David Helman, Eitan Fass and Yedidya Harris from the Hebrew University of Jerusalem;
Carmit Ziv from the Agricultural Research Organization, Volcani Center; Oren Glikman and Eldar Shlomi from Bar Ilan University.
About
Introducing ToMAI-SENS: a revolutionary AI-driven device for rapid, non-destructive assessment of key tomato quality indicators.
Background
Current methods for assessing tomato quality involve destructive lab testing, which is costly, time-consuming, and limited to small post-harvest samples. Farmers need a way to assess fruit quality pre-harvest to optimize growing conditions and management practices. Existing non-destructive technologies like hyperspectral cameras and NIR spectrometers are expensive and impractical for field use.
Our Innovation
The ToMAI-SENS device uses AI and advanced imaging to non-destructively assess multiple tomato quality metrics simultaneously.
- Rapidly measures fruit size, weight, firmness, TSS, pH, acidity, Vitamin-C, and lycopene content without damaging the fruit
- Analyzes many fruits from a single image, enabling high-throughput quality assessment
- Compact, low-cost design is practical for use by farmers, food companies, retailers, and consumers
- Enables pre-harvest quality tracking to optimize farming practices and ripeness at harvest
- Valuable research tool for studying environmental effects on tomato quality development
ToMAI-SENS can transform quality control across the tomato value chain, from farms to consumers. It enables data-driven farming, automated quality-based harvesting, and rapid quality assurance. As a scientific tool, it accelerates research into optimizing tomato quality.
Technology
The ToMAI-SENS device captures multi-spectral images of tomatoes and analyzes them using AI algorithms trained on extensive datasets correlating spectral signatures with lab-measured quality parameters. The AI model predicts multiple quality metrics from the spectral image.
Opportunity
The researchers are looking for an industry partner to collaborate and sponsor additional research & development. The partner will receive an option to license the intellectual property resulting from the project. Such cooperation could include other fruits/vegetables as well as seed quality control.
