![]() Spotted wing drosophila (SWD), an invasive insect originally from Asia, was first reported in the Northeast in 2011. This will make plantings less attractive to SWD, will reduce SWD activity, and will improve spray penetration and coverage. Pruning and training systems can help maintain an open canopy to increase sunlight and reduce humidity. Although we cannot change the weather, we can alter conditions in the planting to reduce the cool, dark, humid areas preferred by SWD. The research is expected to provide engineers a reference providing guidance upon deciding what pruning technique to use for a machine learning model to be deployed on an embedded device.Examine your caneberry (raspberries and blackberries) plantings for conditions that promote spotted wing drosophila (SWD) infestation and take steps to eliminate them. Lastly, the research will analyze the trade-offs between energy consumption, model size and model accuracy for each of the assessed pruning algorithms applied to one of the most commonly used neural network architectures, MobileNetV2, on a Raspberry Pi 4B prototyping board. Furthermore, this research will assess in what way these four neural network pruning techniques affect the total energy consumption during model inference on a Raspberry Pi 4B board, applied to MobileNetV2, a machine learning model architecture optimized for image classification on embedded devices. This research provides an analysis on four current pruning techniques that theoretically efficiently reduce the machine learning model size, where efficiency is defined by the relation between the compression of the model and the accuracy of the model. ![]() Pruning of neural networks is a technique often used to reduce the size of a machine learning model, as well as to reduce the computation cost for model inference. Analysis of pruning methods for compact neural networks on embedded devices. ![]()
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