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

Hamna Waheed, Waseem Akram, Saif ul Islam, Abdul Hadi, Jalil Boudjadar () and Noureen Zafar ()
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Hamna Waheed: University Institute of Information Technology, Pir Mehr Ali Shah, Arid Agriculture University—PMAS AAUR, Rawalpindi 46000, Pakistan
Waseem Akram: Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, Abu Dhabi 127788, United Arab Emirates
Saif ul Islam: Department of Computer Science, Institute of Space Technology, Islamabad 44000, Pakistan
Abdul Hadi: University Institute of Information Technology, Pir Mehr Ali Shah, Arid Agriculture University—PMAS AAUR, Rawalpindi 46000, Pakistan
Jalil Boudjadar: Department of Electrical and Computer Engineering, Aarhus University, 8200 Aarhus, Denmark
Noureen Zafar: University Institute of Information Technology, Pir Mehr Ali Shah, Arid Agriculture University—PMAS AAUR, Rawalpindi 46000, Pakistan

Future Internet, 2023, vol. 15, issue 3, 1-23

Abstract: 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.

Keywords: smart agriculture; deep learning; smartphone application; pests; nutritional deficiency (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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