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Investigation on the environmental causes of tomato fruit cracking and its propagation prediction in greenhouse.

Ying LiuJun YangZhiguo LiFideline Tchuenbou-MagaiaYande Liu
Published in: Journal of texture studies (2024)
In this study, Provence tomato variety was chosen for investigating the environmental causes of tomato fruit cracking, cracks characteristics, and their propagation prediction in a greenhouse. Fruit bagging approach was used to alter the temperature and humidity and to create a microclimate around the fruit to induce fruit cracking for testing. Results showed that the fruit cracking rate increased when the environment temperature exceeded 30°C, and the difference between the highest and lowest temperature values in a day was greater than 20°C. The cracking rate was aggravated when the difference between the highest and lowest humidity values in a day was less than 20%. The proportions of top cracking, longitudinal cracking, ring cracking, radial cracking, and combined cracking were 5.4%, 16.1%, 28.3%, 26.8%, and 32.1%, respectively. The fruit shoulder was the most susceptible region to crack, followed by fruit belly and top regions, whereas longer cracks were observed in the fruit belly region indicating a higher propensity to crack propagation in that region. Finally, the measured data were used to validate an extended finite element method developed to effectively predict cracking susceptibility and propagation in tomato fruit with a relative error of 4.68%.
Keyphrases
  • machine learning
  • risk assessment
  • climate change
  • deep learning
  • artificial intelligence
  • finite element