Analysis of pesticide application practices using an intelligent Agriculture Decision Support System (ADSS)

Abdullah, A.; Barnawi, A.; Hussain, A.

Abstract

Pesticides are used for controlling pests, but at the same time they have impacts on the environment as well as the product itself. Although cotton covers 2.5% of the world’s cultivated land yet uses 16% of the world’s insecticides, more than any other single major crop [1]. Pakistan is the world’s fourth largest cotton producer and a major pesticide consumer. Numerous state run organizations have been monitoring the cotton crop for decades through pest-scouting, agriculture surveys and meteorological data-gatherings. This non-digitized, dirty and non-standardized data is of little use for strategic analysis and decision support. An advanced intelligent Agriculture Decision Support System (ADSS) is employed in an attempt to harness the semantic power of that data, by closely connecting visualization and data mining to each other in order to better realize the cognitive aspects of data mining. In this paper, we discuss the critical issue of handling data anomalies of pest scouting data for the six year period: 2001-2006. Using the ADSS it was found that the pesticides were not sprayed based on the pests crossing the critical population threshold, but were instead based on centuries old traditional agricultural significance of the weekday (Monday), thus resulting in non optimized pesticide usage, that can potentially reduce yield.

Citation

Abdullah, A., Barnawi, A., & Hussain, A. (2012, July). Analysis of pesticide application practices using an intelligent Agriculture Decision Support System (ADSS). Presented at 5th International Conference, BICS 2012, Shenyang, China

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 5th International Conference, BICS 2012
• Start Date: Jul 11, 2012
• End Date: Jul 14, 2012
• Publication Date: 2012
• Deposit Date: Oct 14, 2019
• Publisher: Springer
• Pages: 382-391
• Series Title: Lecture Notes in Computer Science
• Series Number: 7366
• Series ISSN: 0302-9743
• Book Title: Advances in Brain Inspired Cognitive Systems
• ISBN: 978-3-642-31560-2
• DOI: https://doi.org/10.1007/978-3-642-31561-9_43
• Public URL: http://researchrepository.napier.ac.uk/Output/1793200