Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms

• Published in: Neural computing & applications Vol. 31; no. Suppl 1; pp. 277 - 292
• Main Authors: Keswani, Bright, Mohapatra, Ambarish G., Mohanty, Amarjeet, Khanna, Ashish, Rodrigues, Joel J. P. C., Gupta, Deepak, de Albuquerque, Victor Hugo C.
• Format: Journal Article
• Language: English
• Published: London Springer London 09.01.2019; Springer Nature B.V
• Subjects: Agricultural practices; Agriculture; Artificial Intelligence; Artificial neural networks; Best practice; Comparative studies; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data acquisition; Data Mining and Knowledge Discovery; Fuzzy logic; Fuzzy systems; Image Processing and Computer Vision; Internet of Things; Interpolation; Irrigation; Luminous intensity; Measuring instruments; Moisture content; Neural networks; Optimization; Optimization techniques; Probability and Statistics in Computer Science; Remote sensors; S.I. : Machine Learning Applications for Self-Organized Wireless Networks; Sensors; Soil moisture; Soil temperature; Soil water; Temperature sensors; Wireless sensor networks; Wireless sensor network; Fuzzy logic; Internet of things; Gradient descent; Interpolation; Structural similarity index (SSIM); Soil moisture content; Variable learning rate gradient descent
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-018-3737-1

Precision agriculture is the mechanism which controls the land productivity and maximizes the revinue and minimizes the impact on sorroundings by automating the complete agriculture processes. This projected work relies on independent internet of things (IoT) enabled wireless sensor network (WSN) framework consisting of soil moisture (MC) probe, soil temperature measuring device, environmental temperature sensor, environmental humidity sensing device, CO 2 sensor, daylight intensity device (light dependent resistor) to acquire real-time farm information through multi-point measurement. The projected observance technique consists of all standalone IoT-enabled WSN nodes used for timely data acquisitions and storage of agriculture information. The farm history is additionally stored for generating necessary action throughout the whole course of farming. The work summarizes the optimum usage of irrigation by the precise management of water valve using neural network-based prediction of soil water requirement in 1 h ahead. Our proposed irrigation control scheme utilizes structural similarity (SSIM)-based water valve management mechanism which is used to locate farm regions having water deficiency. Moreover, a close comparative study of optimization techniques, like variable learning rate gradient descent, gradient descent for feedforward neural network-based pattern classification, is performed and the best practice is employed to forecast soil MC on hourly basis together with interpolation method for generating soil moisture content (MC) distribution map. Finally, SSIM index-based soil MC deficiency is calculated to manipulate the specified valves for maintaining uniform water requirement through the entire farm area. The valve control commands are again processed using fuzzy logic-based weather condition modeling system to manipulate control commands by considering different weather conditions.