Researchers have developed a machine learning model using hyperspectral imaging to assess pre-harvest tomato quality. The study introduces a cost-effective non-destructive method to predict key quality parameters, including weight, firmness and lycopene -- a natural antioxidant -- content. The innovative approach enables farmers to monitor fruit development in real-time, optimizing harvest timing and improving crop quality. The research demonstrates a significant leap forward in precision agriculture and sustainable food production.
Harvest smarter, not harder
ToMAI-SENS shows images of the fruits at different bands, identifying the fruit and estimating its quality parameters.
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