Plant Disease Detection from Image Using CNN

K. Kumar, K. Tripathi, Rashmi Gupta
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Abstract

The increasing threat of plant diseases poses a significant challenge to global food security. Rapid and accurate identification of plant diseases is crucial for effective disease management and prevention. In recent years, deep learning techniques have shown great promise in automating the process of plant disease identification through image analysis. This report presents a comprehensive study on image-based plant disease classification using deep learning techniques. The report begins by providing an overview of plant diseases and their impact on agriculture. It discusses the limitations of traditional disease identification methods and highlights the potential of deep learning algorithms in revolutionizing the field. The importance of image-based approaches is emphasized due to their non-destructive and scalable nature. Next, the report delves into the methodology of deep learning for plant disease classification. It explores various architectures such as convolutional neural networks (CNNs) and their variants, including transfer learning and ensemble methods. The training process, data augmentation techniques, and hyperparameter tuning are discussed in detail.
利用CNN从图像中检测植物病害
植物病害威胁日益严重,对全球粮食安全构成重大挑战。快速准确地识别植物病害对于有效的病害管理和预防至关重要。近年来,深度学习技术在通过图像分析自动化植物病害识别过程中显示出巨大的前景。本文介绍了利用深度学习技术进行基于图像的植物病害分类的全面研究。该报告首先概述了植物病害及其对农业的影响。它讨论了传统疾病识别方法的局限性,并强调了深度学习算法在革新该领域的潜力。由于其非破坏性和可扩展性,强调了基于图像的方法的重要性。接下来,报告深入研究了植物病害分类的深度学习方法。它探讨了各种架构,如卷积神经网络(cnn)及其变体,包括迁移学习和集成方法。详细讨论了训练过程、数据增强技术和超参数调优。
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