Crop yield prediction is an important aspect of agriculture. The timely and accurate crop yield predictions can be of great help for policy makers and farmers in planning and decision making. Generally, statistical models are employed to predict the crop yield which is time consuming and tedious. Emerging trends of deep learning and machine learning has come up as a major breakthrough in the arena. Deep learning models have the inherent ability to perform feature extraction in large dataset thus more suitable for predictions. In this paper, a deep learning-based Recurrent Neural Network (RNN) model is employed to predict wheat crop yield of northern region of India. The present study also employed LSTM to unravel the vanishing gradient problem inherent in RNN model. Experiments were conducted using 43 years benchmark dataset and proposed model results were compared with three machine learning models. Evidently, the results obtained from RNN-LSTM model(RMSE: 147.12,MAE: 60.50), Artificial Neural Network(RMSE: 732.14,MAE: 623.13), Random Forest (RMSE: 540.88, MAE: 449.36) and Multivariate Linear Regression (RMSE: 915.64,MAE: 796.07), proved the efficacy of model. Also, predicted crop yield values were found to be more close to true values for RNN-LSTM model proving efficiency of the proposed work.

Nature inspired algorithms emulate the mathematical and innovative techniques for non-linear and real life problems worldwide. Imaging technology is emerging out as one of the most prominent and widely used domain in medical field such as cancerous cell nuclei detection, blood vessel segmentation, study of organs or structure of tissues and many more. Nature inspired algorithms emulate the mathematical and innovative techniques for non-linear and real life problems and can be applied to segment or analyse the images. An efficient image segmentation technique may help the subject expert like radiologist, pathologist for early and effective examination or diagnosis of disease. The authors proposed a firefly based segmentation technique that can be employed to segment the breast cancer image regardless the type or modality of the image. The effectiveness of the proposed technique is validated by comparing the procured results with the existing state-of-art techniques.