Retraction notice to: “An event detection method for social networks based on link prediction” [Inf. Syst. 71 (2017) 16–26]

Hu, Wenbin; Wang, Huan; Peng, Chao; Liang, Huanle; Du, Bo

doi:10.1016/j.is.2019.02.002

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2023

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“…Diversified digital image processing tools are being used to meet these challenges [10]. However, image processing has made significant developments in the field of agricultural research [11]. Earlier, in order to identify disease severity, usually, the naked eye method was used, but it was not possible to measure the amount of disease precisely since the results attained were particular.…”

Section: Introductionmentioning

confidence: 99%

Classification of Image-Based Wheat Leaf Diseases using Deep Learning Approach: A Survey

Sharma,

Sethi

2023

J. Sci. Res.

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This paper focuses on detecting leaf diseases in wheat plants from the beginning to the end of the plant's life cycle. It highlights the best techniques for detecting various types of wheat leaf diseases and emphasizes the use of computer vision, image processing, and machine learning. The main focus is on classifying these diseases through deep convolutional neural networks, a popular image recognition and classification approach. The paper reviews various techniques for classifying image-based wheat leaf diseases, including spot blotch, stripe rust, brown rust, and powdery mildew. The paper aims to summarize the state-of-the-art techniques for detecting wheat leaf diseases.

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Section: Introductionmentioning

confidence: 99%