# Supervised Learning-Based Image Classification for the Detection of Late Blight in Potato Crops
## 5 Conclusions and Future Work  
This research project shows a mobile system that aims to assist Boyaca-Colombian farmers to identify late blight disease in potato crops of their plants non-invasively by inputting an image of a single plant leaf into the system and producing a early detection result based on the image proccesing.  
In the review of the literature, it is observed that there is no efficient process to detect the presence of the fungus Phytophthoralora Infestans in early stages in potato crops, which  
Figure 14: ROC curves of the Support Vector Machine (SVM) models.  
Figure 13: ROC curve of convolutional neural network (CNN) models.  
is why a new method was proposed based on supervised learning models to detect and diagnose the disease using mobile applications.  
Of the image processing and segmentation algorithms tested, the method with which the best results were obtained in the image background removal process was the HSV color range segmentation algorithm. Regarding classification, the CNN model obtained better results when trained with the augmented data set. On the other hand, the SVM models obtained better results when the algorithm was trained with the color features, showing that the color features play an important role in the development of the SVM classification models. The models were trained and applied to even augmented datasets and validated using typical performance metrics, confusion matrix, ROC, and AUC, showing that the CNN classifier achieved the highest accuracy with 93.2% compared to the SVM classifiers, where the highest accuracy was 87.5%.  
For the integration of the supervised learning model and the use of this in a real space, a mobile application was developed that allows early detection and diagnosis of the presence of the fungus Phytophthora Inrestans from images of potato leaves. In addition, it allows comparing and maintain a history of late blight detection for the sectorized production of crops, which will give way to identify conditions where the crop may be more prone to contagion and infection.  
Currently, the supervised learning model allows to analyze and determine the current state of a potato leaf, and it is expected that future work will work on models that also allow the stem, tuber, and plant to be analyzed completely. Additionally, it would be significant to analyze the crop from video capture in a way that allows farmers to carry out precision agriculture and supports them in making decisions to improve yield by taking preventive and corrective actions against blight disease, late in the potato crop.  
The next step in our project is to create a multi-platform application that can detect the disease of every type of crop and can provide the proper solution for those diseases of the crop in real time. Additionally, we aim to aggregate with more data than the current dataset.  
M.J.S.B. Conducting a research and investigation process, specifically performing the experiments, or data/evidence collection; A.L.G. Programming, software development; designing computer programs; implementation of the computer code and supporting algorithms; testing of existing code components; J.E.E.D. Acquisition of the financial support for the project leading to this publication. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the UPTC, Colombia [SGI Project 3096].  
Not applicable.  
The authors declare no conflict of interest.