Al-Shamayleh Ahmad Sami, Ibrahim Rabha W
Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Al-Ahliyya Amman University, Al-Salt, Amman 19328, Jordan.
Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University 64001, Thi Qar, Iraq.
MethodsX. 2025 Sep 10;15:103619. doi: 10.1016/j.mex.2025.103619. eCollection 2025 Dec.
Today, one of the most important first steps in attaining sustainable agriculture and guaranteeing food security is the detection of plant diseases. Quantitative analysis of plant physiology is now feasible thanks to developments in computer vision and imaging technologies. On the other hand, manual diagnosis requires a lot of work and in-depth plant pathology knowledge. Numerous innovative methods for identifying and classifying plant diseases have been widely used. In this study, we propose a novel hybrid classification method that combines (q,τ)-Nabla calculus quantum deformation-based features with deep learning feature representations to classify diseases in grapevine leaves. The methodology of this study relies on:•Nabla calculus quantum deformation features are utilized to extract robust handcrafted features that capture local texture and structural variations associated with disease symptoms.•Deep features are extracted using a pre-trained convolutional neural network, which captures high-level semantic information from leaf images.The concatenated feature vectors are then fed into a machine learning classifier for final prediction. Test results on a dataset of grapevine leaf disease show that the proposed method outperforms individual approaches, in accuracy. The proposed method helps minimize financial losses and support effective plant disease management, thereby improving crop yield and contributing to food security.
如今，实现可持续农业和保障粮食安全最重要的首要步骤之一是检测植物病害。由于计算机视觉和成像技术的发展，现在对植物生理学进行定量分析是可行的。另一方面，人工诊断需要大量工作以及深入的植物病理学知识。许多用于识别和分类植物病害的创新方法已被广泛使用。在本研究中，我们提出了一种新颖的混合分类方法，该方法将基于（q，τ）-nabla 演算量子变形的特征与深度学习特征表示相结合，以对葡萄叶片中的病害进行分类。本研究的方法基于：
• 使用 nabla 演算量子变形特征来提取强大的手工特征，这些特征能够捕捉与病害症状相关的局部纹理和结构变化。
• 使用预训练的卷积神经网络提取深度特征，该网络从叶片图像中捕捉高级语义信息。
然后将连接后的特征向量输入到机器学习分类器中进行最终预测。在葡萄叶片病害数据集上的测试结果表明，所提出的方法在准确率方面优于单独的方法。所提出的方法有助于将经济损失降至最低，并支持有效的植物病害管理，从而提高作物产量并促进粮食安全。
