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In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of maize crop has been proposed. The images were captured from experimental fields of ICAR-IIMR, Ludhiana, India, targeted to three important diseases viz. Maydis Leaf Blight, Turcicum Leaf Blight and Banded Leaf and Sheath Blight in a non-destructive manner with varied backgrounds using digital cameras and smartphones. In order to solve the problem of class imbalance, artificial images were generated by rotation enhancement and brightness enhancement methods. In this study, three different architectures based on the framework of ‘Inception-v3’ network were trained with the collected diseased images of maize using baseline training approach. The best-performed model achieved an overall classification accuracy of 95.99% with average recall of 95.96% on the separate test dataset. Furthermore, we compared the performance of the best-performing model with some pre-trained state-of-the-art models and presented the comparative results in this manuscript. The results reported that best-performing model performed quite better than the pre-trained models. This demonstrates the applicability of baseline training approach of the proposed model for better feature extraction and learning. Overall performance analysis suggested that the best-performed model is efficient in recognizing diseases of maize from in-field images even with varied backgrounds.

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In this paper, an analytical threshold voltage model is proposed for a triple-material cylindrical gate-all-around MOSFET considering parabolic approximation of the potential along the radial axis. The center (axial) and the surface potential models are obtained by solving the 2-D Poisson's equation in the cylindrical coordinate system. This paper refutes the estimation of the natural length using surface potential as in previous work and proposes the use of center-potential-based natural length formulation for an accurate subthreshold analysis. The developed center potential model is used further to formulate the threshold voltage model and also extract drain-induced barrier lowering (DIBL) from the same. The effects of the device parameters like the cylinder diameter, oxide thickness, gate length ratio, etc., on the threshold voltage and DIBL are also studied in this paper. The model is verified by the simulations obtained from 3D numerical device simulator Sentaurus from Synopsys. Index Terms-Center potential, drain induced barrier lowering (DIBL), hot carrier effect (HCE), short-channel effects (SCEs).

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