Agri-analytics is an emerging sector which uses data mining to inform decision making in the agricultural sector. Machine learning is used to accomplish data mining tasks such as prediction, known as predictive analytics in the commercial context. Similar to other domains, hidden trends and events in agri-data can be difficult to detect with traditional machine learning approaches. Deep learning uses architectures made up of many levels of non-linear operations to construct a more holistic model for learning. In this work, we use deep learning for unsupervised modelling of commodity price data in agri-datasets. Specifically, we detect how appropriate input signals contribute to, and interact in, complex deep architectures. To achieve this, we provide a novel extension to a method which determines the contribution of each input feature to shallow, supervised neural networks. Our generalisation allows us to examine deep supervised and unsupervised neural networks.