A novel hybrid cnn-rnn model for sugarcane disease identification in agricultural fields

T. Angamuthu — 2025-01-20

The world's most important crop is sugarcane, which is the main source of both sugar and ethanol. The existence of sugarcane diseases, which result in the removal of afflicted crops, is a persistent problem in the sugar business. Small-scale farmers risk suffering large financial losses if these diseases are not identified and treated early. The growing incidence of illnesses and farmers' inadequate understanding of disease diagnosis and identification were the focus of this investigation. The application of deep learning methods, including machine learning and computer vision, showed promise. A deep-learning model was trained and evaluated using a dataset of 13,842 photos of sugarcane that included both diseased and healthy leaves, and it achieved an accuracy rate. The research was ultimately submitted to recurrent neural networks (RNN), conventional neural networks (CNN), and other similar models for additional evaluation after the trained model effectively achieved its goals.

Joule heating and dielectric gradient effects on colloidal particle electrophoresis

Dr. Ujjwal Kanti Ghoshal — 2025-06-16

The following paper discusses the combined action of dielectric gradients and Joule heating effects to the electrophoresis of colloids in micro fluidics. Study introduces a new and useful CFD model that represents the combination of the Joule heating and dielectrophoresis phenomenon to expose the opposite particle dynamics in microchannels when the electric field is applied. The influence of the temperature gradients on the equivalent representation of the electrokinetic forces as well as the particles mobility is included by taking into consideration the temperature-dependent fluid viscosity and dielectric permittivity. These outcomes prove the fact that Joule heating can substantially change the particle velocity by changing the physical parameters of the fluid used, whereas dielectric gradients also bring new dielectrophoretic forces which can impact on the movement of the particles. This is a combined way of outlook to optimize the microfluidic platforms of biomolecule separation and nanoparticle control application. The article is particularly significant when it says that the development of advanced microfluidic devices should not ignore the effect of thermal and electrokinetics.

Journal of Energy Engineering and Thermodynamics

A novel hybrid cnn-rnn model for sugarcane disease identification in agricultural fields

Joule heating and dielectric gradient effects on colloidal particle electrophoresis