A Computer Vision Approach Towards Maturity Stage Classification of Tomatoes Using Second Order Wavelet Features
Author:
Mukherjee Gunjan,
Chatterjee ArpitamORCID,
Tudu Bipan
Publisher
Springer Singapore
Reference21 articles.
1. D. Bhowmik, K.P.S. Kumar, S. Paswan, S. Srivastava, Tomato-a natural medicine and its health benefits. J. Pharmacogn. Phytochem. 1, 33–43 (2012)
2. A.H. Gómez, G. Hu, J. Wang, G.A. Pereira, Evaluation of tomato maturity by electronic nose. Comput. Electron. Agric. 54(1), 44–52 (2006)
3. L.L. Zhang, M.J. McCarthy, Measurement and evaluation of tomato maturity using magnetic resonance imaging. Postharvest Biol. Technol. 67, 37–43 (2012)
4. P. Sirisomboon, M. Tanaka, T. Kojima, P. Williams, Nondestructive estimation of maturity and textural properties on tomato ‘Momotaro’ by near infrared spectroscopy. J. Food Eng. 112(3), 218–226 (2012)
5. R.R. Mhaski, P.B. Chopade, M.P. Dale, Determination of ripeness and grading of tomato using image analysis on Raspberry Pi, in 2015 Communication, Control and Intelligent Systems (CCIS) (IEEE, 2015), pp. 214–220