# Machine Learning in Cereal Crops Disease Detection: A Review
## 5 Machine Learning-Based Cereal Crop Disease Detection
### Machine Learning in Rice Disease Detection  
Identification and classification of 12 types of rice leaf diseases using MobileNetV2 architecture and attention mechanism were proposed by [PERSON] et al. [63]. The MobileNetV2 architecture was pre-trained on the ImageNet dataset and fine-tuned by using the transfer learning approach on a smaller local dataset. The authors utilized Channel Attention Mechanism (CAM) to better learn the inter-channel relationships. For fine-tuning and testing their proposed model, the authors collected a total of 1100 images of healthy and disease rice leaves. These 660 were compiled from various sources on the internet and 440 were collected from the field. The proposed model achieved an average classification accuracy of 99.67%. Similarly, [PERSON] et al. [64] proposed a MobileNetv2 based approach for the classification of three types of rice leaf diseases by utilizing attention mechanism and Bayesian optimization. Model training and validation were performed on a public dataset of 2370 images belonging to three classes of rice disease and one healthy class. The authors achieved a classification accuracy of 94.65%. [PERSON] et al. [65] proposed a convolutional neural network-based rice blast disease detection approach. The authors proposed two CNN architectures, the first network containing four convolutional layers, four max-pooling layers, and three fully connected layers, and ReLU after each layer (Figure 3a) and a second network having the same convolutional layers and max-pooling layer structure as the first network, but with two additional fully connected layers as shown in (Figure 3b). The two models were trained on a custom dataset of 5808 images of healthy and rice blast infected leaves. The dataset was collected on-site and is divided into 2906 positive (rice blast infected) and 2902 healthy images. The authors utilized 5-fold cross-validation and a selected the second model due to its inherent stability on small datasets and chieved an accuracy of 95.83%. The proposed approach was compared to hand-crafted approaches like Local Binary Patterns Histogram (LBPH), Haar-WT. The comparison result suggests that the proposed CNN method achieves superior feature extraction and classification results. A similar approach for the detection and classification of three classes of rice disease was proposed by [PERSON] et al. [66]. The authors proposed a convolutional neural network trained on a dataset of 300 images containing three types of rice leaf disease (Brown spot, Leaf blight, and Hispa) and one healthy class. The model achieved a classification accuracy of 90%. This low classification accuracy is a result of the small dataset size the authors used and the lack of utilizing transfer learning. [PERSON] et al. [67] proposed a convolutional neural network approach for the detection of three classes of rice disease. The authors utilized HSV color space for the separation of background and foreground and the K-means algorithm for disease segmentation. A random forest classifier for the detection and classification of three types of rice leaf disease was proposed by [PERSON] and [PERSON] [68]. A local dataset compromising a total of 276 images of healthy and infected rice leaves was collected by the authors for testing and training their proposed algorithm. Feature extraction was implemented by using intensity moments. The proposed approach achieved a classification accuracy of 91.47%.