A Novel Approach for Weed Type Classification Based on Shape Descriptors and a Fuzzy Decision-Making Method

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.An important objective in weed management is the discrimination between grasses (monocots) and broad-leaved weeds (dicots), because these two weed groups can be appropriately controlled by specific herbicides. In fact, efficiency is higher if selective treatment is performed for each type of infestation instead of using a broadcast herbicide on the whole surface. This work proposes a strategy where weeds are characterised by a set of shape descriptors (the seven Hu moments and six geometric shape descriptors). Weeds appear in outdoor field images which display real situations obtained from a RGB camera. Thus, images present a mixture of both weed species under varying conditions of lighting. In the presented approach, four decision-making methods were adapted to use the best shape descriptors as attributes and a choice was taken. This proposal establishes a novel methodology with a high success rate in weed species discrimination.This research was partly financed by the Spanish Ministry of Economy and Competition (AGL2011-30442-C02-02 project) and by the 7th Framework Programme of the European Union under the Grand Agreement CP-IP245986-2 (RHEA project). Research of Herrera was supported by the JAE-Doc Program, financed by the Spanish National Research Council (CSIC) and the European Social Fund (ESF). We acknowledge support by the CSIC Open Access Publication Initiative throught its Unit of Information Resources for Research (URICI).Peer reviewe

A Novel Approach for Weed Type Classification Based on Shape Descriptors and a Fuzzy Decision-Making Method

An important objective in weed management is the discrimination between grasses (monocots) and broad-leaved weeds (dicots), because these two weed groups can be appropriately controlled by specific herbicides. In fact, efficiency is higher if selective treatment is performed for each type of infestation instead of using a broadcast herbicide on the whole surface. This work proposes a strategy where weeds are characterised by a set of shape descriptors (the seven Hu moments and six geometric shape descriptors). Weeds appear in outdoor field images which display real situations obtained from a RGB camera. Thus, images present a mixture of both weed species under varying conditions of lighting. In the presented approach, four decision-making methods were adapted to use the best shape descriptors as attributes and a choice was taken. This proposal establishes a novel methodology with a high success rate in weed species discrimination