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.

翻译：机器学习已彻底变革了农业科学领域，尤其是在植物病害的早期检测与管理方面，这对于维持作物健康与生产力至关重要。借助先进算法与成像技术，研究者如今能够以前所未有的准确性和速度识别与分类植物病害。番茄病害的有效管理对提升农业生产率极为关键，而番茄病害分类方法的开发与应用是实现这一目标的核心。本文引入了一种基于最新预训练卷积神经网络（CNN）模型的番茄叶片病害检测与分类前沿技术。我们在张量子空间学习领域提出了一种称为高阶白化奇异值分解（HOWSVD）的精细方法，旨在增强系统的判别能力。我们的张量子空间学习方法系统性地分为两个阶段执行：始于HOWSVD，终于多线性判别分析（MDA）。这一创新方法的有效性通过在两个不同数据集（即PlantVillage和台湾数据集）上的全面实验得到了严格验证。研究结果表明，HOWSVD-MD方法优于现有方法，突显了其显著提升番茄叶片病害诊断精度与可靠性的能力。例如，在PlantVillage和台湾数据集上分别实现了高达98.36%和89.39%的准确率。