# 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
#### 2.2.2. Research Opportunities Identified  
* During the literature survey presented in earlier sections, the following opportunities that the authors of this paper believe have seen little interest from researchers are as follows:
* Little discussion of the real-time monitoring of the onset signs of diseases before they spread throughout the whole plant. * Few papers discussed real-time monitoring and real-time mitigation measures such as actuation operations, spraying pesticides, and spraying fertilizers, to name a few examples. * Very little research discussed the combination of these monitoring and phenotyping tasks into one system to reduce costs and improve technology availability to farmers and add convenience. * Little research discussed the post-harvest benefits of disease/nutrient deficiency detection or similar systems. * Most research papers on plant disease detection models processed two-dimensional images captured from plant samples. In cases where samples were in the form of fruits, single-input cameras or a two-dimensional view may pose a challenge because of the spherical or cylindrical nature of most fruits. The authors noticed that the fruit disease symptoms or any types of defects are not always evenly distributed across the surface area of a sample fruit; Figure 14 shows an example. Therefore, in high-throughput and high-speed applications, a sample fruit might be oriented such that the diseased part is masked or hidden from the camera's line of sight, so an incorrect classification is highly probable. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification Method** & **Plant/Crop** & **Reference** & **Number of Diseases** & **Disease** & **Results** \\ \hline \multirow{8}{*}{Fuzzy Classification} & Mango & [83] & \multirow{2}{*}{3} & Powdery mildew, & \multirow{2}{*}{90\% accuracy average} \\  & & & & & \\  & & & & & \\ \cline{1-1}  & Strawberry & [115] & 1 & Iron deficiency & \multirow{2}{*}{97\% accuracy} \\ \cline{1-1}  & & & & & Bacterial blight, leaf & \\  & & & & & curl, port rot, & \\  & & & & & Verticillium wilt, & \\  & & & & & Anthracnose, & \\  & & & & & seed rot, & \\ \cline{1-1}  & Cotton, wheat & [116] & 18 & tobacco struck virus, & \multirow{2}{*}{95\% accuracy average} \\  & & & & & tropical rust, Fusarium & \\  & & & & & wilt, black stem rust, & \\  & & & & & leaf rust, stripe rust, & \\  & & & & & loose smut, flag smut, & \\  & & & & & complete burnt, partial & \\  & & & & & burnt, earcockle, lundo & \\ \hline Soybean & [19] & 1 & Polar & \multirow{2}{*}{96\% accuracy} \\ \hline Cotton & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 14: A sample fruit with uneven distribution of the disease-infected surface area. * Few studies discussed the importance of optimum optical or lighting conditions in the successful operation of an image-based plant disease detection model and their relationship to classification accuracy and efficiency. Hence, this study took advantage of the second-to-last opportunity outlined above and proposes two conceptual ideas as a mitigation measure. The purpose of these two propositions is to give a classification model a virtual three-dimensional view of a sample fruit so that a classification model \"sees\" the total surface of a sample fruit so as not to miss any important details before making a final classification. The two proposed ideas are:  
* A multicamera-input fruit disease detection model
* A dynamic-input fruit disease detection model  
A multicamera-input fruit disease detection model has an improved input system that features multiple-input camera sensors specially arranged in a circular setup and equidistant from each other with a sample fruit at the central point. These cameras capture the surface of a sample fruit at different angles such that all the fruit surface is captured (refer to Figure 15). The classification model should classify each input image from each camera independently and consolidate all the results to make a final classification.