Visible feature engineering to detect fraud in black and red peppers.

Visible imaging is a fast, cheap, and accurate technique in the assessment of food quality and safety. The technique was used in the present research to detect sea foam adulterant levels in black and red peppers. The fraud levels included 0, 5, 15, 30, and 50%.

Ability of visible imaging and machine learning in detection of chickpea flour adulterant in original cinnamon and pepper powders.

Adulteration detection in plant-based medicinal powders is necessary to provide high quality products due to the economic and health importance of them. According to advantages of imaging technology as non-destructive tool with low cost and time, the present research aims to evaluate the ability of the visible imaging combined with machine learning for distinguish original products and the adulterated samples with different levels of chickpea flour. The original products were black pepper, red pepper, and cinnamon, the adulterant was chick pea, and the adulteration levels were 0, 5, 15, 30, and 50 %.

The effect of raw material (cow and chicken manure) and reactor type for improving and maximizing biogas production.

Biomass energy is a type of renewable energy and animal waste is one of the main resources for its production. The purpose of this study is to investigate the effect of raw material type (cow and chicken manure) and the type of reactor (digester) on the biogas produced by measuring the amount of methane in the product. Three types of digester (metal, simple PVC, and PVC with leachate rotation) with the same volume (10 L) were prepared.

A machine learning system to identify progress level of dry rot disease in potato tuber based on digital thermal image processing.

This study proposed a quick and reliable thermography-based method for detection of healthy potato tubers from those with dry rot disease and also determination of the level of disease development. The dry rot development inside potato tubers was classified based on the Wiersema Criteria, grade 0 to 3. The tubers were heated at 60 and 90 °C, and then thermal images were taken 10, 25, 40, and 70 s after heating.

Feasibility study of detecting some milk adulterations using a LED-based Vis-SWNIR photoacoustic spectroscopy system.

The aim of this study is to evaluate a previousely developed photoacoustic spectroscopy system with light sources of visible to short-wave near infrared (Vis-SWNIR, 395-940 nm) for detection of adulterations in cow's milk including formalin, urea, hydrogen peroxide, starch, sodium hypochlorite, and detergent powder. The results of principal component analysis (PCA) showed a very good visual differentiation of different adulterations. The artificial neural networks (ANN) showed the highest classification accuracy (97.

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.

Estimation of dust concentration by a novel machine vision system.

The dust phenomenon is one of the main environmental problems that it reversely affects human health and economical and social activities. In the present research, a novel algorithm has been developed based on image processing to estimate dust concentration. An experimental setup was implemented to create airborne dust with different concentration values from 0 to 2750 µg.

Energy, economic, and environmental assessment of coriander seed production using material flow cost accounting and life cycle assessment.

The agricultural sector in the world is facing social expectations to improve energy efficiency and reduce environmental impacts, and at the same producing enough food and fiber for the growing world population. The purpose of the present research is to determine the economic, energy consumption, and environmental impacts in coriander seed production using material flow cost accounting approach along with life cycle assessment. The positive output and negative energy were 25,485 and 6742 MJ ha, respectively.

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

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.

Correction to: Comparing energy state and environmental impacts in ostrich and chicken production systems.

The correct name of the 3 Author is shown in this paper.

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.

Multivariate modeling of settling depth of apple fruit (Red Delicious variety) in water.

Settling depth of apple was determined by a water column and a digital camera. The depth was experimentally modeled using multivariate regression using a coded program in MATLAB software. The best models were based on the density, dropping height volume/mass with coefficient of determination and mean square error of 0.