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Non-destructive evaluation is advancing in examining the properties of fruits. Kiwi fruit stands out as one of the popular fruits globally. Due to the influence of various environmental factors and storage conditions, diligent checking and storage of this fruit are essential. Therefore, monitoring changes in its properties during storage in cold storage facilities is crucial. One nondestructive method utilised in recent years to investigate changes in fruit texture is the hyperspectral method. This study uses the support vector machine (SVM) method to assess hyperspectral method‘s effectiveness in examining property changes in four kiwi varieties during storage in addition to predicting the properties such as acidity and soluble solid content. The evaluation of the predictive machine learning model revealed an accuracy of 95% in predicting acidity and soluble solid content (SSC) changes in kiwi fruit during storage. Further, investigations found that the support vector machine method provided relatively lower accuracy and sensitivity in identifying product variety during storage, with an average accuracy ranging from about 91% to 94%. These findings suggest that integrating machine learning methods with outputs from techniques like hyperspectral imaging enhances the non-destructive detection capability of fruits. This integration transforms obtained results into practical outcomes, serving as an interface between software and hardware.

The presence of weed seeds and other impurities in the wheat grains affect the identification quality of the wheat grains. This study explores the possibility of identifying the wheat in the wheat grain mass and estimates the amount of impurities in the wheat mass based on the video processing combined with the artificial neural network (ANN) integrated by particle swarm optimization (PSO) algorithm. After pre‐processing the video of the mass movement on the conveyor belt, 35 shapes, color, and textural features were extracted from each grain sample in the image in the presence of the MATLAB software and image processing (IP) toolbox. The data obtained from the IP section were categorized into two approaches. The first one, purposes the identification of the wheat grains in the grain mass, and the other one, identifies each components in the wheat mass. Both of them employs the hybrid ANN‐PSO algorithm to achieve the highest classification accuracy and the lowest error value. According to the results, ANN with the architecture of 36‐10‐10‐5 and ANN with the architecture of 36‐8‐8‐5 overtakes the other architectures with the highest accuracy and performance values for training (100 and 100%) and testing phases (100 and 86.66%), respectively for the first and second approaches. Accordingly, based on the results of the hybrid ANN‐PSO algorithm, the highest classification accuracy was 98.62% and 97.77% (for training and testing phases, respectively) and 76.08% and 73.1% (for training and testing phases, respectively) related to the first and the second approaches, respectively. Finally, the video processing using ANN can be considered as a powerful approach for identifying the impurities in the wheat grain mass. Practical applications: One of the major problems in wheat and similar mass products is the presence of impurities inside the total mass, which drastically reduces the quality and the marketability of the product. Conventional methods of detecting the amount of impurities in the grain mass are always accompanied by difficulties such as lack of precision, time consuming, and tedious. The use of new technologies such as machine vision and artificial intelligence have been considered as the important tools in this regard. One of the practical applications of this study is to detect the amount of impurities in wheat grain mass at the inlet of silos and in pricing on grain masses. The system proposed in this study can accurately detect and report impurities in wheat grain mass. [ABSTRACT FROM AUTHOR]

The present study measured physical properties of lentil and wild oat weed; mass of 1000 seeds, true density, porosity and coefficient of static friction. A gravity table separator was used to separate wild oat weeds from lentil seeds. The gravity table separator had five adjustable parameters; longitudinal slope, latitudinal slope, amplitude of oscillation, frequency of oscillation and air velocity. The effect of these parameters was investigated in order to maximize the separation of wild oat weed from lentil. Results of tests indicated that an increase in latitudinal slope of the table from 0.5° to 1° and longitudinal slope from 1.5° to 2° resulted in increased separation of wild oat weed from lentil. At longitudinal slope of 2°, latitudinal slope of 1°, amplitude of oscillation of 5 mm, frequency of oscillation of 400 cycles min-1, and air velocity of 5.7 m s-1, the separation percentage was 37%. Finally, after determination of the most suitable settings for amplitude of oscillation and air velocity; using the information on longitudinal slope, latitudinal slope and frequency of oscillation of the table was used to calculate mathematical relations of separation percentage of wild oat weed from lentil clumps using Datafit Software. [ABSTRACT FROM AUTHOR]