Prediction of Physical Parameters of Pumpkin Seeds Using Neural Network

The design of the machines and equipment used in harvest and post-harvest processing should be compatible with the physical, mechanical and rheological characteristics of the fruits and vegetables. In machine design for agricultural products, several characteristics of relevant products and seeds should be known ahead. Designers can either measure all these design parameters one by one, or they may use intelligent systems to estimate such parameters. Neural networks (NNs) are new computational tools that provide a quick and accurate means of physical properties prediction of agricultural materials, and have been shown to perform well in comparison with traditional methods. In this research, some physical properties of pumpkin (Cucurbita pepo L.) seeds, including linear dimensions, volume, surface and projected area, geometric mean diameter and sphericity were calculated tridimensional in lab conditions. Then, prediction of these parameters was carried out using NNs. The research was divided into two parts; experimental investigation and simulation analysis with NNs. Back Propagation Neural Network (BPNN) and Radial Basis Neural Network (RBNN) structures were employed to estimate physical parameters of the pumpkin seeds. The Root Mean Squared Error (RMSE) was 0.6875 for BPNN and 0.0025 for RBNN structures. The RBNN structure was superior in prediction and could be used as an alternative approach to conventional methods

Estimation of the Weights of Almond Nuts Based on Physical Properties through Data Mining

Quality attributes are the major parameters designating market values of the agricultural goods and commodities. Several practices are applied to improve quality parameters of the fruits and vegetables. Such quality attributes should also be estimated through various approaches before to design of equipment and tools used in handling and processing of these goods and to design storage facilities. Data mining is a novel approach used to estimate various attributes or quality parameters of the fruits from previously measured attributes. Different algorithms embedded into data mining operations may yield quite accurate and reliable equations for estimation of quality attributes. Almond is a significant cash crop for growers. Since almond is quite tolerant to droughts and salinity, it is preferred in various parts of the country by producers. Weight is the primary quality parameter designating market value of the almonds. This study was conducted to estimate nut weights of seven different almond varieties and to develop an equation for the estimation of nut weights. Data mining approach was used to estimate nut weights from physical fruit quality attributes (kernel length, width, thickness, arithmetic mean diameter, geometric mean diameter, sphericity, surface area, volume, shape index and aspect ratio). Present findings revealed quite significant, accurate and practicable rules to estimate the nut weights of different almond varieties. It was concluded that data mining could be used as a reliable tool to estimate the nut weights of different almond varieties from the physical attributes of the fruits

Vibration analysis of drilling machine using proposed artificial neural network predictors

Small tolerances are very important factors for drilling machines. Due to the mechanical friction on their moving parts, it is necessary to predict vibration effects. This investigation is focused on design of robust neural network predictors for analyzing vibration effects on moving parts of drilling machines. The research is divided into two parts; the first part is experimental investigation, the second part is simulation analysis with neural networks. Therefore, a real time drilling machine is used for vibrations under working conditions. The measured real vibration parameters are analyzed with neural network. As a result, simulation approaches show that radial basis neural network has superior performance to adapt real time parameters of drilling machines