Harvest smarter, not harder: machine learning meets tomato farming
Peer-Reviewed Publication
Updates every hour. Last Updated: 12-May-2025 01:10 ET (12-May-2025 05:10 GMT/UTC)
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. This 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.
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