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Recent technological developments allowed to envision the low-power (solar power) and low-cost (open hardware) sensor devices (Agrisens/FieldServer/Flux Tower/FieldTwitter) with multimode (ZigBee/WiFi/3G/WebGIS) information and communication technologies (ICTs), a model in which is christened as GeoSense. Integrating these multimode and multi-level communication systems with distributed ambient sensory network location-based service (LBS) is a challenging task, which could be a potential technology for monitoring various natural phenomena. This integrated model is introduced to provide and assist the rural stakeholders with real-time decision support system (DSS) with dynamic information and modeling services for precision agriculture through GeoSense cloud service. This GeoSense research has been experimented in semiarid tropics in India under Indo-Japan initiative on multi-disciplinary ICT program.

The objective of this study was to compare two different rice simulation models--standalone (Decision Support System for Agrotechnology Transfer [DSSAT]) and web based (SImulation Model for RIce-Weather relations [SIMRIW])--with agrometeorological data and agronomic parameters for estimation of rice crop production in southern semi-arid tropics of India. Studies were carried out on the BPT5204 rice variety to evaluate two crop simulation models. Long-term experiments were conducted in a research farm of Acharya N G Ranga Agricultural University (ANGRAU), Hyderabad, India. Initially, the results were obtained using 4 years (1994-1997) of data with weather parameters from a local weather station to evaluate DSSAT simulated results with observed values. Linear regression models used for the purpose showed a close relationship between DSSAT and observed yield. Subsequently, yield comparisons were also carried out with SIMRIW and DSSAT, and validated with actual observed values. Realizing the correlation coefficient values of SIMRIW simulation values in acceptable limits, further rice experiments in monsoon (Kharif) and post-monsoon (Rabi) agricultural seasons (2009, 2010 and 2011) were carried out with a location-specific distributed sensor network system. These proximal systems help to simulate dry weight, leaf area index and potential yield by the Java based SIMRIW on a daily/weekly/monthly/seasonal basis. These dynamic parameters are useful to the farming community for necessary decision making in a ubiquitous manner. However, SIMRIW requires fine tuning for better results/decision making.

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