# 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
#### 5.6.1 Machine Learning (ML)
Using spectral relative reflectance values obtained by field spectroscopy as references, total canopy spectral classification by GDA for specific narrow bands was 95 \(\pm\) 4.19% for wheat and 94 \(\pm\) 5.13% for chickpea. Meanwhile, for vegetation and environmental monitoring on a new micro-satellite (VEN\(\mu\)S), total canopy spectral classification was 77 \(\pm\) 8.09% for wheat and 88 \(\pm\) 6.94% for chickpea, and for the operative satellite advanced land imager (ALI) it was 78 \(\pm\) 7.97% for wheat and 82 \(\pm\) 8.22% for chickpea. Thus, an overall classification accuracy of 87 \(\pm\) 5.57% for >5% vegetation coverage in a wheat field was achieved within the critical timeframe for weed control, thus providing opportunities for herbicide applications to be implemented. Meanwhile, [PERSON] and [PERSON] (2018) developed a yield loss due to weed infestation model by combined manual image analysis, automated image analysis, image scoring, field scoring, and weed density data to estimate yield loss by weeds (_Cirsium areruse_) in a barley field on UAV images. With a flying height of 25 m above the ground, they successfully computed the model (Equation (7)) and found that grain moisture increased directly proportional to weed coverage (Equation (8))  
\[Y=100\cdot(1-\text{exp}\ (-0.0017\cdot X) \tag{7}\]  
where:  
\(Y=\text{Percentage of crop yield loss}\). \(X=\text{Percentage of weed coverage}\). \[M=0.0310\cdot X \tag{8}\]  
where:  
\(M=\text{Proportional percentage increase in grain moisture}\). \(X=\text{Proportional percentage of weed coverage}\). Other than artificial neural networks (ANN), support vector machine (SVM), and simple ML algorithms, other algorithms have been tested to detect and classify weeds from crops. They are maximum likelihood (ML), random forest (RF), vegetation indices (VIs), and discriminant analysis (DA) algorithms. [PERSON], [PERSON], and [PERSON] (2018) used ML and VIs to classify cruciferous weed patches on a field-scale and broad-scale. Cruiferous weed patches were accurately discriminated against in both scales. However, the ML algorithm has a higher accuracy than VIs, 91.3 % and 89.45%. The same outcome was archived by [PERSON] et al. (2018) when they classified _Silybum marianum_ (L.) in cereal crops. [PERSON] and [PERSON] (2018) explored the potential of a random forest algorithm in classifying pigweeds in soybean crops using a spectroradiometer (FieldSpec 3, PANalytical Boulder, Boulder, CO, USA) and WorldView-3 satellite data. One nanometer spectral data were grouped into sixteen multispectral bands to match them with the WorldView-3 satellite sensor. The accuracy of weed classifications ranged from 93.8% to 100%, with kappa values ranging from 0.93 to 0.97. The result shows an excellent agreement between the classes predicted by the models and the ground reference data. They also found that the most significant variable in separating pigweeds from soybean is the shortwave infrared (SWIR) band. Similar to [PERSON], [PERSON], and [PERSON] (2018) and [PERSON] et al. (2018), they used feature selection to train the random forest (RF) algorithm to classify weeds on different platforms: UAV RGB and hyperspectral camera, respectively. Their studies showed that the integration of feature selection with the RF algorithm produced an accurate map. As for [PERSON] et al. (2018), their output showed that for _Zea mays_, _Convolvoluus arensis_, _Runex_, and _Cirsium areruse_needs, the optimal random forest model with 30 significant spectral features would achieve a mean correct classification rate of 1.0, 0.789, 0.691, and 0.752, respectively. Meanwhile, [PERSON] et al. [40] tested discriminant analysis (DA) to classify and map invasive plant bracken fern distribution using Landsat 8 OLI. The performance of the classification output was compared with high spatial resolution data, WorldView-2 imagery. Worldwide-2 classification outperformed Landsat 8 OLI with overall accuracies of 87.80% and 80.08%, respectively.