Enhancing Orchard Cultivation Through Drone Technology and Deep Stream Algorithms in Precision Agriculture

The integration of cutting-edge technology in agriculture has revolutionized traditional farming practices, paving the way for Smart Agriculture. This research presents a novel approach to enhancing the cultivation of orchard crops by combining deep-stream algorithms with drone technology. Focusing on pomegranate farming, the study employs a drone system with four specialized cameras: thermal, optical RGB, multi-spectral, and LiDAR. These cameras facilitate comprehensive data collection and analysis throughout the crop growth cycle. The thermal camera monitors plant health, yield estimation, fertilizer management, and irrigation mapping. The optical RGB camera contributes to crop management by analyzing vegetation indices, assessing fruit quality, and detecting weeds. The multi-spectral and hyperspectral cameras enable early detection of crop diseases and assessment of damaged crops. LiDAR aids in understanding crop growth by measuring plant height, tracking phenology, and analyzing water flow patterns. The data collected is processed in real-time using Deep Stream algorithms on an NVIDIA Jetson GPU, providing predictive insights into key farming characteristics. Our model demonstrated superior performance compared to four established models—MDC, MLP, SVM, and ANFIS—achieving the highest accuracy (95%), sensitivity (94%), specificity (96%), and precision (91%). This integrated method offers a robust solution for precision agriculture, empowering farmers to optimize crop management, enhance productivity, and promote sustainable agriculture practices.

@article{Rao2024,
  title     = {Enhancing Orchard Cultivation Through Drone Technology and Deep Stream Algorithms in Precision Agriculture},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {P. Srinivasa Rao and Anantha Raman G R and Madira Siva Sankara Rao and K. Radha and Rabie Ahmed},
  doi       = {10.14569/IJACSA.2024.0150877},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150877}
}