Advances in Non Destructive Apple Quality Prediction

The search for quality in apple production is perpetual. The research paper by Biegert et al., titled “Modelling Soluble Solids Content Accumulation in ‘Braeburn’ Apples,” offers a pivotal step forward in this search. Published in the journal Plants, this study leverages optical sensor data to transform our understanding of apple sweetness—quantified by the Soluble Solids… Continue reading…

Sweetness Predicted: AI Revolutionizes Citrus Fruit Quality Assessment

In her groundbreaking research at NUST, Dr. Ayesha Zeb sought to simplify the assessment of citrus fruit sweetness. The Produce Quality Meter was revolutionary – quickly gathering data from various citrus fruits. Paired with an AI model, the precise data collected led to a breakthrough: an accurate prediction of fruit sweetness. Dr. Zeb’s journey showcases… Continue reading…

How AI Analytics is Transforming Fruit Quality Control and Monitoring

Food production systems’ enormous volumes of data must be precisely analyzed in real-time. AI analytics uses one or more AI tools. It extracts valuable information from big data on food quality, providing answers in an easy-to-understand manner during onsite food analysis. Three AI methods- machine/computer vision, machine learning, and deep learning are used for AI… Continue reading…