# Machine Learning in Cereal Crops Disease Detection: A Review
## 5 Machine Learning-Based Cereal Crop Disease Detection
### Machine Learning in Maize Disease Detection  
An Enhanced CNN for the detection of nine classes of maize leaf disease was proposed by [PERSON] et al. (2017). They proposed a convolutional neural network with receptive field enlargement to enhance the feature extraction performance of the CNN, which is required due to the complexity of maize leaf images. To accomplish this task, the authors collected a dataset of 500 images of maize leaves belonging to nine different classes of maize leaf disease at different stages. The performance of the proposed approach was compared to existing models like AlexNet and GoogleNet and provided an improved classification accuracy of 95.12%. [PERSON] et al. (2018) developed a convolutional neural network for the detection of three different maize leaf diseases by using the Neuroph framework for the java programming language. The proposed approach gave a classification accuracy of 93.5%. [PERSON] et al. (2017) proposed a MobileNet architecture-based maize leaf disease detection that will be deployed on Android mobile devices. The authors utilized a transfer learning approach to fine-tune the pre-trained MobileNet architecture. For this task, they used a public dataset (PlantVillage) with a total of 3852 images of four different classes of maize leaf diseases. The proposed approach yielded an accuracy of 94.53%. [PERSON] et al. (2018) proposed a hybrid network by combining a convolutional neural network and bi-directional LSTM for the detection of nine classes of maize leaf diseases. bi-LSTM was selected by the authors to better accelerate CNN's classification accuracy and increase the co-relation among extracted features. Training of the model was performed on the PlantVillage dataset, which contains 2500 images of maize leaves affected by nine different types of diseases. They implemented various image augmentation techniques and increased the size of the dataset to 29,065 images. The proposed approach achieved a classification accuracy of 99.02%, exceeding existing deep learning methods. [PERSON] et al. (2019) proposed a multi-scale convolutional global pooling convolutional neural network based on the AlexNet and Inception architecture. The proposed model improves on the AlexNet architecture by replacing the last fully connected layer with a global pooling layer and adding a batch normalization layer. This is implemented to solve the low accuracy achieved and the large training data size required when utilizing transfer learning. Training and testing of the proposed model were performed on the PlantVillage \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Citation** & **Year** & **Data Type** & **\# of Classes** & **Sample Size** & **Method** & **Accuracy \%** \\ \hline [PERSON] et al. (2018) & 2021 & Image & 12 & 1100 & MobileNetV2 & 99.67\% \\ [PERSON] et al. (2018) & 2021 & Image & 3 & 2370 & MobileNetV2 & 94.65\% \\ [PERSON] et al. (2018) & 2019 & Image & 1 & 5808 & CNN & 95.83\% \\ [PERSON] et.al (2018) & 2021 & Image & 3 & 300 & CNN & 90\% \\ [PERSON] and [PERSON]. (2018) & 2021 & Image & 3 & 276 & CNN & 91.47\% \\ [PERSON] et al. (2018) & 2020 & Image & 15 & 500 & DenseNet & 94.07\% \\ [PERSON] et al. (2018) & 2019 & Image & 2 & 284 & InceptionV3 & 99\% \\ [PERSON] et al. (2018) & 2019 & Image & 9 & 1080 & InceptionV3 & 97.5\% \\ [PERSON] et al. (2018) & 2020 & Image & 11 & 5932 & SVM & 98.38\% \\ [PERSON] et al. (2018) & 2019 & Image & 3 & 7448 & faster R-CNN & 96.21\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance comparison of selected studies on machine learning based rice disease detection and corresponding datasets. dataset. The authors found that the proposed approach improves average precision by more than 2% when compared to AlexNet. A VGG16 deep learning architecture-based maize disease identification was proposed by [PERSON] [80]. In this work, a transfer learning approach was used to fine-tune the pre-trained VGG16 architecture on a dataset consisting of 7858 images of maize leaves affected by six types of diseases.