CropCare+: Intelligent Drone for Real-Time Farm Monitoring and Action

References

[1] WeedNet, “Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming,” Anonymous.

[2] WeedMap, “A Large-Scale Semantic Weed Mapping Framework Using Multispectral UAV Imaging,” Anonymous.

[3] J. Hunter et al., “Integration of Remote-Weed Mapping and Autonomous Spraying UAV System,” 2019.

[4] S. P. Singh, G. S. Aujla, and R. S. Bali, “Blockchain for Internet of Drones: Application Challenges and Future Directions,” IEEE Access, vol. 7, pp. 154123–154135, 2019.

[5] R. Singh and S. Kumar, “A Comprehensive Insights into Drones,” International Journal of Engineering Research, 2018.

[6] M. Alami, N. Ahmed, R. Matam, and F. A. Barbhuiya, “IEEE 802.11ah-Enabled Internet-of-Drone Architecture,” IEEE Access, vol. 6, pp. 16820–16830, 2018.

[7] X. Han, N. Kumar, A. Chauhan, and S. K. Soni, “Implementation of Drone Technology for Farm Monitoring and Pesticide Spraying,” 2017.

[8] Monitoring System with Drones and Optical Camera Communication (OCC), Anonymous.

[9] Smart Agriculture Drone for Crop Spraying Using Image Processing and Machine Learning, Anonymous.

[10] AI-Based Drone for Crop Disease Detection in Precision Agriculture, Anonymous.

[11] Applications of Drone for Crop Disease Detection and Monitoring: A Review, Anonymous.

[12] Review on Application of Drone Systems in Precision Agriculture, Anonymous.

[13] S. Sankaran, A. Mishra, R. Ehsani, and C. Davis, “A Review of Advanced Techniques for Detecting Plant Diseases,” Computers and Electronics in Agriculture, vol. 72, no. 1, pp. 1–13, 2010.

[14] J. Z. Zarc-Tejada, J. A. Berni, L. Suárez, G. Sepulcre-Cantó, F. Morales, and J. R. Miller, “Imaging Spectroscopy and Field Spectroscopy for Vegetation Monitoring,” Remote Sensing of Environment, vol. 113, no. 1, pp. 69–82, 2009.

[15] A. McCool, B. A. Thomasson, and J. A. Shaw, “Unmanned Aerial Systems for Precision Agriculture and Environmental Monitoring,” IEEE Instrumentation & Measurement Magazine, vol. 21, no. 4, pp. 14–21, 2018.

[16] L. Tang and G. Shao, “Drone Remote Sensing for Forestry Research and Practices,” Journal of Forestry Research, vol. 26, no. 4, pp. 791–797, 2015.

[17] Y. Huang, W. C. Hoffmann, Y. Lan, W. Wu, and B. K. Fritz, “Development of a Spray System for an Unmanned Aerial Platform,” Applied Engineering in Agriculture, vol. 25, no. 6, pp. 803–809, 2009.

[18] A. Tokekar, J. Vander Hook, D. Mulla, and V. Isler, “Sensor Planning for a Symbiotic UAV and UGV System for Precision Agriculture,” IEEE Transactions on Robotics, vol. 32, no. 6, pp. 1498–1511, 2016.

[19] J. Zhang, M. Wang, and Y. Zhou, “Design and Implementation of an Intelligent Irrigation System Based on IoT,” IEEE Access, vol. 8, pp. 123490–123502, 2020.

[20] R. Bogue, “Sensors for Precision Agriculture,” Sensor Review, vol. 37, no. 4, pp. 386–393, 2017.

[21] M. Hasanalian and A. Abdelkefi, “Classifications, Applications, and Challenges of Drones: A Review,” Progress in Aerospace Sciences, vol. 91, pp. 99–131, 2017.

[22] FAO, “Drones in Agriculture: Technology and Applications,” United Nations, Rome, Italy, Tech. Rep., 2018.

[23] A. Verma and K. Saxena, “Review of autonomous drones for smart farming applications,” IEEE International Conference on Smart Agriculture, pp. 112–117, 2022.

[24] S. Rao and P. Kulkarni, “Design considerations for pesticide spraying drones,” International Journal of Agricultural Science and Technology, vol. 8, no. 3, pp. 145–152, 2021.

@article{1716661,
      author = {Sumit Pandey, Shobita Pawar, Amit Pandey, Dr. Manoj S. Kavedia},
      title = {CropCare+: Intelligent Drone for Real-Time Farm Monitoring and Action},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {2550-2556},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716661.pdf},
      abstract = {This paper presents the design and development of CropCare+, an intelligent drone-assisted system aimed at improving efficiency and sustainability in modern agricultural practices through precision farming techniques. The proposed system integrates targeted pesticide and water spraying, real-time crop health monitoring using image processing, soil moisture sensing, and automated irrigation support through a ground-based control unit. A quadcopter-mounted Raspberry Pi captures crop images using an RGB camera and performs on-device machine learning inference to identify crop stress, pest infestation, and disease conditions.Environmental parameters such as soil moisture, temperature, and humidity are continuously monitored using an ESP32-based ground unit, which controls irrigation sprinklers through relay mechanisms based on real-time field conditions and drone feedback. The system enables precise intervention by triggering localized spraying only in affected areas, thereby reducing chemical usage and conserving water.Experimental evaluations demonstrate that the proposed approach reduces manual labor, minimizes resource wastage, and enhances decision-making through real-time data acquisition and visualization on a remote dashboard. CropCare+ provides a scalable and cost-effective prototype for drone-assisted precision agriculture, bridging the gap between conventional farming practices and intelligent, data-driven farm management systems.},
      keywords = {Drone Technology, Health Monitor, Pesticide Sprinkling, Water Supply Automation.},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1716661}
  }