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Sugarcane Disease Identification Using Mobile Deep Learning Solutions | ||
| Journal of Information Technology Management | ||
| دوره 17، Special Issue on SI: Intelligent Security and Management، 2025، صفحه 198-214 اصل مقاله (1.48 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2025.104554 | ||
| نویسندگان | ||
| Ajay Chakravarty* 1؛ Arpit Jain2؛ Ashendra Kumar Saxena3 | ||
| 1Research Scholar, College of Computing Sciences & IT, Teerthanker Mahaveer University, Moradabad. Uttar Pradesh, India. | ||
| 2Prof., Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh, India. | ||
| 3Prof., College of Computing Sciences & IT, Teerthanker Mahaveer University, Moradabad. Uttar Pradesh, India. | ||
| چکیده | ||
| To minimize losses in the agricultural sector and ensure food security, early diagnosis and identification of sugarcane diseases are essential. Conventional diagnostic approaches are often costly, labor-intensive, and reliant on the subjective expertise of individuals in recognizing pathogenic microorganisms. Recent improvements in machine learning and deep learning provide viable solutions for automating the data analysis and classification of plant diseases through image-based analysis. This study presents a comprehensive analysis of image-based sugarcane disease identification systems, emphasizing various computational techniques to achieve optimal results, and applies these methods in a mobile application. In this study, the authors review relevant case studies, highlighting key developments in disease detection using computer vision technologies, and demonstrating how these approaches improve diagnostic accuracy while enhancing computational efficiency and reducing resource consumption. The authors aim to guide future research and development by offering methods to overcome existing challenges. This assessment serves as a resource for academics and practitioners, providing insights into current practices and suggesting ways to enhance automated plant disease detection systems for mobile and handheld devices. | ||
| کلیدواژهها | ||
| Early Detection؛ Sugarcane Disease؛ Machine Learning (ML)؛ Deep Learning (DL)؛ Plant Datasets؛ Disease Category؛ Object Detection؛ Mobile Application | ||
| مراجع | ||
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Singh, M. K., & Kumar, A. (2023). Cucumber Leaf Disease Detection and Classification Using a Deep Convolutional Neural Network. Journal of Information Technology Management, 15(Special Issue: EIntelligent and Security for Communication, Computing Application (ISCCA-2022)). https://doi.org/10.22059/jitm.2023.95248
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