International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | IoT and AI-Based Early Crop Disease Detection System with Cloud Dashboard for Farmers |
|---|---|
| ABSTRACT | The agriculture is important and critical sector in India, but unfavorable weather conditions continue to damage the crop yields, since the crop diseases tend to go unnoticed until the damage is too severe- this lowers yield and puts farmers into financial strain. A novel research addresses the above problem through integrated use of the advanced technologies, such as IoT and AI, thus to enable the real-time updates for decisions to be made rather than estimates. This research makes use of two parts, which can either monitor the crops using a hardware part, or the resulting information can be visualized on a software part. In the fields, a hardware part focused around an Arduino UNO collects continuously collected data; ambient and soil moisture levels, temperature, rain indicator, and light readings, which can be observed directly on a miniature screen situated within vicinity. Images of crops are passed on to a high resolution image processing network which detects the diseases, often before they are visible to naked eye. In parallel, an XGBoost algorithm utilizes the weather conditions to suggest the type of seed to be used under present conditions. Farmers can obtain useful, easily digestible data on the screen as it is rendered on a web framework and hosted online. Real-time weather data can also be obtained for better irrigation decisions. Feed suggestions can be more precise. Disease control can be implemented through visual observation and detection, rather than estimation. Crop yields can be maximized, and resources can be saved. |
| AUTHOR | VANSHIKA, VARTIKA KHARE, YATI SRIVASTAVA, DR. MONIKA BHATNAGAR Department of ECE, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405006 |
| pdf/6_IoT and AI-Based Early Crop Disease Detection System with Cloud Dashboard for Farmers.pdf | |
| KEYWORDS | |
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