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Data driven precision agriculture aspects, particularly the dynamic disease management, require dynamic crop-weather-environment data at micro level. An experiment was conducted during four consecutive seasons (2009 Kharif, 2009-10 Rabi, 2010 Kharif and 2010-11 Rabi) in a semi-arid tropic region of India to understand the crop-weather-environment-disease relations using wireless sensory and field-level surveillance data on the groundnut crop for Leaf Spot (LS) disease, which is economically important yet more prone in the semi-arid tropic. Tailor-made various data mining techniques (Naïve Bayes classification with Gaussian distribution, rapid association rule mining and multivariate regression mining) were developed and applied to turn the data into useful information/knowledge/relations/trends and correlation to understand crop-weather-environment-disease continuum. These dynamics obtained from the data mining techniques and trained through proposed multivariate regression (MVR) mathematical models were validated with ground level surveillance data as well as ARI model (derived from ongoing long term weather-based experiment with diversely pooled data experimented from 10 seasons in semi-arid and arid zones). It was found that LS disease infection is strongly influenced by minimum temperature (18-20. °C), prolonged duration of leaf wetness (7-10. h), high humidity (75% or more) and age of the crop. These findings have been used for development of prediction models (One week and cumulative predictions), which can assist the user community to take respective ameliorative measures and it has been found that cumulative prediction model has performed better than ARI model with respect to ground level observations in all 16 diverse dates of sowing experiments spanning for two model-years.

Recent technological developments allowed envisioning sensor devices with distributed ambient sensory network, which could be a potential technology for monitoring various natural phenomena (weather parameters, soil moisture, etc.) at micro level. As days more and more agricultural data are virtually being harvested along with the crops and are being collected/stored in databases, the same data can be used in productive decision making if appropriate data mining techniques are developed/applied. An experiment was conducted with four consecutive (Kharif and Rabi) agricultural seasons in a semi-arid region of India to understand the crop-weather-environment-pest/diseases relations using wireless sensory and field-level surveillance data on closely related and interdependent pest/disease dynamics of groundnut crop. Association rule mining and multivariate regression mining techniques/algorithms were designed/developed/tailor-made to turn the data into useful information/ knowledge/relations/trends to know crop-weather-environment-pest/disease continuum. These findings have been used for development of prediction models (cumulative and non-cumulative) followed by a web based pest/disease decision support system, which will help the decision makers to take viable ameliorative measures

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

Data driven precision agriculture aspects, particularly the pest/disease management, require a dynamic crop-weather data. An experiment was conducted in a semi-arid region to understand the crop-weather-pest/disease relations using wireless sensory and field-level surveillance data on closely related and interdependent pest (Thrips) - disease (Bud Necrosis) dynamics of groundnut crop. Data mining techniques were used to turn the data into useful information/knowledge/relations/trends and correlation of crop-weather-pest/ disease continuum. These dynamics obtained from the data mining techniques and trained through mathematical models were validated with corresponding surveillance data. Results obtained from 2009 & 2010 kharif seasons (monsoon) and 2009-10 & 2010-11 rabi seasons (post monsoon) data could be used to develop a real to near real-time decision support system for pest/disease predictions.