A Review Analysis of Weed Detection in Crops by Computational Vision
Volume: 10 - Issue: 02 - Date: 01-02-2021
Approved ISSN: 2278-1412
Published Id: IJAECESTU318 | Page No.: 179-186
Author: Deepika Kurmi
Co- Author: Sneha Soni
Abstract:-In recent years, precision agriculture and precision weed control have
been developed aiming at optimising yield and cost while minimising
environmental impact. Such solutions include robots for precise hoeing or spraying.
The commercial success of robots and other precision weed control techniques has,
however, been limited, partly due to a combination of a high acquisition price and
low capacity compared to conventional spray booms, limiting the usage of
precision weeding to high-value crops. Nonetheless, conventional spray booms are
rarely used optimally. A study by Jørgensen et al. (2007) has shown that selecting
the right herbicides can lead to savings by more than 40 percent in cereal fields
without decreasing the crop yield when using conventional sprayers. Therefore, in
order to utilise conventional spray booms optimally, a preliminary analysis of the
field is necessary. The major components of this system are composed of three
processes: Image Segmentation, Feature Extraction, and Decision-Making. In the
Image Segmentation process, the input images are processed into lower units where
the relevant features are extracted.
Key Words:-Weed detection, SVM, Kmeans, Image segmentation
Area:-Engineering
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