# Machine Learning in Cereal Crops Disease Detection: A Review
## 5 Machine Learning-Based Cereal Crop Disease Detection
### Machine Learning in Rice Disease Detection
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Citation** & **Year** & **Data Type** & **\# of Classes** & **Sample Size** & **Method** & **Accuracy \%** \\ \hline [PERSON] et al. [48] & 2021 & Image & 3 & 360 & SVM & 93.3\% \\ [PERSON] et al. [49] & 2020 & Image & 3 & 876 & VGG16 & 99.07\% \\ [PERSON] et al. [51] & 2021 & Image & 11 & 440 & MobileNet & 92\% \\ [PERSON] et.al [52] & 2021 & Image & 1 & 450 & CNN & 89.9\% \\ [PERSON] et al. [53] & 2021 & Image & 3 & 1500 & VGG19 & 99.38\% \\ [PERSON] et al. [56] & 2018 & Image & 4 & 8828 & AlexNet & 84.54\% \\ [PERSON] et al. [55] & 2017 & Image & 6 & 9230 & VGG-FCN & 97.95\% \\ \hline [PERSON] et al. [57] & 2019 & Hyper-spectral & 2 & 284 & v-SVR & 0.99R\({}^{2}\) \\ [PERSON] et al. [58] & 2019 & Hyper-spectral & 2 & 145 & Linear Regression & 0.75R\({}^{2}\) \\ [PERSON] et al. [59] & 2019 & Hyper-spectral & 2 & 89 & SVM & 85.7\% \\ \hline \end{tabular}
\end{table} 
Table 11: Performance comparison of selected studies on machine learning based wheat disease detection and corresponding datasets. A deep learning method for the detection of 15 different rice diseases was implemented by [PERSON] et al. [69]. The authors developed a deep learning architecture based on the fusion of existing DenseNet and Inception architectures. For testing the proposed model, the authors compiled a dataset consisting of 500 images belonging to 15 classes of rice disease. Their proposed model achieved a classification accuracy of 94.07%. [PERSON] et al. [70] utilized three popular deep learning architectures for the task of detecting six different types of rice leaf diseases that occur in Bangladesh. They choose the models, Inceptionv3, MobileNetv1, and ResNet50 for their work. They utilized transfer learning and image augmentation techniques. For testing and training their proposed models, the authors collected a dataset of 600 images from rice fields in Bangladesh. Accordingly, they achieved an accuracy of 98%, 99%, and 96% for the models Inceptionv3, MobileNetv1, and ResNet50, respectively. Similarly, [PERSON] et al. [71] utilized the Inceptionv3 architecture with transfer learning and Support Vector Machine (SVM) for the task of detecting and classifying nine different types of rice disease that occur in Bangladesh. The authors collected a dataset of 1080 images for this task. In this work, Inceptionv3 deep learning model is used for the task of feature extraction and SVM as the final classifier. The authors employed various image processing and augmentation techniques. Their proposed approach gave an accuracy of 97.5%. [PERSON] et al. [72] also proposed a deep learning and SVM approach for the detection and classification of four types of rice diseases. In this work, the authors compared and contrasted the performances of 11 different types of deep convolutional neural network architectures that will give the best feature for use with the SVM. For this task, the authors collected a dataset of 5932 images from rice fields around Odisha, India. The performance of the feature extraction CNN models was measured in terms of accuracy, f1, sensitivity, specificity, and training time. Based on their experimental results, the authors found that ResNet50 architecture in conjunction with SVM yields that best classification result of 98.38% and a training time of 69 s.  
[PERSON] et al. [73] proposed a fusion of FCM-KM and Faster R-CNN algorithms for the detection of three distinct rice diseases. FCM-KM was chosen for its tested tolerance for noise and its effectiveness in addressing low detection accuracy caused by background  
Figure 3: Deep Convolutional Neural Network architecture for the detection of rice blast [65]. interference and blurred images. For conducting the research, the authors compiled a dataset of 7448 images of rice affected by Rice blast, Bacterial blight, and sheet blight. The Otsu thresholding algorithm was chosen for the task of image segmentation and R-CNN for feature extraction and classification.