# A Mobile-Based System for Detecting Ginger Leaf Disorders Using Deep Learning
## 1 Introduction  
The most significant challenges that any crop faces are diseases [1], pests [2], weeds [3], and nutritional deficiencies [4]. For instance, a 20-40% loss occurs due to plant diseases [5] and plant pests [6] annually. Similarly, nutritional deficiency also influences the productivity of agricultural foods [7]. Farmers and domain experts used manual methods for detecting disorders by visualizing the plant's leaf with the naked eye. However, this method became infeasible due to the large size of fields, physical conditions, time, and cost [5]. Therefore, automatic, robust, precise, fast, and cost-effective methods and techniques for plant disorder identification have been demanding research in smart agriculture in recent years. Deep learning has made considerable progress in image-based classification problems [8, 9]. A key benefit of deep learning is that it reduces the effort required for feature extraction, which is time-consuming and requires expertise. In this context, convolutional neural networks (CNN) have gained a lot of success in image classification and object recognition. Deep CNN, an extended version of CNN, has been used in detection, classification, and recognition problems. However, training these models requires considerable training data and computing resources. In the literature, a rise in the use of deep learning-based methods can be noticed in identifying the different diseases associated with crops such as wheat, tomato, cucumber, apple, rice, pearl millet, citrus plants, and cassava. For instance, a [10] study worked on identifying cassava plant disease. In this work, the CNN model was employed on 720 images of the dataset and classified the seven cassava plant diseases and one healthyclass. However, they gained a lower classification rate when tested on real-time image classification. The authors in [2] proposed a deep-learning framework for pest and disease identification problems. They employed a CNN model to diagnose 27 plant diseases. The authors conducted a series of tests that revealed an overall detection accuracy of 86.1%. A DL-based CNN model is trained using 2029 images and detects the five apple diseases on leaves. As they trained on a small dataset, this reported a classification accuracy of 78.8%. Using an open source dataset, PlantVillage, the authors in [11] constructed deep CNN models for plant leaf disease detection. This dataset contains 54,306 images showing a total of 26 diseases collected from different plants in a lab environment. The authors [12] worked on numerous disease detections by considering 12 crops using real-field images. Farmers are also confronted with pest attacks despite plant diseases. Pests are environmental disasters that prevent plants from growing normally or even killing them. When pests attack crops, they leave certain patterns on the leaves. These pest patterns on leaf detection are a very challenging process. Therefore, deep learning is introduced to detect the pest patterns on the leaves of crops [13]. The classification of plant pests using automatic deep learning-based methods have been performed in various studies. The authors in [13] detected the pests of the strawberry plant, which is sown in a greenhouse environment. In this work, a classical machine learning-based support vector machine algorithm is applied to detect the housefly and whitefly pests of the strawberry plant. The authors [14] proposed coffee tree disease identification using different classical and deep learning methods. In this study, InceptionV3 proved challenging for testing the dataset [15]. The study combined saliency techniques and CNNs to build an insect detection system. The system is 92.43% accurate on small datasets and 61.93% accurate on large datasets. A deficiency will emerge when a plant lacks a necessary nutrient for growth and will indicate various signs of defects. Hence, detecting nutrient deficiencies is critical for early diagnosis to avoid severe losses. Deep learning frameworks show their performance in nutrient deficiency recognition [7].