Weed Detection over Between-Row of Sugarcane Fields Using Machine Vision with Shadow Robustness Technique for Variable Rate Herbicide Applicator
A. Muangkasem, S. Thainimit, R. Keinprasit and T. Isshiki
DOI : 10.3844/erjsp.2010.141.145
Energy Research Journal
Volume 1, Issue 2
Problem statement: Uniformly herbicide rate is used as a conventional practice in Thailand for controlling weeds in sugarcane fields. Since weeds usually grow in certain areas with non-uniformly distribution, uniform herbicide rate approach is not suitable and non-sustainable agricultural technique both in terms of economic an environmental aspect. To address these issues, Variable Herbicide Rate (VHR) was introduced. The VHR composes of two main components, which are weed monitoring and real-time spraying. Approach: This study investigated with a development of a fast and robust weed monitoring system for VHR using over between-row of sugarcane fields. The proposed method was designed to work under natural illumination condition. The near-ground images were captured using a typical web camera without any assistant light diffuser. The proposed weed monitoring is a machine vision based approach. The Non Green Subtraction (NGS) technique was proposed for soil background segmentation. Results: The proposed technique exploited variations among three triplets, which are red, green and blue under bright and dull lighting condition to achieve better background segmentation results. The non-background pixels were then classified into weeds and non-weeds using the Offset Excessive Green (OEG) technique. Conclusion: From our experimental results, the proposed method is robust under illumination variations such as in sunny and after raining day conditions. Weeds under different lighting conditions are reliably detects. The approach is less sensitive to chosen threshold value comparing to the OEG technique. The proposed method is very effective especially in spare weeds condition. It is fast, suitable for using in real-time application.
© 2010 A. Muangkasem, S. Thainimit, R. Keinprasit and T. Isshiki. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.