Issue |
SHS Web Conf.
Volume 216, 2025
International Conference on the Impact of Artificial Intelligence on Traditional Economic Sectors (ICIAITES 2025)
|
|
---|---|---|
Article Number | 01007 | |
Number of page(s) | 12 | |
Section | Intelligent Systems and Digital Transformation in Agricultural Economy and Sustainable Development | |
DOI | https://doi.org/10.1051/shsconf/202521601007 | |
Published online | 23 May 2025 |
Implementation and Impact Assessment of Organic Crop Rotation Techniques for Soil Health Improvement in Temperate Agroecosystems
1
Department of computers Techniques engineering, College of technical engineering, The Islamic University, Najaf, Iraq, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq and The Islamic University of Babylon,
Babylon, Iraq
2
Ahl Al Bayt University,
Karbala, KarbalaIraq
3
Department of CSE, GRIET,
Hyderabad,
Hyderabad, Telangana, India
* Corresponding author: ammar.hameed.it@gmail.com
Sustainable cultivation of temperate agroecosystems depends on proper soil health, particularly through organic crop rotation techniques that promote mobilization of soil fertility and productivity. Integration of advanced technologies and data-driven methods is necessary to be able to both implement and evaluate these techniques to be effective. The system focuses on how a comprehensive Decision Support System (DSS) could integrate UAV (Unmanned Aerial Vehicles), automated soil sampling, WSN (Wireless Sensor Networks), and LSTM (Long Short Term Memory) networks concerning the input data. The use of automated soil sampling guarantees the soil health data collection to be precise and consistent, which is necessary to assess the effects of crop rotation. Therefore, UAVs are used for high-resolution aerial images of crop and soil conditions whereas, WSNs deliver real-time data on some critical soil parameters like moisture, temperature, and electrical conductivity. This helps in the integration of these data sources into the DSS for informed decision-making by decisionmakers and in adaptive management. Time series data analysis is performed by LSTM networks to make predictions regarding the trends of soil health as well as to predict the long-term results from crop rotation strategy. The outcomes for this study were that the average increase for Soil Moisture Content was 8%, increased by 10% for Soil Electrical Conductivity, Soil Cation Exchange Capacity improved by 15% and the Soil Respiration Rate improved by 12%. These findings therefore confirm that the organic crop rotation techniques, with advances in monitoring and analysis, are capable of improving soil fertility and sustainability in temperate agroecosystems.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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