# Machine Learning in Cereal Crops Disease Detection: A Review
## 5 Machine Learning-Based Cereal Crop Disease Detection  
In this section, we will discuss machine learning approaches utilized for the detection of the listed cereal crop species. We covered works conducted on disease detection from images taken by mobile/digital cameras and hyper-spectral images [45; 46] captured by spectral imaging devices. Hyperspectral imagery is a non-invasive technology for extracting spectral, spatial, textural, and contextual features from food and agricultural products [47]. segmentation of healthy leaf areas from diseased areas was achieved by using the k-means algorithm. [PERSON] et al. [51] proposed a one-shot learning approach based on the MobileNet v3 architecture. The pre-trained MobileNet model was further fine-tuned on the PlantVillage dataset and the last two fully connected layers were fine-tuned on a dataset of 440 images consisting of 11 wheat disease classes. Each class has 40 images each. The training dataset is composed of images collected from the CGIAR crop disease dataset and Google Images. The authors used accuracy, precision, and recall as the main performance metrics, and, using their proposed approach, they manage to obtain 92% accuracy, 84% precision, and 85% recall. An N-CNN based Powdery Mildew wheat disease detection proposed by [PERSON] et al. [52] uses a CNN that is initially trained on the CGIAR dataset and then utilized a transfer learning approach to increase the model's accuracy on a smaller Powdery Mildew dataset. Their dataset consists of 450 images comprised of images collected on the field by the researchers and also images acquired from sources on the internet. They used the accuracy metrics to measure their model's performance and manage to achieve 89.9% accuracy on testing data. A Deep Learning approach towards the detection of a wide variety of wheat diseases was proposed by [PERSON] et al. [53]. The proposed approach employed popular deep learning architectures i.e., Inceptionv3, ResNet50, and VGG16/19. The authors compared the performance of these architectures on a dataset consisting of 1500 images belonging to three classes of wheat diseases. The dataset was compiled from a combination of images collected from wheat farms in Ethiopia and a publicly available online repository. Classification and detection of 10 classes of wheat disease using VGG16 and ResNet50 architectures were performed by [PERSON] et al. [54]. The authors used a Large Wheat Disease Classification Dataset (LWDCD2020) compromising over twelve thousand images belonging to nine wheat disease classes and one healthy class. For evaluation of the proposed model, they utilized accuracy and f1 metrics. The proposed model managed to achieve 98.62% classification accuracy. An in-field automatic wheat disease diagnosis based on weakly-supervised deep learning was proposed by [PERSON] et al. [55]. The authors trained two models, VGG-FCN and VGC-FCN-S, using Multiple Instance Learning (MIL). To achieve this, they produce a dataset, Wheat Disease Database 2017 (WDD2017), consisting of 9230 images of wheat crops belonging to six classes of wheat diseases and one healthy class. The two proposed deep learning models achieved a 97.95% and 95.12% accuracy, respectively. A modified AlexNet architecture was proposed by [PERSON] et al. [56] for the detection and classification of four types of wheat diseases (Stem rust, Yellow rust, powdery mildew). The authors employed a transfer learning approach, by using a pre-trained AlexNet on the ImageNet dataset and using a custom dataset to further fine-tune the model. The authors collected a dataset of 8828 images divided into 7062 training and 1766 testing sets. The proposed model achieved an accuracy of 84.54%. Wheat leaf rust detection at canopy scale was proposed by [PERSON] et al. [57]. The method investigates four methods, v-Support Vector Regression, boosted Regression Trees, Random Forest Regression, and Gaussian Process Regression for the detection and severity estimation of leaf rust disease.