Thermography and machine learning techniques for tomato freshness prediction

The United States and China are the world's leading tomato producers. Tomatoes account for over $2 billion annually in farm sales in the U.S. Tomatoes also rank as the world's 8th most valuable agricultural product, valued at $58 billion dollars annually, and quality is highly prized. Nondestructive technologies, such as optical inspection and near-infrared spectrum analysis, have been developed to estimate tomato freshness (also known as grades in USDA parlance). However, determining the freshness of tomatoes is still an open problem. This research (1) illustrates the principle of theory on why thermography might be able to reveal the internal state of the tomatoes and (2) investigates the application of machine learning techniques-artificial neural networks (ANNs) and support vector machines (SVMs)-in combination with transient step heating, and thermography for freshness prediction, which refers to how soon the tomatoes will decay. Infrared images were captured at a sampling frequency of 1 Hz during 40 s of heating followed by 160 s of cooling. The temperatures of the acquired images were plotted. Regions with higher temperature differences between fresh and less fresh (rotten within three days) tomatoes of approximately uniform size and shape were used as the input nodes for ANN and SVM models. The ANN model built using heating and cooling data was relatively optimal. The overall regression coefficient was 0.99. These results suggest that a combination of infrared thermal imaging and ANN modeling methods can be used to predict tomato freshness with higher accuracy than SVM models.