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.

利用机器学习及深度学习方法，在农业科学中实现对番茄叶病的自动检测和分类技术的研究。通过提出基于高阶白化奇异值分解方法（HOWSVD）和多线性判别分析（MDA）的张量子空间学习方法，测试结果表明该方法在 PlantVillage 和台湾数据集上取得了显著提升的精确度和可靠性，达到了 98.36％和 89.39％的准确率。

增强植物病害识别：一种基于卷积神经网络、张量子空间学习和 HOSVD-MD 的新方法

Enhancing Plant Disease Detection: A Novel CNN-Based Approach with  Tensor Subspace Learning and HOWSVD-MD

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has
assembled 193,634 labelled wheat heads from 4,700 RGB images acquired from
various acquisition platforms and 7 countries/institutions. With an associated
competition hosted in Kaggle, GWHD has successfully attracted attention from
both the computer vision and agricultural science communities. From this first
experience in 2020, a few avenues for improvements have been identified,
especially from the perspective of data size, head diversity and label
reliability. To address these issues, the 2020 dataset has been reexamined,
relabeled, and augmented by adding 1,722 images from 5 additional countries,
allowing for 81,553 additional wheat heads to be added. We now release a new
version of the Global Wheat Head Detection (GWHD) dataset in 2021, which is
bigger, more diverse, and less noisy than the 2020 version. The GWHD 2021 is
now publicly available at this http URL and a new data challenge
has been organized on AIcrowd to make use of this updated dataset.

通过重新审查、重新标记并添加额外的数据，2021 年的全球小麦穗检测（GWHD）数据集变得更大、更多样化、更可靠，吸引了计算机视觉和农业科学社区的关注，并在 AIcrowd 上组织了新的数据挑战。