# A Mobile-Based System for Detecting Ginger Leaf Disorders Using Deep Learning
## 5 Conclusions  
This study captured a unique dataset containing the 4396 digital images of ginger plant disorders at the early and last stages with congested backgrounds, poor contrast, and under various illuminating conditions. We presented the design and deployment of different deep-learning classification and detection models to classify and detect ginger plant disorders. Furthermore, an android application for ginger plant disorders detection was developed. Various experiments were conducted to determine the functionality and classification performance of the system. The following conclusions were made based on the research conducted on the classification and detection of disorders on ginger plant leaves and their implementation in mobile applications:  
* This research was trained on classification models such as CNN, VGG-16, and MobileNetV2 using the digital images of ginger plant disorders such as soft rot disease, pest patterns, nutrient deficiency, and healthy leaves. The VGG-16 model exhibited the best results in classifying the ginger plant's disorders. The performance metrics prove that the VGG-16 and CNN models showed the highest results.
* The ginger plant disorders were also trained on the object detection model YOLOv5 and gained an average mAP50 value of 80%, 80%, and 79% for pest pattern, nutrient deficiency, and soft rot disease, respectively. The YOLOv5 model detects and localizes the affected part of the ginger from the digital image in a real-time environment.  
Figure 22: Set 2 of visual examples showcasing the qualitative evaluation of the YOLOv5 model on the ginger dataset.  
* A deep-learning-enabled mobile-based system for detecting ginger leaf disorders has several benefits over the status quo, including: **Higher accuracy:** Deep learning models are trained on a significant amount of data, which results in improved accuracy in comparison to the traditional methods. **Cost-effectiveness:** The application can be used on a smartphone and does not require any expensive equipment for the detection of ginger leaf disorders. **Rapid detection:** The application facilitates the real-time detection of disorders, which allows farmers to take preventive measures in a timely manner before the spread of disease. **Accessibility:** The smartphone application facilitates the system's accessibility so that farmers in remote and underprivileged areas can easily recognize ginger plant disorders without any specialized training or dedicated equipment. In the future, we aimed to increase the dataset by adding more classes. Moreover, detecting multiple diseases that affect leaves, stems, roots, and rhizomes with the localization of the affected area will be another potential future research direction.  
Conceptualization, N.Z. and H.W.; methodology, H.W., W.A. and A.H.; software, H.W.; validation, N.Z., H.W., A.H., J.B., S.u.I. and W.A.; formal analysis, N.Z., H.W., W.A., A.H. and J.B.; investigation, N.Z., H.W., A.H. and W.A.; resources, N.Z. and H.W.; data curation, N.Z. and H.W.; writing--original draft preparation, N.Z., H.W., W.A. and S.u.I.; writing--review and editing, N.Z., H.W., W.A., A.H., J.B. and S.u.I.; visualization, N.Z., H.W., W.A., A.H., S.u.I. and J.B. All authors have read and agreed to the submitted version of the manuscript. This research received no external funding. The data presented in this study are available upon request from the authors. The authors would like to thank [PERSON] and [PERSON] from Arid Agriculture University, Rawalpindi, Pakistan, for their valuable discussions and assistance. The authors declare no conflict of interest.