  Machine learning has revolutionized the field of agricultural science,
particularly in the early detection and management of plant diseases, which are
crucial for maintaining crop health and productivity. Leveraging advanced
algorithms and imaging technologies, researchers are now able to identify and
classify plant diseases with unprecedented accuracy and speed. Effective
management of tomato diseases is crucial for enhancing agricultural
productivity. The development and application of tomato disease classification
methods are central to this objective. This paper introduces a cutting-edge
technique for the detection and classification of tomato leaf diseases,
utilizing insights from the latest pre-trained Convolutional Neural Network
(CNN) models. We propose a sophisticated approach within the domain of tensor
subspace learning, known as Higher-Order Whitened Singular Value Decomposition
(HOWSVD), designed to boost the discriminatory power of the system. Our
approach to Tensor Subspace Learning is methodically executed in two phases,
beginning with HOWSVD and culminating in Multilinear Discriminant Analysis
(MDA). The efficacy of this innovative method was rigorously tested through
comprehensive experiments on two distinct datasets, namely PlantVillage and the
Taiwan dataset. The findings reveal that HOWSVD-MDA outperforms existing
methods, underscoring its capability to markedly enhance the precision and
dependability of diagnosing tomato leaf diseases. For instance, up to 98.36\%
and 89.39\% accuracy scores have been achieved under PlantVillage and the
Taiwan datasets, respectively.
