A Mobile-Based System for Detecting Ginger Leaf Disorders Using Deep Learning

The agriculture sector plays a crucial role in supplying nutritious and high-quality food. Plant disorders significantly impact crop productivity, resulting in an annual loss of 33%. The early and accurate detection of plant disorders is a difficult task for farmers and requires specialized knowledge, significant effort, and labor. In this context, smart devices and advanced artificial intelligence techniques have significant potential to pave the way toward sustainable and smart agriculture. This paper presents a deep learning-based android system that can diagnose ginger plant disorders such as soft rot disease, pest patterns, and nutritional deficiencies. To achieve this, state-of-the-art deep learning models were trained on a real dataset of 4,394 ginger leaf images with diverse backgrounds. The trained models were then integrated into an Android-based mobile application that takes ginger leaf images as input and performs the real-time detection of crop disorders. The proposed system shows promising results in terms of accuracy, precision, recall, confusion matrices, computational cost, Matthews correlation coefficient (MCC), mAP, and F1-score.

Automated summary

The agriculture sector is critical for food supply, but plant disorders lead to a significant annual loss in crop productivity. Detecting these disorders accurately and early is a challenge for farmers. This paper presents an Android system utilizing deep learning to diagnose ginger plant disorders, achieving promising results in real-time detection using a trained model on a dataset of ginger leaf images.