Physical and nutritional properties of hawthorn fruit (Crataegus pontica L.)

Hawthorn, from Rosaceae family, is one of the important forest fruits which there are different species in Iran. In this study, some characteristics of hawthorn fruit (Crataegus pontica), including physical properties (fruit length, width, thickness, geometric, arithmetic and equivalent mean diameter, surface area, sphericity, aspect ratio, thousand fruit mass and true density) and nutritional properties (total dry matter, total soluble solid, titratable acidity and moisture content) were considered. Results showed that average of fruit length, width and thickness were 1.53 mm, 1.95 mm and 1.78 mm, respectively. The geometric mean diameter was same to arithmetic mean diameter (1.75 mm), while equivalent mean diameter was higher than both (1.76 mm). Some physical properties such as sphericity (1.13 %), surface area (1.69 mm) and aspect ratio (1.26) were determined. Average of 100 fruit weight in this species was 306.54 g and it is estimated 3.06 g for one fruit. Total soluble solid percent (TSS) and titratable acidity percent (TA) of fruit hawthorn were estimated 18.7 % and 1.71 %, respectively

Experimental modeling of orange settling depth in water

Settling depth of orange fruits and vegetables with the density lower than the density of water is an important hydrodynamic property important in hydraulic sorting and transporting. In this research, settling depth of orange fruit with regular shapes was experimentally modeled. The considered parameters in multivariate modeling were fruit characteristics, density, mass and volume, and dropping height of the fruits. The characteristics were determined by standard methods. The settling depth was determined by a water column and a digital camera. The models were obtained in MATLAB software. The best model was based on the density, volume and dropping height with coefficient of determination (R2) and mean squire error (MSE) of 0.89 and 4.67×10-7, respectively

Precision spray modeling using image processing and artificial neural network

This study employed artificial neural network method for predicting the sprayer drift under different conditions using image processing technique. A wind tunnel was used for providing air flow in different velocities. Water Sensitive Paper (WSP) was used to absorb spray droplets and an automatic algorithm processed the images of WSPs for measuring droplet properties including volume median diameter (Dv0.5) and Surface Coverage Percent (SCP). Four Levenberg-Marqurdt models were developed to correlate the sprayer drift (output parameter) to the input parameters (height, pressure, wind velocity and Dv0.5). The ANN models were capable of predicting the output variables in different conditions of spraying with a high performance. Both models predicted the output variables with R2 values higher than 0.96 indicating the accuracy of the selected networks. Therefore, the developed predictor models can be used in precision agriculture for decreasing spray costs and losses and also environmental contamination

The driver responses to the vibration of tractor

Whole body vibration is one of the main causes of musculoskeletal disorders of drivers. The purpose of this study was to investigate the allowable exposer time, the care limit and driver response to the vibrations of the seat of ITM 475 Tractor. A three-way accelerometer was used to carry out the tests in the present study based on the ISO 2631-1 international vibration standard. The studied factors were engine rotational speed at three levels of 1000, 1500 and 2000 rpms, gear ratio (1, 2 and 3) and road at two level, dirt and asphalt. The obtained data were analyzed through factorial experiment based on completely randomized design with 18 treatments and three replications. The results showed that effects of the main factors and those of their interactions on the total vibration emitted from the tractor seat were significant at 1% probability level. The highest amount of whole body vibration on a dirt road was 1.49 m s-2 which took placed at 2000 rpm and the 3rd gear ratio. Consequently, the minimum exposure time and the driver care time limit were 1.16 and 0.14 h, respectively. This treatment was in very uncomfortable range. The maximum whole body vibration for 8 h ride on ITM 475 was 0.85 m s-2. Therefore, it is necessary to reduce whole body vibration of the studied tractor via designing a cabin and/or a new seat

Design, construction and evaluation of a sprayer drift measurement system

Spray drift study for reducing environmental hazards and protecting crops is of high importance as the pesticides used today are more active and many are non-selective. Drift potential can be restricted by assessing and optimizing equipment design, application parameters, the liquid spray properties, type of formulation and environmental conditions. The aim of this research was to design, construct and assess an intelligent system to determine the level of the spraying drift. The main parts of the system were liquid supply mechanism, pipes and nozzles, a controlled pneumatic system to pressurize the liquid, a nozzle moving system with a controlling panel and a tunnel for wind providing and control. To assess the performance of sprayer and drift of droplets, water sensitive papers were placed in different distances from the nozzle considering different environmental conditions including: wind speed, spraying pressure and height. The evaluation results showed that the drift was increased with increasing of sparing pressure and nozzle height.

