# 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)  
Machine learning is a part of artificial intelligence that enables machines to recognize patterns and judge with little or no human input. Back during the early introduction to machine learning, [PERSON] et al. [81] proposed a simple morphological characteristic measurement of a leaf shape (perimeter2/area) and a self-organizing neural network to discriminate weeds from carrots using a Nikon Digital Camera E900S. Their proposed method enables the system to learn and differentiate between species with more than 75% accuracy without predefined plant descriptions. [PERSON] et al. [86] tested an artificial neural network (ANN) to classify weeds (wild oats, reroot pigweed) from crops (field pea, spring wheat, canola) using hyperspectral images. The original data were 61 bands that were reduced to seven bands using principal component analysis (PCA) and stepwise discriminant analysis. A total of 94% overall accuracy was obtained from the ANN classification. [PERSON] et al. [90] also successfully classified weeds from sugarcane using ANN with an overall accuracy of 91.67% with a kappa coefficient of 0.8958. [PERSON] et al. [45] investigated the use of artificial neural networks (ANN) to detect weed plants in rice fields using aerial images. To train the algorithm with a flying height of 50 m, they used a gray-level co-occurrence matrix (GCLM) with Haralicks descriptor for texture classification and a normalized difference index (NDI) for color. As a result, they successfully obtained 99% precision for detecting weed on the test data. However, the detection level was low for weeds similar to rice crops, because the image resolution was 50 m above the ground. Later, to evaluate the ANN's performance, [PERSON] and [PERSON] [37] used a digital camera to detect weeds using shape features with an improved machine learning algorithm, support vector machine (SVM). Results showed that SVM outperformed the AAN with an overall accuracy of 95.00%, while 93.33% of weeds were correctly classified. Meanwhile, for ANN, its overall accuracy was 92.92%, where 92.50% of weeds were correctly classified. [PERSON] [84] used ML knowledge to discriminate rice from weeds from paddy fields by overlapping and merging 13 layers of binary images of red-green-blue and other color components (cyan, magenta, yellow, black, and white). These color components were captured using a digital camera (Cyber-shot DSC T-700, Sony) and used as input to specify \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sensors** & **Crops** & **Weed Type** & **Technique** & **Accuracy (\%)** & **Implications** & **Year** & **Reference** \\ \hline \multirow{4}{*}{RGB*} & \multirow{4}{*}{Cotton} & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & & 83.33\% (low & The findings & \multirow{4}{*}{2020} & \multirow{4}{*}{[97]} \\  & & Object-based image & & density plot, & RGB images for weed & \\  & & broad-leaved & analysis (OBIA) and random forest (RF) & density plot) and & density plot) and & density plot) and & density plot) and \\  & & & random forest (RF) & 89.16\% (high & precision weed & \\  & & & & density plot) & management & \\  & & & & & The combination of & \\  & & & & OBIA-ANN & & \\  & & broadleaved & & OBIA with artificial & & demonstrated the & \\  & & & (NN) algorithm & & feasibility of weed & \\  & & & & mapping in the sorghum & field & \\ \hline \hline \end{tabular}  
* RGB = red, green, blue; OLI = operational land imager.  
\end{table} 
Table 4: \(Cont\). the pixels with target intensity values based on mean ranges with \(\pm 3\times\) standard deviation. The result shows that yellow with 1x standard deviation has the best target intensity values in discriminating paddy from weeds, with improved ratio values from 0.027 to 0.0015. [PERSON] et al. (2018) used general discriminant analysis (GDA) to detect grasses and broad-leaved weeds among cereal and broad-leaved crops.