Physical and mechanical properties of hemp seed

The current study was conducted to investigate the effect of moisture content on the post-harvest physical and mechanical properties of hemp seed in the range of 5.39 to 27.12% d.b. Results showed that the effect of moisture content on the most physical properties of the grain was significant (P<0.05). The results of mechanical tests demonstrated that the effect of loading rate on the mechanical properties of hemp seed was not significant. However, the moisture content effect on rupture force and energy was significant (P<0.01).The lowest value of rupture force was obtained at the highest loading rate (3mm min-1)and in the moisture content of 27.12% d.b. Moreover, the interaction effects of loading rate and moisture content on the rupture force and energy of hemp seed were significant (P<0.05)

Deep learning-based precision agriculture through weed recognition in sugar beet fields

Weeds are among the major factors adversely affecting crop yield. Therefore, weed control with minimal environmental damage is a global concern. Traditional weed control methods are not cost-effective. Hence, precision agriculture proposes variable flow herbicide technology through regional weed management to distinguish weed and crop. In the present study, we employed the U-Net architecture, as a deep encoder-decoder convolutional neural network (CNN) for pixel-wise semantic segmentation of sugar beet, weed, and soil. We trained the U-Net architecture with ResNet50 as the encoder block using 1385 RGB images collected under different conditions and various heights. We utilized the combination of the dice and focal losses as a custom linear loss function to overcome imbalanced data and small area segmentation challenges. The structure of the dataset for the training process and using the custom loss function led to a model with the accuracy and intersection over union (IoU) score of 0.9606 and 0.8423, respectively. The results showed that using the image dataset with proper distribution and custom loss function can improve segmentation accuracy, especially in small regions. Furthermore, in an autonomous weed control robot, CNN-based automatic weed detection can be integrated into selective herbicide applications

D:Int Agrophysics -3ShahbaziShahbazi.vp

A b s t r a c t. The objective of this research was to determine the effects of moisture content and stalk region on some physical and mechanical properties of safflower stalks. The experiments were conducted at four moisture contents of 9.98, 17.85, 26.37 and 38.75% w.b. and at the bottom, middle and top regions of stalk. The values of the stalk physical properties increased with increasing moisture content. Their values also increased towards the bottom region. The bending stress and Young modulus in bending decreased with increase in the moisture content and increased towards the top regions. The average bending stress values and Young modulus in bending varied between 47.71 and 25.9 MPa and between 2.52 and 1.28 GPa, respectively. The shearing stress and the specific shearing energy increased with increasing moisture content. Their values also increased towards the bottom region of the stalk. The maximum shear stress and specific shearing energy were found to be 7.66 MPa and 33.05 mJ mm -2 , respectively, and both occurred at the bottom region with the moisture content of 38.75% w.b. K e y w o r d s: safflower stalk, bending stress, Young modulus, shearing stress, specific shearing energ