# A Mobile-Based System for Detecting Ginger Leaf Disorders Using Deep Learning
## 1 Introduction
In recent studies, work on nutrient deficiency classification problems can also be seen. The goal of the work [7] was to provide a thorough review of the methods utilized to identify plant nutrient deficiencies using digital images. The authors in [16] detected nitrogen deficiencies in one variety of rice plant, capturing an image of 5 megapixels and gaining 0.92% accuracy. The authors in [17] detected seven nutrient deficient types with the ResNet-50 model, having 4,088 images of black gram and showing an accuracy of 65.44%. By combining inception-ResNet and the autoencoder, the system accurately identified the three nutritional deficiencies for 571 tomato plant images and obtained a 91% test accuracy. However, the dataset they employed is limited in scope, only covering N, Ca, and K nutrients [18]. The research community has presented extensive work on plant defect identification and recognition problems in recent decades. However, there is still a need for work on ginger plant defect identification, recognition, and classification. To cover the existing issues of the ginger plant, we used various deep-learning models to classify ginger plant-associated disorders such as pest pattern, nutrient deficiency, and soft rot disease. This work is an extension of our previous work on the ginger plant [19]. In the former study, we proposed identifying and classifying ginger plant soft rot disease, nutritional deficiencies, and pest patterns at early as well as multiple stages using different deep learning-based models such as CNN, VGG-16, MobileNetV2, and ANN. The proposed deep learning models were trained and tested on the dataset of ginger plant leaf images consisting of healthy plants, pest patterns, nutrient deficiency, and soft rot disease acquired from an entire field of a standing crop. The study analyzed the performance and capability of the deep learning methods for ginger plant disorder detection. Here, the previous work is extended by considering the identification and classification of ginger plant disorders in real time. We developed an automatic, android-based detection system that takes the leaf images of the crop in the field as input and provides real-time identification results. Moreover, the system also provides a recommendation to the end user based on the detectedresults. In addition, this study presented an in-depth analysis of the system's performance in terms of timing complexity and accuracy. The key contributions of this paper are given as follows:  
* Creating a large dataset of ginger plant leaf images containing patterns of health, pests, nutrient deficiency, and soft rot disease. * Presenting ginger plant pest patterns, nutritional deficiencies, and soft rot disease identification by applying deep learning classification and detection models such as CNN, MobileNetV2, VGG-16, and YOLOv5. * Analyzing the performance of the proposed models in terms of time complexity and accuracy under different conditions. * Validating a deep learning-based detection platform that executes on smartphones in a real-time environment, generates identification results based on the given input, and recommends appropriate actions to the farmers. The rest of the paper is organized as follows. Section 2 reviews the related work. Section 3 discusses the proposed methods, followed by obtaining results and discussion in Section 4, and finally, Section 5 concludes the paper.