Fuzzy deep learning-based crop yield prediction model for sustainable agronomical frameworks

The evolution in science and innovation has to lead to an immense volume of information from various agricultural fields to be accumulated in the public domain. As a result, an objective arises from the investigation of the accessible information and incorporating them with processes like foreseeing crop yield, plant diseases examination, crops enhancement, etc. Machine learning has grown with tremendous processing methods to conceive new innovations in the multi-disciplinary agricultural sector. In experimenting with machine learning models, there exist certain limitations like improvident nonlinear mapping between the raw data and crop yield values. Hence, deep learning models are comprehensively used to extricate critical crop parameters for prediction. Foreseeing the crop yield depending on climate, soil and water parameters has been a potential research subject. This paper proposes a hybrid deep learning-based crop yield prediction system using deep belief network (DBN) and fuzzy neural networks system (FNN). DBN is a combination of statistics and probability with neural networks. Though DBN performs better for nonlinear systems, the algorithm alone cannot provide satisfactory results in terms of robustness, model accuracy and learning speed, which is predominantly due to gradient diffusion. Hence, a DBN along with FNN has been proposed to overcome the nonlinearity and gradient diffusion problems. The proposed model initially performs an efficient pre-training technique by DBN for enhanced model development and feature vector generation. This characteristic feature vector is fed as an input to the FNN for further processing. The superiority of the proposed fuzzy neural network-based deep belief network is analyzed by comparing it with other deep learning algorithms. The proposed model efficiently predicts the results outperforming the other models by preserving the original data distribution with an accuracy of 92%.