Efficient agricultural yield prediction using metaheuristic optimized artificial neural network using Hadoop framework.

The low-resolution imagery of satellite is used extensively for monitoring crops and forecasting of yield which has a major role to play in the operational systems. A combination of high levels of temporal frequency along with an extended coverage was connected with lower costs per each area unit making the images a choice that is convenient at the national level and the regional level scales. There are various quantitative and qualitative approaches for low-resolution satellite imagery to be used for the primary predictor of the final yield of crops. But, very little work is done on the yield prediction that is based on environmental and satellite data. To handle such satellite images may be very challenging owing to large data amounts. Big data analysis is efficient in handling a large amount of data generated for predicting agricultural yield. In this work, a neural network is used for prediction and to enhance its performance; a population-based incremental learning technique is proposed for optimizing the weights. The results of the experiment proved that the method proposed has better results compared to that of the other methods. [ABSTRACT FROM AUTHOR]