This study was performed to determine the proper conditions for measurements of reflectance spectra, and to develop models to predict the freshness attributes of beef using visible and near infrared (NIR) spectroscopic technique. The measured attributes for beef freshness were volatile basic nitrogen (VBN), thiobarbituric acid reacted substance (TBARS), total viable counts (TVC), color (CIE L*, a*, b* values), and pH value. A total of 240 beef spectra were collected during the storage time. Partial least squares (PLS) models were developed, to predict freshness attributes for beef samples. The appropriate spectral regions of PLS models to predict freshness attributes of beef were found in the range of 550–1000 nm. VBN and TVC models showed a good relationship between the predicted and the measured values. The results showed that NIR spectroscopy would be one of the high potential tools to determine beef freshness in a real-life purchase environment.

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This study was conducted to develop a numerical program to predict the effective height and width of a safety frame, or tip-over protective structure (TOPS), for a three-wheeled agricultural carrier. To hasten program development, the main algorithm of computation in the original program for a tractor with protective structure laterally rolling over was not modified for this study. The target carrier was assumed to be safe when the successive rollover on the slope was prevented by the adoption of TOPS with sufficient height and width. Based on catalogue data of commercially available carrier, the accuracy of the developed program was investigated. Results showed a predicted height of TOPS that is reasonable with respect to the carrier dimensions. Then, a small carrier model was constructed to verify the accuracy of analysis by comparing the experimentally obtained result of overturning and the numerical result of the model carrier. Results showed that the developed program had qualitatively sufficient accuracy for predicting the TOPS height. For more precise prediction of the necessary TOPS height for three-wheeled agricultural carrier, a computer program based on the contact physics should be developed to include rolling or sliding of vehicles.

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In this study, an algorithm based on combined image processing and machine learning techniques including artificial neural networks (ANN) and support vector machine (SVM) were implemented for grading peeled pistachio kernels (PPK) into five classes: green, yellowish green, yellow, mixed color and unwanted materials. Initially, the B-component of the images in L*a*b* color space and Otsu thresholding were used for segmentation of the images. Altogether, 72 chromatic and four shape features were extracted from the samples. After carrying out sensitivity analysis, the input vector was reduced to 26. Principal component analysis (PCA) was applied to further compress the size of the input vector to 7. The best ANN classifier had a 7-8-5 structure with correct classification rate (CCR) of 99.4%. The best kernel function for SVM algorithm was radial basis with CCR, C, sigma and the number of support vectors of 99.88, 10, 3.5 and 266, respectively.

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A very important ingredient in the recipe for a productive broiler breeder flock is the collection of frequent and accurate body weights. To achieve this goal in this paper image processing and support vector regression (SVR) were used as a non-invasive method. An ellipse fitting algorithm using generalized Hough transform was performed to localize chickens within the pen and the head as well as the tail of chickens was removed using Chan-Vese method. After that from broiler images six features were extracted, namely area, convex area, perimeter, eccentricity, major axis length and minor axis length. According to statistical analysis between weight estimation of SVR and manual measurement of birds up to 42 days, no significant difference was observed (P > 0.05). The RMSE (root mean square error), MAPE (mean absolute percentage error) and the R² (correlation coefficient) value of SVR algorithm were 67.88, 8.63% and 0.98, respectively. This shows that machine vision along with SVR could promisingly estimate the weight of life broiler chickens.

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Measuring the area of combine-harvested field sections corresponding to a truckload of grain received at a grain elevator (LHA: load harvest area) allows the estimation of brown rice yield. The objective of this study was to evaluate the accuracy of LHA measured based on combine movement traces when the traces were recorded by a GPS or a GPS + Quasi-Zenith Satellite System (QZSS) receiver. For the measurements using a GPS receiver, the mean and standard deviation of relative errors of LHA were 4.7% and 8.1%. The center of the relative error distribution shifted toward positive because positioning errors of a GPS receiver shifted outer edges of harvested sections outward and resulted in measuring larger harvested area than a true value. For the measurements using a GPS + QZSS receiver, the mean and standard deviation of relative errors of LHA were 0.5% and 4.8%. The center of the relative error distribution was around 0% for its better positioning accuracy. The relative errors of LHA tended to be large when an LHA was measured from a field section where only the outer-part of a field is harvested or several small harvested sections. These cases of LHA measurements increased the sum of the perimeter of harvested sections and resulted in deteriorating the measurement accuracy. This result indicated that measurement accuracy of LHA can be further improved by correcting error, which can be estimated based on positioning errors and the perimeter of harvested sections.

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The effect of surface type and angle, drop height, and number of impacts on the creep behavior of Lady Rosetta potato cultivar was studied. Instrumented sphere (impact recording device) was used to obtain the coefficient of restitution and to calculate the absorbed energy for steel sheet, steel rods, rubber-coated steel rods, and two-layer potato surfaces. The four-element model (E_o, E_r, η_o, and η_r) was used to simulate the creep behavior of samples. Results showed that there is significant effects of drop height, surface angle, and number of impact on the creep parameters (P < 0.05). Higher parameters values, i.e. lower incident strain, were associated with steel rods and steel sheet surfaces, dropping tubers from 100 cm, and dropping tubers for 5 and 10 times. Moreover, the two-layer potato surface was found to cause the lowest strain values to the dropped tubers compared to other tested surfaces. Non-linear regression analysis was conducted between the absorbed energy and creep parameters. Low regression performance was obtained for E_o, η_r with determination coefficient (R²) best values falling in the range of 0.40–0.69, while E_r and η_o showed fair regression. Results of this study could be used for improving storage facilities of potatoes by accurately estimating the strain affecting stored tubers and consequently the appropriate height such that strain values in the bottom layers do not negatively affect quality and shelf life of potato tubers.

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Rice is a staple for a large population around the world. Drying and tempering of rice, a known process overtime is still done improperly due to energy cost, know-how and other factors. This causes huge loses in quantity and quality. Different aspects of drying were investigated to optimize the process. Moisture contents and drying rate were determined. Japonica rice was treated using the parboiling process. The rice was dried and tempered at different periods and temperatures using sealed plastic bags. Various quality parameters were examined. Duncan multiple test and one way ANOVA were used to analyze data in SAS software. Moisture content after parboiling was increased to 28.3% from the initial 14.3%. A significant improvement in milling yield from 68.71% of a control to 73.4% of the optimal was recorded. Duncan multiple test and ANOVA analyses showed a possibility in tempering time reduction. Findings from this study provides more information on drying of parboiled Japonica rice and will enable optimization of the process.

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