The crop yield prediction (CYP) has a high significance in agriculture. Early crop yield predictions assist the farmers, decision-makers in making timely decisions during the actual growing season. In many developing countries such as India, the process of crop yield prediction is done manually, based on surveys and field visits which are time-consuming, expensive and prone to human error. To overcome these drawbacks, we propose a hybrid prediction strategy which can be applied to predict agricultural information such as crop yield and air temperature with a critical focus on crop yield prediction. The weighted principal component analysis (w-PCA) is used as the feature extraction strategy to extract the relevant features. A hybrid prediction strategy integrating artificial neural network (ANN) with modified-particle swarm optimization (m-PSO) is proposed. Initial parameters of ANN are selected using m-PSO with modified inertia weight and velocity update equations. This hybridized ANN then performs prediction on the selected features. This proposed prediction model which we call as hybrid-ANN (H-ANN) comprises of w-PCA as feature extractor, m-PSO for selecting initial weights and biases of ANN. Experiments were performed on eight real world and two benchmark agriculture data sets for crop yield and air temperature prediction. Results show that the proposed prediction model (H-ANN) performed with improvements in the range of 2 to 30%, 0.2 to 4% and 0.12 to 3% with respect to R-squared ($R^2$), root mean square error ($RMSE$) and mean absolute error ($MAE$) respectively when compared to other prediction models such as ANN, ANN trained using GA (GA-ANN), ANN trained using standard PSO (SPSO-ANN), multiple linear regression (MLR), support vector regression (SVR) and ensemble of bagged regression trees (ET) on benchmark and real-world agricultural datasets.