New AI Model That Determines Whether Tomatoes are Ripe for Picking Developed at Hebrew University - SURSE SI RESURSE

New AI Model That Determines Whether Tomatoes are Ripe for Picking Developed at Hebrew University

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Researchers at the Hebrew University of Jerusalem have created a novel artificial intelligence (AI) model that uses hyperspectral imaging to evaluate the quality of tomatoes before harvest. This model could eventually be utilized in a portable, low-cost gadget.

A cost-effective, nondestructive method to predict important quality parameters, such as weight, firmness, and lycopene (a natural antioxidant) content, allows farmers to track fruit development in real-time, improving crop quality and harvest timing, according to a study published in Computers and Electronics in Agriculture. The study shows that sustainable food production and precision agriculture have advanced significantly.

“Our research aims to bridge the gap between advanced imaging technology, AI, and practical agricultural applications. This work has the potential to revolutionize quality monitoring not only in tomatoes but also in other crops. Our next step is to build a low-cost device (ToMAI-SENS) based on our model that will be used across the fruit value chain, from farms to consumers,” Dr. David Helman from the Hebrew University Robert H. Smith Faculty of Agriculture, Food, and Environment, mentioned.

Hyperspectral images of light wavelengths, known as spectral bands, are used to study objects’ properties based on how they reflect light. This approach focused on fruit addresses challenges associated with traditional methods, offering a faster, non-destructive, and cost-effective alternative.
The study, conducted in collaboration with researchers from Bar-Ilan University and the Volcani Center, used a handheld hyperspectral camera to collect data from 567 tomato fruits across five cultivars. Machine learning algorithms, including Random Forest and Artificial Neural Networks, were employed to predict seven critical quality parameters: weight, firmness, total soluble solids (TSS), citric acid, ascorbic acid, lycopene, and pH. The models demonstrated high accuracy, with the Random Forest algorithm achieving an R² of 0.94 for weight and 0.89 for firmness, among others.

Key findings of the study include:
Efficiency in Band Selection: The model effectively predicts quality parameters using only five spectral bands, paving the way for developing affordable, portable devices;
Broader Applicability: Tested across diverse cultivars and growing conditions, the model exhibits robustness and scalability;
Pre-Harvest Benefits: Farmers can now monitor fruit quality during ripening stages, optimizing harvest timing and improving produce quality.

The study highlights the potential integration of this technology into agricultural practices, from smart harvesting systems to consumer tools for evaluating produce quality in supermarkets.

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