Deep Gaussian Convolutional Neural Network Model in Classification of Cassava Diseases using Spectral Data

Abstract

Early disease identification in crops is critical for food security, especially in Sub-Saharan Africa. To identify cassava diseases, professionals visually score the plants by looking for disease indicators on the leaves which is notoriously subjective. Automating the detection and classification of crop diseases could help professionals diagnose diseases more accurately and allow farmers in remote locations to monitor their crops without the help of specialists. Machine learning algorithms have been used in the early detection and classification of crop diseases. However, traditional machine learning algorithms are not calibrated even though they have high accuracy. The ability to provide well-calibrated posterior distributions is one of the most appealing properties of Gaussian processes. Motivated by the current developments in the field of Gaussian Processes, this study proposed a deep Gaussian convolutional neural network model (DGCNN) for the detection and classification of cassava diseases using spectral data. The proposed model uses a hybrid kernel function that is the product of a rational quadratic kernel and a squared exponential kernel. Experimental results revealed that our proposed hybrid kernel function performed better in terms of accuracy of 90.10% when compared to both the squared exponential kernel with an accuracy of 88.01% and the rational quadratic kernel with an accuracy of 88.52%. In our future work, we propose to integrate the Optimised model proposed in this study with the transfer learning approach, a move that may help to improve the model performance.

Classification of cassava leaf diseases using deep Gaussian transfer learning model

“…The results in Table 2 show the performance of the pre-trained transfer learning models. It is evident from 19 as a prediction layer. Three covariance functions were used in this study, that is, hybrid kernel, rational quadratic kernel, and squared exponential kernel.…”

“…in the target domain  T utilizing the knowledge in the source domain  s and the learning task  s , where  s ≠  T , or  s ≠  T . The DGCNN 19 that we previously suggested for the detection and classification of cassava diseases is being integrated with the transfer learning approach in this study.…”

“…16 Traditional (single-layer) GPs have limitations, and a non-parametric deep Bayesian method known as a deep Gaussian process (DGP) uses a hierarchical composition of GPs to address these limitations while maintaining the benefits of GPs. 17,18 We extend our hitherto described technique, termed deep Gaussian convolutional neural network model (DGCNN) 19 in this article, by using a transfer learning strategy. Transfer learning (TL) is "a research subject in machine learning (ML) that focuses on retaining knowledge learned while addressing one problem and applying it to another similar but unrelated problem."…”

“…We extend our hitherto described technique, termed deep Gaussian convolutional neural network model (DGCNN) 19 in this article, by using a transfer learning strategy. Transfer learning (TL) is “a research subject in machine learning (ML) that focuses on retaining knowledge learned while addressing one problem and applying it to another similar but unrelated problem.” The main advantage of TL is that it provides a better and faster response while taking less effort to collect the training data and construct the model 20 .…”

“…Approximations or iterative matrix calculations can be used to address the problem of computational cost 29,30 . This study proposes to combine transfer learning (pre‐trained deep neural networks) and a DGCNN, which is motivated by recent advances in deep learning and deep Gaussian processes 19 . The strength of deep transfer learning and deep Gaussian processes are both utilized by the proposed model.…”