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Subject: Others × Author: Kamalpreet Kaur × Start date: 1960 × Type: Thesis × Thesis Type: M.Tech. ×
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    ThesisItemOpen Access
    Software development for determining quality and maturity levels of tomato using image processing techniques
    (Punjab Agricultural University, Ludhiana, 2017) Kamalpreet Kaur; Gupta, O.P.
    Agriculture contributes a lot in the economic development of India. Maturity checking has become mandatory for the food industries as well as for the farmers so as to ensure that the fruits and vegetables are not diseased and are ripe. However, manual inspection leads to human error, unripe fruits and vegetables may decrease the production. Thus, this study proposes a Tomato Classification system for determining maturity stages of tomato (Green, Breaker, Pink, Light-Red and Red- Mature) through Machine Learning using Image Processing approach. Design and development of software has been implemented using Pycharm as an IDE and Python as a programming language. The method consists of image collection, preparing database and training seven different classifiers on 80% of the total data for evaluating the maturity stages of tomato using the surface color as an attribute. Rest 20% of the total data is used for the testing purpose. The results are obtained in the form of Learning Curve, Confusion Matrix and Accuracy Score. The Graphical User Interface (GUI) for Tomato Classification by using Python is achieved. It is observed that out of seven classifiers, Random Forest is successful with 92.49% accuracy in evaluating the maturity category of tomato. It is concluded from the results that the performance of the classifier depends on the size and kind of features extracted from the data set.