RBCA: Context-Aware Yield Forecasting in Agriculture

Most of the developing countries rely on agriculture for their annual growth, a greater section of GDP contribution is affected by its agriculture. Alongside this, major industries rely upon the agricultural to derive their profit. The farmers of many countries are still dependent on classical methods as they are not aware of various technologies and lack significant knowledge in the sector. Consequently, the loss in the agricultural domain accounts for poor quality seed and delayed sowing, environmental hazards, attacks by insects, irrelevant harvesting etc. However, these factors cannot be controlled but can be monitored and an effective strategy can be adopted with the help of the Internet of Things (IoT). IoT-assisted agriculture can help to monitor the conditions and predict the yield which can be useful in taking decision about crops. However, there are no concurrent real-time forecasting strategies that can avail the inputs from different sensors and give parameter-specific or complete forecasting of the yields. To overcome this issue, a Rule-Based Context-Aware (RBCA) framework is proposed that considers a specialized IoT setup to help yield-forecasting of different crops by using available parameters as input. The proposed model uses a probabilistic mapping theory with residual analysis. The model can help to analyze if the given conditions of agriculture-setup are good enough for the growth of a particular crop by using the standard growing environment as matching criteria. Both numerical and realtest data for various crops between the years 1981 and 2012 are used for evaluating the performance of the model.