Abstract
Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category.
Highlights
Quality is the most important factor for agricultural and food products because high quality products are significant for success in today’s highly competitive market
The combination that implemented the logistic sigmoid function for both input and output neurons managed to classify the ripeness of oil palm fresh fruit bunches (FFBs) when all 59 features were used
The results have revealed that different numbers of output neurons with different ranges of output values contribute to the mapping complexity of an Artificial Neural Network (ANN), and affecting its performance
Summary
Quality is the most important factor for agricultural and food products because high quality products are significant for success in today’s highly competitive market. The quality of a product—especially fruits—is often classified by their texture, shape and color. These features are usually observed using human’s vision in determining the ripeness of fruits. The method of human grading is tedious and may be erroneous. This leads to extensive researches on automated fruit grading using sensor-based technologies such as image sensors. It is believed that the use of non-contact image sensing technology combined with robust computing and decision processes provides automated, non-destructive and cost-effective method to determine the quality of agricultural and food products [1]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
