# A Review of Plant Disease Detection Systems for Farming Applications
## 2 Literature Review: Precision Agriculture Research Developments
### Literature Survey: Plant Disease/Nutrient Deficiency Monitoring Systems
#### 2.2.1 Tabulated Summary of Plant Disease/Nutrient Deficiency Monitoring Systems  
Table 4 summarizes a literature survey on these systems. Several publications have been consulted for this research study. A few aspects have been noted for each publication such as the type of crop investigated, the number of crop disease covered in the study and the classification results achieved. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification Method** & **Plant/Crop** & **Reference** & **Number of Diseases** & **Disease** & **Results** \\ \hline \multirow{7}{*}{SVM Classification} & Maize & [84] & 1 & Not specified & 79\% accuracy \\ \cline{2-5}  & Graperuit, lemon, lime & [85] & 2 & \begin{tabular}{c} Canker and \\ anthracose diseases \\ \end{tabular} & 95\% accuracy for both \\ \cline{2-5}  & Grape & [86] & 2 & \begin{tabular}{c} Downy mildew, \\ powdery mildew \\ \end{tabular} & \begin{tabular}{c} 88.99\% accuracy \\ for both \\ \end{tabular} \\ \cline{2-5}  & \multirow{2}{*}{Oil palm} & [3] & 2 & \
\begin{tabular}{c} Chimera, anthracose \\ accuracy respectively \\ \end{tabular} & 97\% and 95\% accuracy respectively \\ \cline{2-5}  & Potato & [87] & 4 & Late blight, early blight & 95\% for both \\ \cline{2-5}  & Grape & [10] & 3 &
\begin{tabular}{c} Black rot, Esca, \\ leaf blight \\ \end{tabular} & Not specified \\ \cline{2-5}  & Tea & [88] & 3 & Not specified & 90\% accuracy \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of a literature survey on plant disease/pest/weed detection systems. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Pros and Cons of Different Classification Methods Most Used in Plant Phenomics and Disease Monitoring** \\ \hline
**1. Support Vector Machine (SVM)** \\ \hline
**Advantages** & **Disadvantages** \\ \hline \(\bullet\) Works very accurately when there is a clear formation of a hyperplane [74]. & \(\bullet\) & Accuracy difficulties with a large amount of training data [71]. \\ \hline \(\bullet\) Works more accurately in high-dimension spaces such as & \(\bullet\) & Susceptibility to noise and overlapping data classes [75]. \\ \cline{2-5}  & 3D and 4D [51]. & \(\bullet\) & The number of characteristics for a single dataset must not exceed the number of data points in the training set [74]. \\ \hline
**2. Artificial Neural Network (ANN)** \\ \hline \(\bullet\) Capable of multitasking [76]. & \(\bullet\) & Complex programming algorithms [75]. \\ \hline \(\bullet\) The machine is learning continuously, and the accuracy is & \(\bullet\) & Accuracy is data-dependent; more training data translate to a more accurate classification and vice versa [75]. \\ \hline \(\bullet\) Has many applications (e.g., mining, agriculture, medicine, and engineering) [59]. & \(\bullet\) & Hardware reliance (cost, complexity, and maintenance) [33]. \\ \hline
**3. k-Nearest Neighbor (k-NN)** \\ \hline \(\bullet\) No initial training period [74]. & \(\bullet\) & Accuracy difficulties with a large amount of training data [79]. \\ \hline \(\bullet\) Simple to add new data to the model to extend its scope [80]. & \(\bullet\) & Not suitable for high-dimensional space [80]. \\ \hline \(\bullet\) Relatively easy to implement with only the two parameters to work out the k value and the geometric distance & \(\bullet\) & Susceptibility to noise and outliers [74]. \\ between the points [78]. & & \\ \hline \multicolumn{3}{c}{**4. Fuzzy Classifier**} \\ \hline \(\bullet\) Unclear, distorted, degraded, or vague input data is & \(\bullet\) & Depending on people’s experience and expertise [82]. \\ \multicolumn{3}{c}{ accommodated by the model [81].} & \(\bullet\) & Require excessive supervision in a form of testing \\ \hline \(\bullet\) More flexibility and ease to change the rules [83]. & \(\bullet\) & The is no universal approach to implementing fuzzy classification \\ \hline \(\bullet\) Robust in applications with no exact input format [82]. & \(\bullet\) & The is no universal approach to implementing fuzzy classification \\ \hline \hline \end{tabular}
\end{table}