Influence of vermicompost and sheep manure on mechanical properties of tomato fruit

Mechanical properties of the horticultural products play an important role in improving the products quality and storage life after harvesting and also reducing product waste. Recently, using organic fertilizers has increasing trend for producing high‐quality products as well as improvement of soil quality. Two of the best options to produce organic material and sustainability of agricultural production are vermicompost and sheep manure. The present study relied on determination of mechanical properties through pressure and shear tests. Vermicompost and sheep manure were used separately to fertilize the soil. After planting tomato seeds and harvesting, tomato fruits were analyzed by a universal test machine. The results showed that vermicompost was a better fertilizer than sheep manure due to its more appropriate carbon to nitrogen ratio (C/N), acidity, and salinity. Also, in the pressure test, the maximum force required for bruise of tomato produced with vermicompost (41.5N) was more than that of control sample (no fertilizer) and sheep manure. In the shearing test, the maximum force required for shearing tomato produced with vermicompost (58.60 N) was lower than that of control sample (no fertilizer) and sheep manure. The findings of this study can be used to reduce the amount of waste at different stages of tomato production and supply including the design and optimization of processing and transportation equipment

Evaluation of image processing technique and discriminant analysis methods in postharvest processing of carrot fruit

Abstract The most important process before packaging and preserving agricultural products is sorting operation. Sort of carrot by human labor is involved in many problems such as high cost and product waste. Image processing is a modern method, which has different applications in agriculture including classification and sorting. The aim of this study was to classify carrot based on shape using image processing technique. For this, 135 samples with different regular and irregular shapes were selected. After image acquisition and preprocessing, some features such as length, width, breadth, perimeter, elongation, compactness, roundness, area, eccentricity, centroid, centroid nonhomogeneity, and width nonhomogeneity were extracted. After feature selection, linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were used to classify the features. The classification accuracies of the methods were 92.59 and 96.30, respectively. It can be stated that image processing is an effective way in improving the traditional carrot sorting techniques

Detection of Unripe Kernels and Foreign Materials in Chickpea Mixtures Using Image Processing

The existence of dockage, unripe kernels, and foreign materials in chickpea mixtures is one of the main concerns during chickpea storage and marketing. Novel algorithms based on image processing were developed to detect undesirable, foreign materials, and matured chickpea kernels in the chickpea mixture. Images of 270 objects including 54 sound samples and 36 samples of each undesired object were prepared and features of these acquired images were extracted. Different models based on linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural networks (ANN) methods were developed by using MATLAB. Three classification algorithms based on LDA, SVM, and ANN methods were developed. The classification accuracy in training, testing, and overall detection showed the superiority of ANN (99.4, 92.6, and 94.4%, respectively) and LDA (91.1, 94.0, and 91.9%, respectively) over the SVM (100, 53.7, and 88.5%, respectively). The developed image processing technique can be incorporated with a vision-based real-time system

Development of an Intelligent Imaging System for Ripeness Determination of Wild Pistachios

Rapid, non-destructive, and smart assessment of the maturity levels of fruit facilitates their harvesting and handling operations throughout the supply chain. Recent studies have introduced machine vision systems as a promising candidate for non-destructive evaluations of the ripeness levels of various agricultural and forest products. However, the reported models have been fruit-specific and cannot be applied to other fruit. In this regard, the current study aims to evaluate the feasibility of estimating the ripeness levels of wild pistachio fruit using image processing and artificial intelligence techniques. Images of wild pistachios at four ripeness levels were recorded using a digital camera, and 285 color and texture features were extracted from 160 samples. Using the quadratic sequential feature selection method, 16 efficient features were identified and used to estimate the maturity levels of samples. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and an artificial neural network (ANN) were employed to classify samples into four ripeness levels, including initial unripe, secondary unripe, ripe, and overripe. The developed machine vision system achieved a correct classification rate (CCR) of 93.75, 97.5, and 100%, respectively. The high accuracy of the developed models confirms the capability of the low-cost visible imaging system in assessing the ripeness of wild pistachios in a non-destructive, automated, and rapid manner

Dust determination methods and instrumentations

In recent years, different aspects of dust storm, as one of the environmental problems, have been investigated by many researchers. One of the main goals in this regard is controlling of the dust and its subsequent hazards. The first step in the controlling process of dust or its hazards is detection of dust as well as determination of dust concentration. There are different methods and devices to determine dust concentration. In the work, the existing methods and devices to measure the dust concentration in the environment have been presented and explained. Furthermore, the potential application of image processing technique as a low cost method to determine dust concentration has been discussed