SSCTrac: AI-Powered Soil Moisture Condition Detection
- Conference
- Location
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Minneapolis, MN
- Publication Date
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August 23, 2022
- Start Date
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June 26, 2022
- End Date
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June 29, 2022
- Conference Session
- Page Count
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14
- DOI
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10.18260/1-2--41722
- Permanent URL
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https://peer.asee.org/41722
- Download Count
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437
Paper Authors
Biswajit Biswal South Carolina State University
Dr. Biswal is working as Assistant Professor of Computer Science at South Carolina State University, Orangeburg, SC, USA since January 2017. He holds Ph.D. in Computer and Information Systems Engineering from Tennessee State University, M.S. in Electrical Engineering form NYU Tandon School of Engineering, and B.E. in Medical Electronics Engineering from India. His research interests are AI, machine learning, data mining, cyber security, cloud computing, RF signal detection (Drones), IOT, and big data analysis. He has more than 10 technical papers published in conferences and journals. He is also a senior member of
IEEE.
Abstract
The long-term common goal of high-end agrisystem is to attain sustainable and productive farming at high standards of food quality. Water plays an important role in supplying plant nutrition and a healthy plant root produces quality food. The rapid adoption of Artificial Intelligence (AI) and drones see many precision farming applications such as disease detection from the image, identification of crop-readiness, farming field management, monitoring crop health, soil profile and active irrigation automation. The proposed method is an AI technique – deep learning based noninvasive technique applied to drone captured images of soil to detect soil moisture condition. In our initial evaluation, the proposed AI technique can determine dry or wet soil from drone image. The test is carried out in an agricultural research and demonstration farm to study the feasibility of our proposed method.
Biswal, B. (2022, August), SSCTrac: AI-Powered Soil Moisture Condition Detection Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41722
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