Crop yield prediction by Mestrial Environ Netsual Network (MENN)
Abstract
Crop yield prediction methods can roughly predict actual yield, although better yield prediction performance is still sought. In the existing methodologies the crop yield prediction outcomes are based on the past experience data and failed to predict the exact outcomes of the crop yield. Hence, a hybrid approach namely Crop yield prediction by Mestrial Environ Netsual Network (MENN) has been proposed to overcome the challenges in the existing approaches and to predict the crop yield with impeccable manner. In previous techniques, the change in phenotype as well as genes in the seed and the plant pathology are not combined as a new model. Hence, Mestrial Neural Network (MNN) has been proposed which consist of Task allocation layer, Subset-net layer and Integrated yield estimation layer to predict the sowing seed gene along with the phenotype and pathology. Also, incorporated pathology module examines the phenotype of respected sowing seed selected for the prediction of yield value. Moreover, while combining the statistical data and image data for the prediction, the generalization ability of prediction model was affected by reason of the images that shared the same timestamp as the statistical data were eliminated as part of the procedure for creating the dataset utilized in the existing approaches. Hence, a novel, Yield Environ Netsual Network (YENN) has been proposed which is consists of two deep networks; (i) Deep Q network (DQN) and (ii) VGG16 for the generalization ability as well as the elimination of data caused by the same timestamp is rectified. Here, VGG-16 is utilized for processing the given input images. As a result, the proposed model well examine the potential disease based on the gene and environment conditions and effectively predict the yield value of crops.
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Authors
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