School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia.
Sensors (Basel). 2012 Oct 22;12(10):14179-95. doi: 10.3390/s121014179.
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.
油棕鲜果串（FFB）在收获期间的成熟度分类对于确保在最佳产油阶段进行收获非常重要。本文介绍了颜色视觉在油棕 FFB 自动成熟度分类中的应用。使用数字图像处理技术采集并分析了 DxP Yangambi 型油棕 FFB 的图像。然后从这些图像中提取颜色特征，并将其用作人工神经网络（ANN）学习的输入。使用两种方法研究了 ANN 对油棕 FFB 成熟度分类的性能：使用全特征训练 ANN 和使用基于主成分分析（PCA）数据降维技术的降维特征训练 ANN。结果表明，与在 ANN 中使用全特征相比，使用经降维特征训练的 ANN 可以将分类准确率提高 1.66%，并且在开发油棕 FFB 的自动成熟度分类器方面更有效。开发的成熟度分类器可以作为传感器，用于确定正确的油棕 FFB 成熟度类别。
