Abstract:
Machine learning continues to transform how plant diseases are identified and
classified, offering scalable solutions for timely crop health monitoring. Despite
significant progress, many conventional models struggle with quantifying predictive
uncertainty, a critical requirement in decision-sensitive domains such as agriculture.
Gaussian Processes (GPs), known for their ability to provide calibrated predictions,
offer a principled way to address this gap. This study leverages the strengths of Deep
Gaussian Processes (DGPs), Convolutional Neural Networks (CNNs), and Transfer
Learning to improve cassava disease classification. The research aimed to address the
following objectives: To Evaluate existing Gaussian Process models and identify
their architectural strengths, limitations, and suitability for cassava disease
classification; to design and implement a Deep Gaussian Convolutional Neural
Network (DGCNN) that integrates GPs and CNNs; to develop a Deep Gaussian
Transfer Learning (DGTL) model combining DGPs with pre-trained CNN
architectures; and to assess the performance of the proposed models against standard
machine learning baselines. A hybrid kernel, formed by combining rational quadratic
and squared exponential kernels, was also introduced to enhance model accuracy and
expressiveness. Experimental results showed that the DGCNN achieved an accuracy
of 90.1%, outperforming models using standard kernels. The DGTL model, when
integrated with MobileNetV2 and the hybrid kernel, achieved 90.11% accuracy,
surpassing other configurations. Although computational constraints were
encountered due to limited hardware resources, the proposed models demonstrated
strong predictive performance while maintaining the probabilistic interpretability of
Gaussian Processes. These findings contribute to the advancement of intelligent and
reliable tools for precision agriculture.
Key words: Cassava Disease Classification, Deep Gaussian Processes (DGPs),
Convolutional Neural Networks (CNNs), Transfer Learning, Precision Agriculture.
