Abstract

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.

© 2016 Optical Society of America

Nondestructive nitrogen content estimation in tomato plant leaves by Vis-NIR hyperspectral imaging and regression data models

Razieh Pourdarbani, Sajad Sabzi, Mohammad H. Rohban, Ginés García-Mateos, and Juan I. Arribas
Appl. Opt. 60(30) 9560-9569 (2021)

Detection of Huanglongbing in Florida using fluorescence imaging spectroscopy and machine-learning methods

Caio Bruno Wetterich, Ruan Felipe de Oliveira Neves, José Belasque, Reza Ehsani, and Luis Gustavo Marcassa
Appl. Opt. 56(1) 15-23 (2017)

Recognition of wall materials through active thermography coupled with numerical simulations

Francesca Pietrarca, Mauro Mameli, Sauro Filippeschi, and Fabio Fantozzi
Appl. Opt. 55(25) 6821-6828 (2016)

Colorimetric analysis of saliva–alcohol test strips by smartphone-based instruments using machine-learning algorithms

Huisung Kim, Olumide Awofeso, SoMi Choi, Youngkee Jung, and Euiwon Bae
Appl. Opt. 56(1) 84-92 (2017)

Phase discontinuity predictions using a machine-learning trained kernel

Firas Sawaf and Roger M. Groves
Appl. Opt. 53(24) 5439-5447 (2014)