# Supervised Learning-Based Image Classification for the Detection of Late Blight in Potato Crops
## 2 Related Works  
The literature review about this topic shows that in recent years, the application of digital image processing techniques and machine learning models for the detection and classification of diseases in various crops has increased significantly. The studies carried out have obtained disease identification results with success rates greater than 95% in crop disease prediction and classification tasks [5]. [PERSON] and [PERSON] presented a study on disease classification of rice plants using an image-based approach based only on color features. In addition, they tested different classifiers and found that the support vector machine achieved higher accuracy with 94.65% [6]; furthermore, a convolutional neural networks (CNN) model was developed in this work to detect diseases in tomato crops. The proposed model obtained an average precision of 91.2%, demonstrating the effectiveness over previously trained models such as VGG16, InceptionV3, and MobileNet, which obtained an average precision of 77.2%, 63.4%, and 63.75%, respectively. According to [7]. access to smart devices can be used to facilitate automatic diagnosis of diseases in maize crops and prevent losses. They present a method based on deep CNNs for the recognition of diseases in maize leaves, which has the ability to run on independent smart devices such as Raspberry-pi, smartphones, and/or drones with an accuracy of 88.46% [8]. Likewise, [PERSON] and [PERSON] carry out a study with the purpose of implementing a CNN model capable of identifying five diseases in tomato crops on smartphones, and the results obtained show an accuracy greater than 95.54% [9].  
However, it has been observed that most of the studies in the literature diagnose different diseases such as _gota or tizon tardio_ for a particular plant or several plants [10]. In this study, a CNN architecture is proposed that will be compared with SVM for the diagnosis of gout disease or late blight produced by the fungus phytophthora infestans present in potato crops. The main goal is to find the architecture with the best performance in identification and classification of the disease under study and using the created dataset and the deployment using a mobile app.  
The results show the effectiveness of supervised learning models in detecting healthy and infected leaves with a significant reduction in human efforts to detect the disease especially in large crops, and the strengthening of agricultural productivity in the region. Finally, the development of a mobile app for precision agriculture supports the farmers of the region with increased agriculture productivity.  
This work proposes a supervised learning model based on the SVM and CNN algorithms to classify and detect late blight disease in potato crops. This model was implemented in a mobile application to support the farmers in defining strategies to control this disease exhibited by their crops. In this work, the proposed deep-learning approach helps with an accurate identification of the common types of leaf diseases related with late blight in potato crops.  
This article is organized as follows: The literature review conducted on machine learning and deep-learning models is presented in Section 2. Section 3 describes the material and methods used, the CNN architectures created, a comparation with SVM, and the evaluation metrics; Section 4 contains the results and the discussion regarding the different architectures; and finally, the final conclusions are reported in Section 5.