Mining Intelligent Patterns using SVAC for Precision Agriculture and Optimizing Irrigation (Student Abstract)

Authors

  • Vishal Vinod Sir M. Visvesvaraya Institute of Technology
  • Vipul Gaurav Sir M. Visvesvaraya Institute of Technology
  • Tushar Sharma Sir M. Visvesvaraya Institute of Technology
  • Savita Choudhary Sir M. Visvesvaraya Institute of Technology

Keywords:

Object Detection, Crop Counting, Time Series Forecasting, Machine Learning, Hyperspectral Data, Precision Agriculture, Climate Change, Sustainable Intelligent Agriculture

Abstract

The ability to leverage the advances in precision agriculture, computer vision, and edge devices can immensely benefit sustainable agriculture yield. Utilizing the available resources to their maximum requires reliable and intelligent real-time insights to optimize and automate the current agriculture infrastructure. In some countries with low internet penetration rates, such systems need offline and extremely efficient edge deployments. We propose a framework that attends to the trifecta of (i) predicting crop water requirements and irrigating the land appropriately, (ii) providing intelligent insights from aerial images and sensor data for crop management that is fully offline, and (iii) effective post-training quantization and model pruning that leverage the lottery ticket hypothesis - an arbitrarily instantiated network containing a subnetwork that when trained independently will perform as well as the original full network, trained for a similar number of cycles for shrinking the machine learning models and improving latency on the edge.

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Published

2021-05-18

How to Cite

Vinod, V., Gaurav, V., Sharma, T., & Choudhary, S. (2021). Mining Intelligent Patterns using SVAC for Precision Agriculture and Optimizing Irrigation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15909-15910. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17951

Issue

Section

AAAI Student Abstract and Poster Program