Click on “Download PDF” for the PDF version or on the title for the HTML version.

If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

Automated Identification And Counting Of Pests In The Paddy Fields Using Image Analysis

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Computers in Agriculture and Natural Resources, 4th World Congress Conference, Proceedings of the 24-26 July 2006 (Orlando, Florida USA) Publication Date 24 July 2006  701P0606.(doi:10.13031/2013.21969)
Authors:   Abdul Rashid Mohamed Shariff, Yap Ying Aik, Wong Tai Hong, Shattri Mansor, Radzali Mispan
Keywords:   Image analysis, pests in paddy field, automated identification, pest counting, segmentation, classification

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.

(Download PDF)    (Export to EndNotes)