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Thermography and machine learning techniques for tomato freshness prediction

机译：番茄新鲜度预测的热成像和机器学习技术

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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. (C) 2016 Optical Society of America
机译：美国和中国是世界领先的番茄生产国。西红柿在美国每年的农业销售额中占超过20亿美元。西红柿也位居世界第八位最有价值的农产品，每年价值580亿美元，并且质量倍受赞誉。已经开发出无损检测技术，例如光学检测和近红外光谱分析，以评估番茄的新鲜度（在USDA中也被称为等级）。但是，确定西红柿的新鲜度仍然是一个未解决的问题。这项研究（1）说明了热成像为什么能够揭示西红柿内部状态的理论原理，并且（2）研究了机器学习技术的应用-人工神经网络（ANN）和支持向量机（SVM）-结合瞬时步进加热和热像仪进行新鲜度预测，指的是西红柿变质的时间。在加热40 s，然后冷却160 s期间，以1 Hz的采样频率捕获红外图像。绘制所获取图像的温度。新鲜和不新鲜（三天内腐烂）大小和形状均一的西红柿之间温差较高的区域用作ANN和SVM模型的输入节点。使用加热和冷却数据建立的ANN模型相对最佳。总体回归系数为0.99。这些结果表明，与SVM模型相比，红外热成像和ANN建模方法的组合可用于预测番茄的新鲜度。 （C）2016美国眼镜学会

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