Abstract
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.
Highlights
The Precision Agriculture (PA) concept advocates for the adjustment of resources and agronomic practices to the requirements of the soil and crop, seeking greater sustainability and efficiency
Among the practices associated with a PA schema, site-specific weed management is effective in decreasing herbicide costs, optimising weed control and preventing unnecessary environmental contamination [1,2,3,4]
Regarding the scenes shown in the images used in this work, the vegetation always coincided with weeds because the images were taken in the inter-row area
Summary
The Precision Agriculture (PA) concept advocates for the adjustment of resources and agronomic practices to the requirements of the soil and crop, seeking greater sustainability and efficiency. The variable spatial distribution of weeds must, be considered in weed-management strategies: by using target chemical applications, agriculture and the environment can be more sustainable [7]. To carry out suitable site-specific weed management, it is essential to have accurate information on within-field variation of weeds, i.e.: (i) where the weeds are located; (ii) the weed seedling density; and (iii) the type of infestation present. This information can be obtained by different methods, including cameras located on aerial platforms or ground platforms
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