Development of an Approach for Early Weed Detection with UAV Imagery

Abstract

Curating a precise decision-based classifier algorithm to automate target detection based on feature extraction(s) in UAV imagery can assist in various scientific and practical applications. Localization and detection of weed in sugarcane field is a critical classification problem. Vegetative stage of weed, especially, when it is growing and is at its earliest phase, exhibits challenging characteristics such as small weed patch area and color merging tendencies with the crop, which makes it a very typical task to correctly identify, localize and detect weed. A meticulous and scientific detection of weed at early stages may aid in providing timely and quick treatment in the scene to preserve crop health. Random forest classifier is a combination of numerous decision tree classifiers and is a type of ensemble learning which has ample potential for clustering data of similar nature into different classes. This predictive averaging approach has the capability to detect early weed patches, which in turn facilitates precision agriculture. The presented research focusses on binary classification of UAV data of weed infested sugarcane field using decision based random forest classifier at weed's premature stage. Small and multiple green on green weed patches in sugarcane field have been accurately detected and classified into two classes “weed” and “crop”. This algorithm helps detect early weed patches in agricultural setting which in turn aids in weed removal strategies.