Moisture Content

Moisture content determination is crucial across diverse fields, from industrial processes to environmental monitoring, with primary objectives focused on accurate, rapid, and cost-effective measurement. Current research heavily utilizes machine learning, employing various architectures like artificial neural networks, random forests, support vector machines, and recurrent neural networks, to predict moisture content from diverse data sources including images, sensor readings, and weather data. These advancements offer significant improvements over traditional methods, enabling real-time monitoring and improved process control in applications ranging from wood processing and zinc production to wildfire management and agriculture.