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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM30707
Volume:
8 |
4 |
Apr 2024
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Agriculture is crucial for India’s economy, but one problem is identifying plant diseases, especially in remote areas where contacting experts is difficult. Wrong disease identification can lead to crop loss. Even government agriculture departments sometimes give incorrect advice to farmers. To tackle this, a dataset of apple crop images has been created to help identify healthy and diseased leaves accurately. These images come from different angles and directly from apple fields. The early disease detection in agriculture, particularly focusing on diseases affecting apple crops. Specifically, it mentions diseases like early blight, late blight, black scurf, common scab that can affect apple plants. It discusses using automatic methods like Convolutional Neural Networks (CNN) to detect and classify these diseases based on a dataset. The approach involves automatic feature extraction and classification using colur information.
KEYWORDS
Publication Date
12/31/45394
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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