Automatic Irrigation System Using Internet of Things

Abstract : The motivation for this project came from the countries where economy is based on agriculture and the climatic conditions lead to lack of rains & scarcity of water . Irrigation is moreover the backbone of Agricultural industry .Due to inadequate knowledge of proper utilization of water resource, lots of water is wastage in the application of irrigation system.So to overcome this problem , it is necessary to make the system automate with the help of modern technology like “Internet Of Things”.

 EXISTING SYSTEM :

 ? The existing water monitoring system used wireless sensors for monitoring the soil condition for irrigation. ? These systems barely capture the data from the land and subsequently controls the electric motor for watering the land. ? The average value is calculated and stored as the final reading for the particular day. ? To eliminate the inter-dependency among the used parameters, the Pearson correlation is computed. ? It is found that there is no strong correlation exists.

 DISADVANTAGE :

 ? Realizing the water scarcity issue and at the same time the technological advancement, we are motivated to design a fully automated irrigation system. ? At present, labor-saving and water-saving technology could also be a key issue in irrigation. ? This paper presents the development of a sensor based smart irrigation system with the capabilities of remote monitoring and controlling of water usage in the agriculture field using Internet of Things (IoT). ? Water waste is a major issue in agriculture. Every time there is an overabundance of water applied to the fields.

 PROPOSED SYSTEM :

 • Solar power-based intelligent irrigation system is proposed to provide the required amount of water to the crop field. • In this work, a smart irrigation system prototype for efficient usage of water and minimal human intervention is proposed. • The proposed recommendation system includes regression of soil and environmental attributes, which are further improved with the help of AC. • As the ML-based models are data hungry, there is a strong intuition that the proposed system will perform even better with more samples.

 ADVANTAGE :

 ? It is Mainly used to make creating graphs faster and more efficient. ? A coordination layer used for capturing the measurements from the physical layer, and sending the measurements to our application. ? This clustering may improve the performance of classical regression by partitioning the sample training space into subspaces. ? The different performance evaluations of the proposed model on our own collected dataset. ? The performance of this system may further improve with experience as new data will be collected. ? The designed system uses fuzzy logic and neural network to save water efficiently.