# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 5 Weed Detection Using Remote Sensing Technique
### Image Classification and Validation  
Many machine learning (ML) and deep learning (ML) algorithms are available for image classification. However, choosing the best one that fits the research's objective is crucial, because different algorithms have different difficulty levels. Therefore, Section 5.6 will further discuss the application of remote sensing algorithms in detecting weeds in crops.  
Accuracy assessment is crucial to validate the quality of the classification output that best represents the study area. Overall, the assessment can be carried out by comparing the classified pixels with ground truth pixels using a confusion matrix [67]. The result for weed classification is presented in terms of producer accuracy and overall accuracy. Producer accuracy (Equation (1)) is the probability that a pixel in the classification correctly shows class X. Given the ground truth class is X, producer accuracy can be calculated using  
\[Producer\ accuracy=\frac{c_{an}}{c_{.a}}\times 100\% \tag{1}\]  
where:  
- \(c_{aa}\) = element at a position \(a\)th row and \(a\)th column.  
- \(c_{a}\) = column sums.  
Overall accuracy (Equation (2)) is the total percentage of pixels correctly classified, and it can be calculated by using  
\[Overall\ accuracy=\frac{\sum_{a=1}^{U}c_{aa}}{Q}\times 100\% \tag{2}\]  
where:  
- \(Q\) = total number of pixels.  
- \(U\) = total number of classes.  
The agreement between variables with ground truth data can be represented by using the kappa coefficient (Equation (3)), and its value can be calculated by using  
\[Kappa\ coefficient,\ K=\frac{\sum_{u=1}^{U}\frac{c_{a}}{Q}-\sum_{u=1}^{U}\frac{c_{a }c_{a}}{Q^{2}}}{1-\sum_{u=1}^{U}\frac{c_{a}c_{a}}{Q^{2}}}\times 100\% \tag{3}\]  
where:  
- \(c_{a}\) = row sums.  
However, some limitations occur when dealing with object-based classification, primarily related to the real-world object recognition's thematic and geometrical accuracy [68]. Therefore, to address this concern, [PERSON] et al. [46] designed Weed detection Accuracy  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Categories** & **Feature** & **Description/Formula** & **Reference** \\ \hline Vegetation indices & Normalized vegetation index (NDVI) & \((\text{NIR }-\text{R})/(\text{NIR }+\text{R})\) & \\ Excess green index (ExG) & \(2\times\frac{c-R-R}{c-R+R}\) & [62; 63] \\ \hline Color space transformed features & Hue & A gradation or variety of a color & \\  & Saturation & Depth, purity, or shades of the color & [64] \\  & Value & Brightness intensity of the color tone & \\ \hline Wavelet transformed coefficients & Wavelet coefficient mean & Mean value calculated for a pixel & \\ Wavelet transformed coefficients & Wavelet coefficient standard deviation & using discrete wavelet transformation & [65] \\  & & Standard deviation calculated for a & \\  & Wavelet coefficient standard deviation & pixel using discrete wavelet & \\  & & transformation & \\ \hline Principal components (1) & Principal component 1 & Principal component analysis-derived & \\  & & component accounting maximum & [66] \\  & & amount of variance & \\ \hline \hline \end{tabular}
\end{table}
Table 3: An example of features extracted or computed for image classification.  
(WdA), Equation (4). This index analyzes the spatial placement of classified weeds by using the intersection of shapefiles as a spatial relationship rather than the overall overlap.  
\[WdA\;(\%)=\frac{Area\;of\;Observed\;Wed\;objects\;intestice\;Detected\;Wed\; Objects}{Area\;of\;Observed\;Wed}\;\times\;100 \tag{4}\]  
The detection of weeds is crucial for successful site-specific weed management (SSWM). However, weed detection is still challenging for automatic weed removal [37]. In addition, low tolerance between the cutting point and the crop location requires an accurate weed classification against the main crop. Therefore, several works have been conducted in the context of remote sensing image processing to detect and improve site-specific management [69; 70; 71].