An intelligent sensing framework using Machine Learning (ML) and Deep Learning (DL) architectures to precisely quantify dielectrophoretic force invoked on microparticles in a textile electrode-based DEP sensing device is reported. The prediction accuracy and generalization ability of the framework was validated using experimental results. Images of pearl chain alignment at varying input voltages were used to build deep regression models using modified ML and CNN architectures that can correlate pearl chain alignment patterns of Saccharomyces cerevisiae(yeast) cells and polystyrene microbeads to DEP force. Various ML models such as K-Nearest Neighbor, Support Vector Machine, Random Forest, Neural Networks, and Linear Regression along with DL models such as Convolutional Neural Network (CNN) architectures of AlexNet, ResNet-50, MobileNetV2, and GoogLeNet have been analyzed in order to build an effective regression framework to estimate the force induced on yeast cells and microbeads. The efficiencies of the models were evaluated using Mean Absolute Error, Mean Absolute Relative, Mean Squared Error, R-squared, and Root Mean Square Error (RMSE) as evaluation metrics. ResNet-50 with RMSPROP gave the best performance, with a validation RMSE of 0.0918 on yeast cells while AlexNet with ADAM optimizer gave the best performance, with a validation RMSE of 0.1745 on microbeads. This provides a baseline for further studies in the application of deep learning in DEP aided Lab-on-Chip devices.
We report a smart irrigation system that allows selective irrigation of localized dry spots in an agricultural field. The proposed irrigation system uses a quadcopter drone equipped with a Thermal Infrared (TIR) camera and a GPS module to generate georeferenced thermal images that indicate the area and location of the dry spots in a survey area. Drones navigate and acquire aerial thermal images, which are then processed by an onboard edge intelligence module along with flight data (GPS coordinates, altitude, and drone direction). Smart sprinklers deployed on the field are able to wirelessly receive the coordinates of dry spots so they can be irrigated selectively. A terrestrial edge unit generates an irrigation pattern for the smart sprinklers using a pre-trained machine learning (ML) model to generate an irrigation pattern by varying the head rotation angle (θ) and the water flow control valve rotation angle(∅) of the smart sprinkler.
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