Ranpariya et al. (2026) Smart Agriculture System for Optimal Crop Management Integrating AI, ML, IoT Technologies and UAVs

Identification

• Journal: Lecture notes in networks and systems
• Year: 2026
• Date: 2026-01-01
• Authors: Akshay Ranpariya, Madhu Shukla
• DOI: 10.1007/978-3-032-08246-6_20

Research Groups

Department of CSE - AI, ML & DS, Marwadi University, Rajkot, Gujarat, India

Short Summary

This study proposes a Smart Agriculture system integrating AI, ML, IoT, and UAVs to enhance precision crop management, improve plant disease identification, optimize resource use, and predict yields, while promoting environmental sustainability through solar energy utilization.

Objective

• To create a comprehensive Smart Agriculture system that leverages AI, ML, IoT, and UAV technologies to improve the precision of crop management practices, facilitate plant disease identification, monitor plant health, automate irrigation, and predict optimal crop yields.

Study Configuration

• Spatial Scale: Individual plants, crop fields, and agricultural areas.
• Temporal Scale: Real-time monitoring of plant health and environmental conditions, continuous data collection, and prediction of future yields.

Methodology and Data

• Models used: Artificial Intelligence (AI), Machine Learning (ML) techniques, image processing.
• Data sources: Internet of Things (IoT) sensors (for plant health, irrigation), Unmanned Aerial Vehicles (UAVs) with multispectral imagers (for aerial images of crop condition), historical agricultural data, real-time sensor data, weather data.

Main Results

• The developed system improves the identification of plant diseases through image processing and machine learning.
• It enables continuous monitoring of plant health using IoT sensors.
• Automated irrigation is performed based on sensor data and weather predictions, optimizing water usage.
• UAVs with multispectral imagers provide data for analyzing crop condition, including nutrient deficiencies and pest proliferation, allowing for focused application of fertilizers and pesticides.
• The system facilitates timely problem resolution through efficient field scouting and monitoring, acting as a quasi-expert system.
• Optimal yields are predicted by algorithmically relating historical data, real-time sensor data, and weather data.
• The system incorporates solar energy utilization, contributing to environmental sustainability.

Contributions

• Integration of a comprehensive suite of advanced technologies (AI, ML, IoT, UAVs) into a single smart agriculture system for holistic crop management.
• Enhanced precision in identifying plant diseases and monitoring plant health through image processing and sensor data.
• Optimization of resource use (fertilizers, pesticides, water) by providing targeted application based on real-time and aerial data.
• Development of a system that predicts optimal yields by combining diverse data sources, offering a proactive approach to farming.
• Emphasis on environmental sustainability through the utilization of solar energy and minimized waste.

Funding

[No specific funding projects, programs, or reference codes were mentioned in the provided paper text.]

Citation

@article{Ranpariya2026Smart,
  author = {Ranpariya, Akshay and Shukla, Madhu},
  title = {Smart Agriculture System for Optimal Crop Management Integrating AI, ML, IoT Technologies and UAVs},
  journal = {Lecture notes in networks and systems},
  year = {2026},
  doi = {10.1007/978-3-032-08246-6_20},
  url = {https://doi.org/10.1007/978-3-032-08246-6_20}
}