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Prediction of soil is one the important factors in smart farming. The use of near infrared spectroscopy that has exclusive benefit in the forecast of soil moisture content is an appropriate and operative method. Convolutional Neural Network (CNN) is a kind of deep learning model with extraordinary performance. Effective attributes can be mined from the complex spectral data and the internal organisation of attributes can be learned with the help of CNN. When a comparison has been made with conventional models, CNN has influential modelling capability since it has objective of attaining better outcomes in soil prediction for hyperspectral data. CNN is proficient to comprehend the arrangement of hyperspectral data by spatial interpolation. The concept of CNN was utilized to forecast the soil humidity content by near infrared spectroscopy. LUCAS dataset have been adopted in this which have nearly 84 attributes. In order to enhance the results of CNN, EPSO-CNN is proposed. The projected PSO variant is used to optimize the learning hyper-parameters of CNNs to overcome performance barriers. The empirical results have showed that the proposed EPSO CNN achieves better results in terms of precision, recall and accuracy and that too in minimum time.