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Conference Paper
Transforming Crop Drying Operations through IoT and Data-Driven Solutions
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- Authors
- Dasong Yu, Sunghyun Park, Aekyung Moon
- Issue Date
- 2024-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2024, pp.142-147
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Digital agriculture plays a crucial role in enhancing crop management and reducing reliance on manual labor by monitoring crops in real-time and controlling them automati-cally. However, while digital agriculture has made substantial progress, the advancement of labor-intensive agricultural ma-chinery, particularly in the crop dryer machine, has not kept pace, underscoring the imperative for innovation in this domain. This paper proposes a digital crop dryer equipped with IoT sensors and a sophisticated management system, designed to automate and optimize the drying process. This dryer not only monitors real-time data, including temperature, humidity, and crop weights, but also uses a camera to directly observe crop conditions. Furthermore, we have developed a deep learning-based predictive model that uses data analysis to forecast the moisture content of drying crops and determine optimal endpoint of the drying process, achieving a prediction performance with an R2 score of approximately 0.985.
- KSP Keywords
- Crop management, Data analysis, Data-Driven, Digital agriculture, Drying process, IoT sensors, Learning-based, Management system, Predictive model, Real-time data, deep learning(DL)