# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 5 Weed Detection Using Remote Sensing Technique
### Advantages of Implementation of Remote Sensing in Weed Detection through PA  
The usage of herbicides, also known as agrochemicals, to control weeds in paddy fields has caused several impacts on the environment and human health [100]. Therefore, the authorities can consider reducing these inputs to follow an environmentally friendly rice production practice. A study by [PERSON], [PERSON], and [PERSON] [101] showed that a 10% reduction in agrochemical grants would reduce agrochemical use. However, it dramatically reduces national welfare and decreases food safety. Nevertheless, we can overcome these issues by implementing remote sensing SSWM techniques into precision agriculture (PA).  
Improving weed management can improve our food security. Numerous remote sensing platforms are available to monitor weeds, and unmanned aerial vehicles (UAV) are among the most popular platforms used these days. The excellent part of a UAV is that it can fly low and precisely detect the presence of weeds in the paddy plot. Numerous researchers proved that a UAV could produce an accurate SSWM map with overall accuracy ranging from 66.6% to 99%, depending on the type of weeds found in the plot [49; 89; 91; 94].  
The remote sensing technique can be used to locate weed presence in the paddy plot by using multiple approaches such as machine learning [62] and deep learning [57; 58] or by combining them both. Previous studies (Table 4) proved that any weeds, grass, sedge, and broad-leaved weeds in crops could be classified using remote sensing techniques. Therefore, this technique can be adopted into paddy field practices. These algorithms were beneficial in detecting weed distribution in the paddy field, with sufficient training data. The weed location will be recorded, and thus, the farmers will know its location and estimate the suitable volume of herbicide needed to control the invasive plant in the plot. Therefore, the over-application of herbicides will not be an issue anymore.  
There is no standard method drawn systematically and strategically planned to detect and manage weeds in paddy fields using remote sensing in developing countries. This study is significant for finding the best approach to classify weeds in a paddy plot. Using UAV imagery, [PERSON] et al. [42] chose a semantic labelling approach to generate weed distribution maps in paddy. A residual framework with an ImageNet pre-trained convolutional neural network (CNN) was adapted and transferred into the dataset by a fine-tuning process. A fully connected conditional random field (CRF) was adapted to improve the spatial details. They successfully produced weed distribution maps with an overall accuracy up to 94.45% and kappa coefficient of 0.9128. The newly generated map can guide the sprayer UAV to spray the herbicide only at the weed colony. Thus, the usage of a spraying UAV can minimize the contact between farmers and herbicides and, at the same time, reduce the impact of agriculture on the environment and human health [102].  
Different types of weeds need different treatments. Traditional practices are too time-consuming and require many human resources, and they are not effective methods to monitor weed presence. Developing countries' farmers need this technology to improve and increase yield production.