Weed Detection using Convolutional Neural Network
This work addresses weed management in agriculture to reduce herbicide use, but it is incremental as it applies existing CNN methods to a specific domain.
The paper tackled weed detection in agricultural fields using convolutional neural networks (CNNs), achieving an accuracy of 94% on a dataset of 15,336 segments.
In this paper we use convolutional neural networks (CNNs) for weed detection in agricultural land. We specifically investigate the application of two CNN layer types, Conv2d and dilated Conv2d, for weed detection in crop fields. The suggested method extracts features from the input photos using pre-trained models, which are subsequently adjusted for weed detection. The findings of the experiment, which used a sizable collection of dataset consisting of 15336 segments, being 3249 of soil, 7376 of soybean, 3520 grass and 1191 of broadleaf weeds. show that the suggested approach can accurately and successfully detect weeds at an accuracy of 94%. This study has significant ramifications for lowering the usage of toxic herbicides and increasing the effectiveness of weed management in agriculture.