Farooq Muhammad Shoaib, Kamran Ayesha, Raza Syed Atir, Wasiq Muhammad Farooq, Hassan Bilal, Herzog Nitsa J
Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore 54000, Pakistan.
Department of Applied Computing Technologies FoIT& CS, University of Central Punjab Lahore, Lahore 54000, Pakistan.
J Imaging. 2025 Jul 31;11(8):256. doi: 10.3390/jimaging11080256.
Potatoes are one of the world's most widely cultivated crops, but their yield is coming under mounting pressure from early blight, a fungal disease caused by . Early detection and accurate identification are key to effective disease management and yield protection. This paper introduces a novel deep learning framework called ZeroShot CNN, which integrates convolutional neural networks (CNNs) and ZeroShot Learning (ZSL) for the efficient classification of seen and unseen disease classes. The model utilizes convolutional layers for feature extraction and employs semantic embedding techniques to identify previously untrained classes. Implemented on the Kaggle potato disease dataset, ZeroShot CNN achieved 98.50% accuracy for seen categories and 99.91% accuracy for unseen categories, outperforming conventional methods. The hybrid approach demonstrated superior generalization, providing a scalable, real-time solution for detecting agricultural diseases. The success of this solution validates the potential in harnessing deep learning and ZeroShot inference to transform plant pathology and crop protection practices.
土豆是世界上种植最广泛的作物之一，但其产量正受到早疫病的日益严重的压力，早疫病是一种由……引起的真菌病害。早期检测和准确识别是有效病害管理和产量保护的关键。本文介绍了一种名为ZeroShot CNN的新型深度学习框架，该框架将卷积神经网络（CNN）和零样本学习（ZSL）集成在一起，用于对已见和未见病害类别进行高效分类。该模型利用卷积层进行特征提取，并采用语义嵌入技术来识别以前未训练过的类别。在Kaggle土豆病害数据集上实现的ZeroShot CNN，对于已见类别达到了98.50%的准确率，对于未见类别达到了99.91%的准确率，优于传统方法。这种混合方法表现出了卓越的泛化能力，为检测农业病害提供了一种可扩展的实时解决方案。该解决方案的成功验证了利用深度学习和零样本推理来变革植物病理学和作物保护实践的潜力。
