# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 5 Weed Detection Using Remote Sensing Technique
### An Overview of Machine Learning in Agriculture  
In recent years, machine learning (ML) has provided a new criterion for agriculture with big data technology and high-performance computing. The development of ML has created new opportunities in agriculture operational management to unravel, measure, and analyze complex data [72]. Generally, the ML framework involves learning from 'experience', known as training data, to execute the classification, regression, or clustering tasks. These training data are usually regarded as a feature described by a set of attributes or variables. The machine learning model works by predicting the pattern and trend of future events in crop monitoring and assessment [73]. The ML model's performance in a particular task is evaluated by performance metrics improved by experience over time. As a result, classification techniques have been a prominent research trend in machine learning for many years, informing various studies. This method seeks to create features from the input data. Furthermore, it is highly field-specific and requires significant human effort, leading to deep learning techniques [36]. Figure 3 shows how machine learning and deep learning techniques work. Figure 3: The differences in how deep learning and machine learning techniques work. Deep learning is a subset of machine learning, but with more complicated image analysis [36], commonly used in agricultural crop monitoring and management. In terms of functionality, machine learning and deep learning share the same purpose: to make intuitive and intelligent decisions using artificial neural networks stacked layer-wise based on what it has learned while being trained [74]. However, in terms of developing an accurate model, machine learning requires a pre-processing stage before the model is developed, trained, and validated. In contrast, deep learning has a 'build in' feature extractor to extract meaningful features from the raw data. It learns features layer by layer, which means that it learns low-level features in the first levels and then progresses up the hierarchy to learn a more abstract representation of the input. [75]. Regardless of which agricultural domain and purpose, it has taken a directive in various crop monitoring purposes such as nutrient disorder, weed detection, plant insects, and disease detection. Many studies on weed detection have utilized deep learning with other remote sensing methods concerning classification or regression performance differences. The outcome has marked high accuracy, outperforming other commonly used image processing techniques [76]. In deep learning (DL), CNN is the most well-known and widely used algorithm [69; 70; 77]. The fundamental advantage of CNN over the other DL algorithms is that it automatically detects significant elements without the need for human assistance [36]. Comparable to the multi-layer perceptron (MLP), where it consists of three layers known as the input, output, and hidden layer [78], CNN has many convolution layers before sub-sampling (pooling) layers, with fully connected (FC) layers as the last layers. An illustration of the CNN framework for image classification is shown in Figure 4.  
A CNN model's input image is structured in three dimensions: height (m), width (m), and depth (r), where height (m) equals the width (m), and the depth (r) is referred to as channel number. For example, the depth (r) of the RGB image in Figure 4 equals three (three bands). The available kernel filters for the convolution layer will be designated by the letter k (n \(\times\) n \(\times\) q). However, n must be less than m, and q must be equal to or less than r. The dot product between the input and the weights is calculated by the convolution layer using Equation (5)  
\[h^{k}=f\left(W^{k}*x+b^{k}\right) \tag{5}\]  
where:  
- \(h^{k}\) = feature maps in size (m  
- \(W^{k}\) = weightage. - \(b^{k}\) = bias. These groundbreaking CNNs were able to achieve such incredible accuracy, partly because of their non-linearity.