A digital image analysis (DIA) algorithm based on fuzzy logic was developed using digital
values of color, shapes and texture features to identify pests from images captured from a paddy field.
Images were acquired under natural lighting using digital cameras and analog camera. Six pests species
commonly found in the study site, Sawah Sempadan, Malaysia paddy field, namely rice leaffolder
(Marasmia Patnalis), rice skipper (Pelopidas Mathias), rice leaf-butterfly (Melanitis Leda), Malayan black
rice-bug (Scotinophara Coarctata), seedbugs (Pachybrachius Pallicorais) and rice butterfly (Abisara Saturate
Kausambioides) were selected for this study. Image processing software, eCognition was used for
discriminating analysis. Protocol that recorded all the procedure involved in the analysis process was built to
automatically identify and count the total number of pests in the image. A satisfactory result of classification
and counting of the pest was obtained from the image analysis. 100% accuracy was obtained for pest
extraction and classification. There is 33.33% accuracy of the automation process in identification and
counting of the pests obtained from the protocol built without the need of refinement. However an accuracy
of 100% for automation process was obtained in identification and counting of the pests after the refinement
of the protocol. The precision analysis system was capable of detecting pests from paddy plants images
quickly.