African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6800

Full Length Research Paper

Thai botanical herbs and its characteristics: Using artificial neural network

 P. Pattanasethanon1* and B. Attachoo2
  1Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, 10520, Thailand. 2Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, 10520, Thailand.
Email: [email protected]

  •  Accepted: 21 December 2011
  •  Published: 31 January 2012



This paper proposed an explicit supervising technique with artificial neural network (ANN) which is associated with typical Content-based image retrieval (CBIR) systems. The main purpose of implementing this CBIR system with a unique supervising technique is for the system to apprehend various parts of Thai Botanical Herbs in the retrieving processing. This essential step prevents users from herbal usage misconception, and the lack of proper knowledge may lead to faulty exploitation of botanical herbs in Thailand. The input image or query image for our CBIR system is an image data of botanical herb features. The implementation of ANN supervision provides four kinds of training to the retrieval system which consists of 35, 50, 70, and 100% image section training of the original image. The weight, color histogram and edge pattern are set manually in order for these data to be supervised with back propagation technique. The retrieving results manifested a 91% precision and 50% recall with our implemented technique for 50% training. With all the assumptions and procedures, the implemented herbal CBIR system can facilitate several botanical herb users in terms of proper knowledge and misconception.


Key words: Artificial neural network, back propagation, Thai herb image, supervised retrieval.