# Machine Learning in Cereal Crops Disease Detection: A Review
## 2 Related Literature Review  
In this section, we will discuss the latest survey/review works undertaken on the application of machine learning in the overall field of agriculture--from disease detection to crop growth monitoring. This section contains papers excluded from the reviewed publications according to selection criteria discussed under Section 4.  
A review of deep learning and visualization techniques for the detection and classification of plant diseases was discussed by [PERSON] et al. (2019). The authors discussed the most used deep learning architectures, public datasets, and performance metrics used for the task of plant disease detection.  
A systematic literature review on the use of convolutional neural networks for the detection and classification of plant diseases was presented by [PERSON] et al. (2019). The authors reviewed 121 works performed on the area of plant disease detection using deep learning techniques for the past ten years (2010-2019). Based on their systematic survey, the authors identified PlantVillage (2018) as the most commonly used dataset and TensorFlow as the commonly used deep learning framework.  
A review in the application of machines in the detection of non-destructive defects in horticultural products was discussed by [PERSON] and [PERSON] (2018). They discussed the most common and damaging types of defects that occur to fruit and vegetable products due to pre and post-harvest practices, handling and storage conditions, and pathogens. The authors conducted a detailed summary of the different types of machine learning algorithms and the various sensing techniques. They concluded that machine learning and deep learning methods have shown a good result in overcoming challenges of effective, objective, and fast detection of defects in horticultural products.  
[PERSON] et al. (2019) undertook a systematic literature review of 567 studies performed on crop yield prediction. Their work aims at finding the latest algorithms and most common features used for the problem of crop yield prediction using machine learning. From this systematic literature review, they found that temperature, rainfall, and soil type are the most common features, and Artificial Neural Network(ANN) is the most applied algorithm.  
[PERSON] et al. (2019) discussed deep learning approaches for the detection and classification of weeds. The authors presented a detailed review of the data acquisition, dataset preparation, deep learning architectures, and evaluation metrics used for the task of detection, classification, and localization of weeds.  
[PERSON] et al. (2019) performed a systematic review of 84 works of literature on the application of deep learning in plant disease detection. Their review aims at identifying the best datasets for various plants, deep learning models, and pre-processing techniques. They found that most deep learning models are limited in processing original unaltered image data and that an appropriate pre-processing algorithm is required for good model performance.  
As it can be observed in Table 1, most of the works conduct a detailed discussion on machine learning approaches for the detection of plant diseases in general and miss out on almost all major cereals. This work attempts to fill in this gap by dedicating the whole content to works conducted on machine learning-based cereal crop detection and identifying all available datasets.