Butt Nouman, Iqbal Muhammad Munwar, Ramzan Shabana, Raza Ali, Abualigah Laith, Fitriyani Norma Latif, Gu Yeonghyeon, Syafrudin Muhammad
Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan.
Department of Computer Science & IT, Government Sadiq College Women University, Bahawalpur, Pakistan.
PLoS One. 2025 Jan 22;20(1):e0316081. doi: 10.1371/journal.pone.0316081. eCollection 2025.
Citrus farming is one of the major agricultural sectors of Pakistan and currently represents almost 30% of total fruit production, with its highest concentration in Punjab. Although economically important, citrus crops like sweet orange, grapefruit, lemon, and mandarins face various diseases like canker, scab, and black spot, which lower fruit quality and yield. Traditional manual disease diagnosis is not only slow, less accurate, and expensive but also relies heavily on expert intervention. To address these issues, this research examines the implementation of an automated disease classification system using deep learning and optimal feature selection. The system incorporates data augmentation and transfer learning with pre-trained models such as DenseNet-201 and AlexNet to improve diagnostic accuracy, efficiency, and cost-effectiveness. Experimental results on a citrus leaves dataset show an impressive 99.6% classification accuracy. The proposed framework outperforms existing methods, offering a robust and scalable solution for disease detection in citrus farming, contributing to more sustainable agricultural practices.
柑橘种植是巴基斯坦主要的农业部门之一，目前约占水果总产量的30%，在旁遮普邦的集中度最高。尽管具有重要的经济意义，但甜橙、葡萄柚、柠檬和柑橘等柑橘类作物面临着溃疡病、疮痂病和黑斑病等各种病害，这些病害会降低果实品质和产量。传统的人工病害诊断不仅速度慢、准确性低、成本高，而且严重依赖专家干预。为了解决这些问题，本研究考察了使用深度学习和最优特征选择的自动病害分类系统的实施情况。该系统将数据增强和迁移学习与DenseNet - 201和AlexNet等预训练模型相结合，以提高诊断准确性、效率和成本效益。在柑橘叶片数据集上的实验结果显示出高达99.6%的分类准确率。所提出的框架优于现有方法，为柑橘种植中的病害检测提供了一个强大且可扩展的解决方案，有助于实现更可持续的农业实践。
