M. Eskandari; A. Hosainpour
Abstract
Many research projects have been conducted about using ultrasonic sensors to estimate canopy volume. This study investigates using software applications such as artificial neural network (ANN) to improve the estimation of canopy volume by using ultrasonic sensors. A special experimental system was built. ...
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Many research projects have been conducted about using ultrasonic sensors to estimate canopy volume. This study investigates using software applications such as artificial neural network (ANN) to improve the estimation of canopy volume by using ultrasonic sensors. A special experimental system was built. The system had three ultrasonic sensors mounted vertically on a wooden pole with an equal distance of 0.6 m. As the wooden pole moves with a constant speed, the ultrasonic sensors measure the thickness of tree canopy with sampling rate of 4 Hz. Experiments were conducted on 5 samples of Benjamin tree at three speed levels of 35,45 and 55 cm s-1 in three replications. The real volume of trees was measured manually with rectangular elements method. After a full passing of ultrasonic sensors, potential features such as canopy diameter, average width of tree canopy and height of the tree canopy were considered as the inputs to the ANN model and the manually volume as the output of the model. Optimal ANN model was selected based on mean square error and correlation coefficient. The results showed that 13-16-7-1 was the optimal neuron numbers in ANN topology for estimating canopy volume.
S. Kordi; A. Fadavi; M. Eskandari; M. Barari; M. Rafiee; A. Ashraf Mehrabi
Abstract
Mechanical properties of grain are influenced by various factors including soil nutrients and grain moisture content at harvest time. In order to reduce mechanical losses, the design of different processing operations should be performed based on the knowledge of factors influencing the mechanical properties. ...
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Mechanical properties of grain are influenced by various factors including soil nutrients and grain moisture content at harvest time. In order to reduce mechanical losses, the design of different processing operations should be performed based on the knowledge of factors influencing the mechanical properties. The effects of urea fertilization methods and grain moisture content at harvest time on mechanical properties of dried corn were investigated in a field experiment as a strip split plot with four replications based on randomized complete block design at Khorram Abad Agricultural Research Station in 2010. The investigated factors were urea fertilization methods (urea foliar application and urea side-dress application), grain moisture content at harvest time (20, 30 and 40%) and four corn hybrids (NS 640, Konsur 580, Jeta 600 and control SC 704). The moisture content of dried grains due to different absorption property of the treatments was about 7±1 percent. The results showed that the interaction of fertilization methods and hybrid was significant (P < 0.05) for grain toughness. However, the grain moisture content at harvest time had significant effect on all studied traits except on grain firmness. The highest maximum fracture force, displacement at the maximum rupture force, energy consumption at maximum force point, specific deformation, rupture power and toughness were obtained at 20% grain’s moisture content Also, the results showed that NS hybrid had the highest maximum rupture force (219 N), displacement at the maximum fracture force (0.37 mm), energy consumption at maximum force (42.51 mj), rupture power (3.89 . 10-3W) and toughness (0.33 mj mm-3).
