Abstract
Convolutional neural networks (CNNs) performance requires tuning of network architectures, which requires machine learning knowledge and significant time and effort. Thus, modern deep CNN in soil spectroscopy faces a major barrier as a result of this process. The components in convolutional neural networks (CNNs) for spectroscopic modelling are usually set heuristically before turning the hyperparameters. This can lead to poorly designed CNN models that are not fully optimised, or to models that take a long time to train to derive the optimal model. Although recent work has shown the importance of tuning the CNN hyperparameters, few studies have sought to tune the CNN components and hyperparameters concurrently. We propose such an approach when designing CNNs for soil spectroscopy, and test its effectiveness with the LUCAS soil library and dataset from the Kellogg Soil Survey Laboratory database. This approach has two phases. First, we automated the process of building the fully connected network which involved automating the selection of the different types of layers and the number of neurons per layer. In the second stage, we automated the selection of the hyperparameters for the network layers, and we experimented with two strategies for selecting the best combination. The first strategy involved adapting the population-based training (PBT) technique by replacing the random search used in PBT with a Bayesian optimisation method, which we call adapted-PBT. In the second strategy, we employed the Bayesian optimisation method. Our study revealed that our adapted-PBT was able to achieve high model performance when compared to using Bayesian optimisation in the second stage when predicting soil texture properties for all three predictive measures used (model efficiency coefficient (MCE), Root Mean Squared Error (RMSE), and the ratio of performance to interquartile range (RPIQ)). The results from our modelling were compared with two recent CNN studies that had used the LUCAS dataset and evaluated using the MCE, RMSE and RPIQ. This comparison indicated improvements (5% to 37%) in performance for all three soil properties (sand, silt and clay) in all performance metrics. These findings provide new insights that could help advance the use of CNNs in soil spectroscopy modelling by concurrently combining the building of the CNN components with tuning the hyperparameters.
•This approach simplified the design of ID- CNNs for soil texture prediction.•The outcomes reveal improvements (5% to 26%) in performance for all soil properties.•With population-based training, we achieve higher results in soil texture prediction.