Al-Shamasneh Ala'a R, Ibrahim Rabha W
Department of Computer Science, College of Computer & Information Sciences, Prince Sultan University, Rafha Street, Riyadh 11586, Saudi Arabia.
Faculty of Engineering and Natural Sciences, Advanced Computing Lab, Istanbul Okan University, 34959, Türkiye.
MethodsX. 2024 Jul 3;13:102844. doi: 10.1016/j.mex.2024.102844. eCollection 2024 Dec.
Plant diseases can spread rapidly, leading to significant crop losses if not detected early. By accurately identifying diseased plants, farmers can target treatment only to the affected areas, reducing the number of pesticides or fungicides needed and minimizing environmental impact. Tomatoes are among the most significant and extensively consumed crops worldwide. The main factor affecting crop yield quantity and quality is leaf disease. Various diseases can affect tomato production, impacting both yield and quality. Automated classification of leaf images allows for the early identification of diseased plants, enabling prompt intervention and control measures. Many creative approaches to diagnosing and categorizing specific illnesses have been widely employed. The manual method is costly and labor-intensive. Without the assistance of an agricultural specialist, disease detection can be facilitated by image processing combined with machine learning algorithms. In this study, the diseases in tomato leaves will be detected using new feature extraction method using conformable polynomials image features for accurate solution and faster detection of plant diseases through a machine learning model. The methodology of this study based on:•Preprocessing, feature extraction, dimension reduction and classification modules.•Conformable polynomials method is used to extract the texture features which is passed classifier.•The proposed texture feature is constructed by two parts the enhanced based term, and the texture detail part for textual analysis.•The tomato leaf samples from the plant village image dataset were used to gather the data for this model. The disease detected are 98.80 % accurate for tomato leaf images using SVM classifier. In addition to lowering financial loss, the suggested feature extraction method can help manage plant diseases effectively, improving crop yield and food security.
植物病害传播迅速，如果不及早发现，会导致农作物严重减产。通过准确识别患病植株，农民可以仅对受影响区域进行针对性治疗，减少所需农药或杀菌剂的使用量，并将对环境的影响降至最低。番茄是全球最重要且消费广泛的作物之一。影响作物产量和质量的主要因素是叶部病害。多种病害会影响番茄生产，对产量和质量都造成影响。叶片图像的自动分类有助于早期识别患病植株，从而能够迅速采取干预和控制措施。许多用于诊断和分类特定病害的创新方法已被广泛应用。人工方法成本高且劳动强度大。在没有农业专家协助的情况下，结合机器学习算法的图像处理可以促进病害检测。在本研究中，将使用基于共形多项式图像特征的新特征提取方法来检测番茄叶片中的病害，以通过机器学习模型准确解决并更快地检测植物病害。本研究的方法基于：•预处理、特征提取、降维和分类模块。•使用共形多项式方法提取纹理特征并将其传递给分类器。•所提出的纹理特征由两部分构成，即增强型项和用于文本分析的纹理细节部分。•使用植物村图像数据集中的番茄叶片样本为该模型收集数据。使用支持向量机分类器对番茄叶片图像进行病害检测的准确率为98.80%。除了降低经济损失外，所建议的特征提取方法还可以帮助有效管理植物病害，提高作物产量和粮食安全。
