A research team led by Dr. David Helman from the Faculty of Agriculture, Food and Environment at the Hebrew University of Jerusalem has developed a novel machine learning model employing hyperspectral imaging to assess the quality of tomatoes before harvest. Hyperspectral images of specific ranges of light wavelengths, known as spectral bands, are used to study objects’ properties based on how they reflect light. A research team led by Dr. David Helman from the Faculty of Agriculture, Food and Environment at the Hebrew University of Jerusalem has developed a novel machine learning model employing hyperspectral imaging to assess the quality of tomatoes before harvest. Hyperspectral images of specific ranges of light wavelengths, known as spectral bands, are used to study objects’ properties based on how they reflect light. Molecular & Computational biology Agriculture Phys.org – latest science and technology news stories
Harvest smarter, not harder: Machine learning meets tomato farming
