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%.

科学和创新的发展必须导致大量来自各个农业领域的信息积累到公共领域。结果，通过对可访问信息的调查，并将其与诸如预测作物产量，植物病害检查，作物增产等过程结合起来，产生了一个目标。机器学习已经发展出了巨大的处理方法，可以在多学科领域中构想出新的创新。农业部门。在机器学习模型的实验中，存在某些局限性，例如原始数据和农作物产量值之间的即兴非线性映射。因此，深度学习模型被全面用于提取关键作物参数以进行预测。预测取决于气候的农作物产量，土壤和水的参数已成为潜在的研究课题。本文提出了一种使用深度信念网络（DBN）和模糊神经网络系统（FNN）的基于深度学习的混合作物产量预测系统。DBN是统计和概率与神经网络的结合。尽管DBN在非线性系统中表现更好，但是仅靠算法的鲁棒性，模型准确性和学习速度，还是不能提供令人满意的结果，这主要是由于梯度扩散所致。因此，已经提出了将DBN与FNN一起克服非线性和梯度扩散问题。所提出的模型最初通过DBN执行有效的预训练技术，以增强模型开发和特征向量生成。将该特征特征向量作为输入馈入FNN，以进行进一步处理。通过将其与其他深度学习算法进行比较，分析了所提出的基于模糊神经网络的深度置信网络的优越性。所提出的模型通过保留原始数据分布（准确度为92％）而有效地预测了优于其他模型的结果。