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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.

Agriculture plays a significant role in the economy and its contribution is based on measurable crop yield which is highly dependent upon irrigation. In a country like India, where agriculture is largely based on the unorganized sector, irrigation techniques and patterns followed are inefficient and often lead to unnecessary wastage of water. This calls for the need of a system which can provide an efficient and deployable solution. In this paper, we provide an Automatic Irrigation System based on Artificial Intelligence and Internet of Things, which can autonomously irrigate fields using soil moisture data. The system is based on prediction algorithms which make use of historic weather data to identify and predict rainfall patterns and climate changes; thereby creating an intelligent system which irrigates the crop fields selectively only when required as per the weather and real-time soil moisture conditions. The system has been tested in a controlled environment with an 80 percent accuracy, thus providing an efficient solution to the problem.