# Machine Learning in Cereal Crops Disease Detection: A Review
## 6 Discussion  
The past decade has seen the rise of Machine learning applications in various sectors; this exponential rise is attributed to the development of efficient deep learning models for classification and object detection. In our work, we managed to explore research works conducted on the application of machine learning in the area of agriculture, specifically on the application of machine learning techniques in the detection and identification of cereal crop diseases. The survey is conducted on research works performed in the past five years and, as such, we noticed an increase of interest in the research of applying machine learning techniques for cereal crop disease es detection. This is reflected in Figure 4, in which the majority of the works covered in this review paper are performed in the past three years. This rise in research work is attributed to :  
* Availability of public datasets
* Availability of efficient and powerful models
* Free cloud computing resources like Google co-laboratory  
Our review work has outlined the rapid popularity of Deep Learning techniques (Figure 5) when compared to traditional machine learning algorithms. Deep learning techniques have become a choice for their performance and there flexibility of adapting to unique tasks. The outstanding performance of deep learning techniques in the area of object detection is also one of the factors for their rise in popularity. We also factored in Transfer Learning as one of the major reasons for the increased use of deep learning architectures. Transfer learning allowed researchers to gain the most out of existing architectures by reducing the time needed for training a model and also the need for high-performance computing resources. From our observation and analysis of works conducted on machine learning-based cereal crop disease detection, we summarise the best approaches for the task. These are:  
* For early disease detection, Hyperspectral/multispectral imaging in conjunction with deep learning is the appropriate tool. It allows for much earlier disease detection, even before major symptoms arise.
* Deep Learning models trained via transfer learning achieve a better detection/classification performance with a much shorter training time
* Deep Learning models trained on a dataset of images captured using mobile/digital cameras are the preferable option when aiming for an effective and easily deployable solution. The major challenges we identified during the survey work are:
* Lack of a standardised public dataset;  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Citation** & **Year** & **Data Type** & **\# of Classes** & **Sample Size** & **Method** & **Accuracy \%** \\ \hline [PERSON] et al. [63] & 2021 & Image & 9 & 500 & CNN & 95.12\% \\ [PERSON] et al. [76] & 2019 & Image (PlantVillage) & 9 & 2500 & CNN & 95.5\% \\ [PERSON] et al. [77] & 2021 & Image (PlantVillage) & 9 & 2500 & MobileNetV2 & 93.5\% \\ [PERSON] et al. [78] & 2020 & Image (PlantVillage) & 9 & 2500 & LSTM & 99.02\% \\ [PERSON] et al. [79] & 2021 & Image (PlantVillage) & 9 & 2500 & TCI-ALENN & 99.18\% \\ [PERSON] [80] & 2019 & Image (PlantVillage) & 9 & 2500 & VGG16 & 96.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Performance comparison of selected studies on machine learning based maize disease detection and corresponding datasets.  
secondary research question. Works that answer the research questions and pass the selection criterion are selected from five online search databases. With regard to reviewing articles, we tried to identify and summarise the available open datasets available for each category of cereal crops.  
Conceptualization, F.G.W. and T.G.D.; Methodology, F.G.W. and T.G.D.; Validation, Y.M.A. and F.S., T.G.D. and S.R.K.; Writing--original draft preparation, F.G.W.; Writing--review and editing, Y.M.A., F.S., T.G.D. and S.R.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.