coefficient Of Determination ◽  
Square Root ◽  
Mean Square ◽  
Use Context ◽  
The Mean ◽  
Acceptance Model ◽  
The Internet Of Things

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.

Prediction of Fatty Acid Content in Beef Neck Lean by near Infrared Reflectance Analysis

1998 ◽  
Vol 6 (1) ◽  
pp. 229-234 ◽  
Author(s):  
William R. Windham ◽  
W.H. Morrison
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Near Infrared ◽  
Fatty Acid Content ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Longissimus Dorsi ◽  
Near Infrared Reflectance ◽  
Total Fatty Acids

Near infrared (NIR) spectroscopy in the prediction of individual and total fatty acids of bovine M. Longissimus dorsi neck muscles has been studied. Beef neck lean was collected from meat processing establishments using advanced meat recovery systems and hand-deboning. Samples ( n = 302) were analysed to determine fatty acid (FA) composition and scanned from 400 to 2498 nm. Total saturated and unsaturated FA values ranged from 43.2 to 62.0% and 38.3 to 56.2%, respectively. Results of partial least squares (PLS) modeling shown reasonably accurate models were attained for total saturate content [standard error of performance ( SEP = 1.10%); coefficient of determination on the validation set ( r2 = 0.77)], palmitic ( SEP = 0.94%; r2 = 0.69), unsaturate ( SEP = 1.13%; r2 = 0.77), and oleic ( SEP = 0.97; r2 = 0.78). Prediction of other individual saturated and unsaturated FAs was less accurate with an r2 range of 0.10 to 0.53. However, the sum of individual predicted saturated and unsaturated FA was acceptable compared with the reference method ( SEP = 1.10 and 1.12%, respectively). This study shows that NIR can be used to predict accurately total fatty acids in M. Longissimus dorsi muscle.

Which common NIRS variable reflects muscle estimated lactate threshold most closely?

2006 ◽  
Vol 31 (5) ◽  
pp. 612-620 ◽  
Author(s):  
Lixin Wang ◽  
Takahiro Yoshikawa ◽  
Taketaka Hara ◽  
Hayato Nakao ◽  
Takashi Suzuki ◽  
...  
Keyword(s):  
Lactate Threshold ◽  
Near Infrared ◽  
Ventilatory Threshold ◽  
Lactate Concentration ◽  
Blood Lactate Concentration ◽  
Oxygenation Index ◽  
Muscle Lactate ◽  
Tissue Oxygenation Index ◽  
The Mean ◽  
Tissue Hemoglobin

Various near-infrared spectroscopy (NIRS) variables have been used to estimate muscle lactate threshold (LT), but no study has determined which common NIRS variable best reflects muscle estimated LT. Establishing the inflection point of 2 regression lines for deoxyhaemoglobin (ΔHHbi.p.), oxyhaemoglobin (ΔO2Hbi.p.), and tissue oxygenation index (TOIi.p.), as well as for blood lactate concentration, we then investigated the relationships between NIRS variables and ventilatory threshold (VT), LT, or maximal tissue hemoglobin index (nTHImax) during incremental cycling exercise. ΔHHbi.p. and TOIi.p. could be determined for all 15 subjects, but ΔO2Hbi.p. was determined for only 11 subjects. The mean absolute values for the 2 measurable slopes of the 2 continuous linear regression lines exhibited increased changes in 3 NIRS variables. The workload and VO2 at ΔO2Hbi.p. and nTHImax were greater than those at VT, LT, ΔHHbi.p., and TOIi.p.. For workload and VO2, ΔHHbi.p. was correlated with VT and LT, whereas ΔO2Hbi.p. was correlated with nTHImax, and TOIi.p. with VT and nTHImax. These findings indicate that ΔO2Hb strongly corresponds with local perfusion, and TOI corresponds with both local perfusion and deoxygenation, but that ΔHHb can exactly determine deoxygenation changes and reflect O2 metabolic dynamics. The finding of strongest correlations between ΔHHb and VT or LT indicates that ΔHHb is the best variable for muscle LT estimation.

Non-Traditional Applications of near Infrared Spectroscopy Based on the Optical Characteristic Models for a Biological Material Having Cellular Structure

1998 ◽  
Vol 6 (1) ◽  
pp. 41-46 ◽  
Author(s):  
Satoru Tsuchikawa
Keyword(s):  
Biological Material ◽  
Path Length ◽  
Cellular Structure ◽  
Process Model ◽  
Near Infrared ◽  
Traditional Approach ◽  
Nir Spectroscopy ◽  
Optical Path Length ◽  
Optical Path ◽  
The Mean

Non-destructive measurements, based on near infrared (NIR) spectroscopy, on biological material with a cellular structure like wood require a non-traditional approach. We have developed new concepts to model the optical properties of a sample having cellular structure, for the illumination conditions of the spectrometer available to us. A set of optical models, which consisted of the directional characteristics models, the light-path models and the equivalent surface roughness model was proposed to clarify the behaviour of light propagation in a wood sample. Furthermore, the mean optical path length, which was derived by incorporating the nth power cosine model of radiant intensity into the diffusion process model in consideration of the parallel beam component of incident light, was calculated. By introducing the concept of equivalent sample thickness, compatible with the mean optical path length, into the Kubelka–Munk theory, generalised input/output equations for radiation were constructed. In this non-traditional application of NIR spectroscopy, these optical concepts make it possible to analyse both the physical condition and chemical composition of a biological material with a cellular structure.

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