# A Review of Plant Disease Detection Systems for Farming Applications
## 2 Literature Review: Precision Agriculture Research Developments
### Plant Disease/Pest/Weed Detection System Basic Principles
#### 2.1.1 Image Processing  
Image Acquisition  
Image collection is the first step in a system for detecting plant diseases [6; 8; 12]. Image sensors, scanners, and unmanned aerial vehicles (UAVs) can all be used to capture photos of plants [3]. The commonly utilized image-acquisition tools are a charge-coupled device (CCD) and a complementary metal-oxide-semiconductor (CMOS) [15]. Both of these camera technologies convert light signals and protons to digital data, which is then further transformed into a picture [15; 16]. However, their methods of turning the light signals into image data vary [16]. In a CCD camera, the light signals are transferred through a series of adjacent pixels before being amplified and converted into image data at the end of these pixel strings [17; 18]. This enables CCD cameras to possess minimal degradation during the image-acquisition process [19]. CCD cameras generate sharp pictures with reduced distortion [18]. Contrarily, in CMOS cameras the light signals are collected, amplified, and converted at each pixel of the image sensor [15]. This enables the CMOS devices to generate images faster than CCD devices since each pixel can convert light signals into an image locally [17]. CMOS devices are normally preferred in projects with a low budget since they \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**A Typical General Plant Disease Detection System**} \\ \hline \multicolumn{3}{c}{**Summary of Image-Processing Steps**} & \multicolumn{2}{c}{**Different Classification Techniques**} \\ \hline
1. & Image processing: & 1. & SVM Classifier \\  & 1.1 & Image acquisition & 2. & ANN Classifier \\  & 1.2 & Image pre-processing & 3. & k-NN Classifier \\  & 1.3 & Image segmentation & 3. & k-NN Classifier \\  & 1.4 & Feature extraction & 4. & FUZZY Classifier \\  & 1.5 & Machine learning classification & 4. & FUZZY Classifier \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of image-processing steps and different classification techniques in plant disease detection. Figure 4: Steps of image processing [5]. are cheap compared to CCD devices, have a lower power consumption, and can acquire high-quality images faster than their CCD counterparts [17; 18; 19]. Figure 5 shows the serial versus localized pixel image conversion of CCD and CMOS image sensors, respectively. An imaging acquisition tactic known as time delay and integration (TDI) can be combined with either CCD or CMOS technology to drastically improve their image-acquisition capabilities [20]. Applications involving fast-moving objects and requiring high precision and the capacity to function in extremely dim lighting environments use TDI [20; 21]. Refer to Figure 6 for an example of a high-speed application of TDI technology in which a high-velocity train was captured with a normal and a TDI-featured camera in the left and right pictures, respectively. When the camera was operated in normal mode, the image of the train was a blur due to its high velocity and dim lighting conditions; however, the incorporation of a TDI mode countered these challenges and produced a clear detailed picture of the train. This is a crucial step in an ML-based disease detection system [14]. Pre-processing of an image deals with the correct setting of image contrast and filtration of interference signals resulting in noise and hence blurry images [18; 19]. This procedure can greatly enhance the precision of feature extraction and the correct disease detection in general [15]. Pre-processing typically involves straightforward treatments such as image cutting, clipping, cropping, filtering, trimming, and deblurring [3]. [PERSON] [23] explained that a typical image pre-processing procedure that is generally employed in image-based detection systems comprises image acquisition, gray scaling, filtering, binarization, and edge filtering. Figure 5: CCD vs. CMOS image conversion [15]. The first step in the procedure illustrated in Figure 7 involves the transformation of a colored image into a gray image [23].