Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Cluster analysis & Control theory. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

Automated tomato leaf disease classification using transfer learning-based deep convolution neural network

Abstract: Early and accurate detection of plant diseases is necessary to maximize crop yield. The artificial intelligence based deep learning method plays a vital role in the detection of the diseases using a huge volume of plant leaves images. However, to detect disease with small datasets is a challenging task using deep learning methods. Transfer learning is one of the popular deep learning methods used to accurately detect plant disease with minimal plant image data. In this paper, the transfer learning-based deep convolution neural network model to identify tomato leaf disease has proposed. The model performs detection of disease using real-time images and stored tomato plant images. Furthermore, the performance of the proposed model is evaluated using adaptive moment estimation (Adam), stochastic gradient descent (SGD), and RMSprop optimizers. The experimental result demonstrates that the proposed model using the transfer learning approach is effective in automated tomato leaf disease classification. The Adam optimizer achieves better accuracy compared with SGD and RMSprop optimizers.

Development of extruded snacks using soy, sorghum, millet and rice blend – A response surface methodology approach

Abstract: Summary Response surface methodology (RSM) based on a five-level-three-factor central composite rotatable design (CCRD) was employed for optimisation of formulation for production of a soy-fortified millet-based extruded snack. Effects of amount of ingredients such as ragi (40–50%), sorghum (10–20%) and soy (5–15%) on the physical properties like bulk density, expansion ratio, water absorption index and water solubility index of snacks were investigated. Significant regression models that explained the effects of different percentages of ragi, sorghum and soy on all response variables were determined. The coefficients of determination, R2, of all the response variables were higher than 0.90. Based on the given criteria for optimisation, the basic formulation for production of millet-based extruded snack with desired sensory quality was obtained by incorporating with 42.03% ragi, 14.95% sorghum, 12.97% soy and 30% rice.

Prediction of cutting tool wear during milling process using artificial intelligence techniques

Abstract: An efficient tool condition monitoring system was designed for keyway milling of 7075-T6 hybrid aluminium alloy composite with resultant machining force and sound acquired while the milling process...