IoT-enabled smart gardening with precise soil type monitoring for horticultural crops
This manuscript introduces an IoT-enabled Smart Gardening system focused on precise soil type monitoring for horticultural crops. The system is designed to adapt to the unique properties of four primary soil types: Sandy, Silty, Loamy, and Clay. By utilizing advanced soil sensors, the system continuously measures critical parameters, including soil moisture, temperature, pH levels, and nutrient concentrations (NPK), specific to each soil type. These real-time environmental metrics are transmitted to a cloud-based platform, enabling data analysis and dynamic decision-making. The system employs predictive algorithms to optimize irrigation, fertilization, and other cultivation processes. Tailored irrigation strategies are applied based on soil type characteristics—high drainage in sandy soils, moisture retention in silty and clay soils, and nutrient-rich loamy soils. By integrating sensor data with cloud computing, the system allows remote monitoring and control, ensuring accurate water and nutrient management. This reduces resource wastage and minimizes the risks of overwatering or under-irrigation, enhancing crop yield and quality. Field trials conducted on various horticultural crops demonstrated significant improvements in water conservation, nutrient management, and overall crop performance across different soil types. The results highlight the system’s ability to increase productivity while reducing environmental impacts. Additionally, the cloud-based infrastructure supports long-term data collection, enabling predictive insights for future cultivation cycles. This IoT-based approach showcases the potential for precision agriculture, driving sustainable practices and efficient resource utilization in horticultural crop management.
Authors
- Shubham Anjankar (ORCID: https://orcid.org/0000-0002-1057-7343)
- A. Parivazhagan
- Abhishek Tripathi (ORCID: https://orcid.org/0000-0003-2048-7453)
- Minjie Rao
- Abhishek Pahuja
- Divyangna Gandhi
Institutions
- Indus University (IN)
- Koneru Lakshmaiah Education Foundation (IN)
- Kalasalingam Academy of Research and Education (IN)
Publication Details
- Journal
- Procedia Computer Science
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.procs.2025.04.101
- Citations
- 1
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 1.75