Rumex Weed Classification Using Region-Convolution Neural Networks Based-Colour Space Information

Abstract

Weed detection is considered the gold standard in smart agriculture field. An automated detection of weed
procedure is a complicated task, specifically detection of Rumex weed due to different real-world environmental conditions, including illumination, occlusion, overlapped, growth stage, and colours. Few works have done
to classify Rumex weed using machine learning. However, the performance is still not at the level required for
agriculture communities and challenges have not been solved. This work proposes Region-Convolutional Neural
Networks (RCNNs) and VGG16 model based on colour space information to classify Rumex weed from grassland.
This paper is investigated the effectiveness of our proposed method over real-world images under different conditions. The findings have shown that the proposed method superior comparing with other AI existing techniques.
The results demonstrate that the proposed method has an excellent adaptability over real-world images.