报告详情
STRAWBERRY PLANT DISEASE DETECTION USING U-NET SEGMENTATION AND XGBOOST CLASSIFICATION
编号：8
访问权限：仅限参会人
更新：2025-11-04 14:05:01 浏览：24次
拓展类型2
报告开始：暂无开始时间（Asia/Amman）
报告时间：暂无持续时间
所在会场：[暂无会议] [暂无会议段]
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摘要
Despite being one of the horticultural crops with the highest economic value, strawberries are extremely vulnerable to bacterial and fungal infections. To reduce yield losses and guarantee sustainable cultivation, early and precise disease detection is crucial. This is also the first research where an integrated technique that uses machine learning and deep learning to classify diseases of strawberry plants is developed. A U-Net segmentation model is first used to determine the locations of diseased strawberry plants, based on the images of their leaves which the model has been trained on. During training, K-fold cross-validation is utilized to ensure the model is optimally trained such that it can generalize on new samples. After some extra steps, special features that help in distinguishing between different classes are taken from the segmented results to perform the classification.An XGBoost classifier is then used to categorize the leaves into classes that are either healthy or diseased. K-Means clustering is used as a preprocessing step for better lesion feature extraction. Based on experimental results, the suggested framework provides 92% classification accuracy and roughly 90% segmentation accuracy (Dice coefficient). Due to its effectiveness and dependability in computerized disease detection in strawberry plantations, this combination approach holds great potential for application in precision agriculture.
关键词
Deep Learning, Machine Learning, Computer Vision, XGBoost, K-Means Clustering, Image Segmentation, Strawberry Leaf Disease, Convolutional Neural Network (CNN), U-Net, Precision Agriculture.
报告人
Sharmisri N S
student Velammal College of Engineering and Technology； Madurai稿件作者
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重要日期
-
会议日期
12月29日
2025
至12月31日
2025
-
11月30日 2025
初稿截稿日期
-
12月30日 2025
报告提交截止日期
-
12月30日 2025
注册截止日期
主办单位
国际科学联合会
承办单位
扎尔卡大学
联系方式
- Miss AsianComNet
- as******@usssociety.org
历届会议
-
2024年10月24日 泰国 Bangkok
2024亚洲通讯与网络会议
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