# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 5 Weed Detection Using Remote Sensing Technique
### The Application of Remote Sensing and Machine Learning Technique into Weed Detection
#### 5.6.2 Deep Learning (DL)  
Deep learning has recently become a machine learning component widely utilized in agricultural crop monitoring and management. It has taken a directive in many crop monitoring objectives such as weed detection, nutrient disorder, and disease detection. [PERSON] et al. [43] utilized the fully convolutional network (FCN) method to map weeds in rice using unmanned aerial vehicle red-green-blue (UAV-RGB) imagery. Transfer learning was used to optimize the generalization capacity, and skip architecture was chosen to boost prediction accuracy. The result was then compared with the patch-based convolutional neural networks (CNN) algorithm and the pixel-based CNN method. The findings showed a proposed FCN method that outperformed others, both in efficiency and efficacy in terms of accuracy. The overall accuracy of the FCN method was up to 93.5%, and the accuracy for weed recognition was 88.3%. Meanwhile, [PERSON] et al. [94] also tested the same algorithm to delineate weeds from rice in multi-rotor UAV images. Using an RGB-UAV with a flying height of 10 m above the surface, they compared the object-based image analysis with the fully convolutional network (FCN). As expected, their finding shows that FCN performs better than OBIA, with an overall accuracy of 80.2% and 66.6%, respectively, which means that this algorithm can produce precise weed cover maps for the evaluated UAV-based RGB imagery. [PERSON] et al. [69] also tested other deep learning algorithms: convolutional neural networks (CNNs) on other crops, spinach, beet, and bean using UAV images to classify weeds in the crops from a 20 m flying height. The method effectively differentiates weeds from crops with an overall accuracy for beet of 93%, spinach of 81%, and bean of 69%. However, deep learning alone requires a great deal of training data. It is too time-consuming of a process to construct large agricultural datasets with pixel-level identifications by an expert. Therefore, [PERSON] et al. [70] proposed a fully automatic learning method using CNNs with an unsupervised training dataset collection for weed detection from UAV images. The classification started with the identification of inter-row weeds from the automatic detection of crop rows. Then, training datasets from inter-row weeds were made before performing the CNNs to detect crop and weed images. Results obtained were compared with supervised training data, and the difference in accuracy for spinach is 1.5%, and for bean is 6%. The differences between supervised and unsupervised are narrow. This proposed method can be the best option, since supervised labelling is expensive and challenging and requires human expertise. [PERSON] et al. [71] evaluated the unsupervised deep learning performance to discriminate weeds from soybean in UAV images. They tested two unsupervised deep clustering algorithms, joint unsupervised learning of deep representations and image clusters (JULE) and deep clustering for unsupervised learning of visual features (DeepCluster), using two public weed datasets. The first datasets were captured in a soybean plantation in Brazil, and weeds were distinguished between the grass and broad leaf weed. Meanwhile, the second dataset consists of 17,509 labelled images of eight common species originating from Australia. Semi-automatic data labelling in agriculture was used to evaluate the outputs, and the result showed that this method achieved up to 97% accuracy, reduced 100 times in manual annotations. This study has used the shape, texture, and pattern of weeds and crops trained and classified by remote sensing algorithms. However, more research needs to be carried out to detect and produce an accurate weed coverage map that recognizes weed types: grass, sedge and broad-leaved in the paddy field. This is because different weeds have different characteristics that require other variables to identify them.