Tuberculosis (TB) is the second biggest killer disease after HIV. Therefore, early detection is vital to
prevent its outbreak. This paper looked at an automated TB bacteria counting using Image Processing technique and Matlab Graphical User Interface (GUI) for analysing the results. The image processing algorithms used in this project involved Image Acquisition, Image Pre-processing and Image Segmentation. In order to separate any overlap between the TB bacteria, Watershed Segmentation techniques was proposed and implemented. There are two techniques in Watershed Segmentation which is Watershed Distance Transform Segmentation and Marker Based Watershed Segmentation. Marker Based Watershed Segmentation had 81.08 % accuracy compared with Distance Transform with an accuracy of 59.06%. These accuracies were benchmarked with manual inspection. It was observed that Distance Transform Watershed Segmentation has disadvantages over segmentation and produce inaccurate results. Automatic counting of TB bacteria algorithms have also been proven to be less time consuming, contains less human error and consumes less man-power.
Herbs have unique characteristics such as colour, texture and odour. In general, herb identification is
through organoleptic methods and is heavily dependent on botanists. It is becoming more difficult to
identify different herb species in the same family based only on their aroma . It is because of their similar
physical appearance and smell. Artificial technology, unlike humans, is thought to have the capacity to
identify different species with precision. An instrument used to identify aroma is the electronic nose.
It is used in many sector including agriculture. The electronic nose in this project was to identify the
odour of 12 species such as lauraceae, myrtaceae and zingiberaceae families. The output captured by the
electronic nose gas sensors were classified using two types of artificial intelligent techniques: Artificial
Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). From the result, ANFIS
has 94.8% accuracy compared with ANN at 91.7%.