Automated Irrigation System Using Improved Fuzzy Neural Network in Wireless Sensor Networks

Abstract

Irrigation plays a significant role in various agricultural cropping methods deployed in semiarid and arid regions where valuable water applications and managing are considered crucial concerns. Multiple factors such as weather, soil, water, and crop data need to be considered for irrigation maintenance in an efficient besides uniform manner from multifaceted and different information-based systems. A Multi-Agent System (MAS) has been proposed recently based on diverse agent subsystems with definite objectives for attaining global MAS objective and is deployed on Cloud Computing paradigm capable of gathering information from Wireless Sensor Networks (WSNs) positioned in rice, cotton, cassava crops for knowledge discovery and decision making. The radial basis function network has been used for irrigation prediction. However, in recent work, the security of data has not focused on where intruder involvement might corrupt the data at the time of data transferring to the cloud, which would affect the accuracy of decision making. To handle the above mentioned issues, an efficient method for irrigation prediction is used in this work. The factors considered for decision making are soil moisture, temperature, plant height, root depth. The above-mentioned data will be gathered from the sensors that are attached to the crop field. Sensed data will be forwarded to the local server, where data encryption will be performed using Adaptive Elliptic Curve Cryptography (AECC). After the encryption process, the data will be forwarded to the cloud. Then the data stored in the cloud will be decrypted key before being given to the decision-making module. Finally, the uniform distribution-based fuzzy neural network is formulated based on the received data information in the decision-making module. The final decision regarding the level of water required for crop fields would be taken. Based on this outcome, the water volve opening duration and the level of fertilizers required will be considered. Experimental results demonstrate the effectiveness of the proposed model for the United States Geological Survey (USGS) database in terms of precision, accuracy, recall, and packet delivery ratio.