Recent development in deep learning techniques have had a massive impact in the field of agricultural disease detection. The negative impact of pest and bacterial diseases to rice plants are well known, and for regions where rice is staple, this is issue carries a lot of weight. This work proposes a high accuracy, transfer learned model that can provide a mobile solution for farmers and agricultural organizations to detect rice leaf diseases at hand. This study also utilizes a generative adversarial network to balance the number of disease samples. We compare our model to other transfer learning architectures as well. The presented model tested on a GAN augmented dataset, achieves an average cross validation accuracy of 98.79% outperforming paradigm classification architectures. The model is also compared on 3 different datasets, without the GAN augmentation, establishing benchmark performance of 98.38% average accuracy.
The high mechanical strength of the lamellar Ti-6Al-4V extruded rod was characterized. The grain morphology of the extruded Ti-6Al-4V rod was investigated by optical microscopy. The microstructure of Ti-6Al-4V alloy consists of very fine α lamella of approximately 2.5 µm in thickness and β lamella with 1 µm thickness. Besides, a numerical model has been presented that reproduces the morphology of the lamellar colonies. In particular, the representative volume element with 512 elements, 4096 elements and, 32768 elements are studied with the C3D8 linear element. Experimental and numerical comparisons of the lamellae morphology have been presented.
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