An Evaluation of Various Machine Learning Approaches for Detecting Leaf Diseases in Agriculture

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外文摘要:A BSTRACT Background: Machine learning has shown remarkable promise in recent years for use in areas such as pattern detection and categorization. The diagnosis of diseases is crucial in agriculture since they are a natural occurrence in plants. The easiest and most effective way to identify crop disease is through the use of image processing, computer vision and machine learning techniques. Methods: To identify and categorize cotton leaf diseases, the study compares the effectiveness of established techniques like Support Vector Machine (SVM) and random forest with state-of-the-art techniques like neural network (CNN) methods and architectures like Inceptionv3, VGG16 and RasNet50 with data augmentation and transfer learning. Result: The models were trained with four distinct types of plant photos that were manually gathered from a government agency and a farm. It was also noted that as the quantity of training data rose, so performed the resultant models.
外文关键词:machine learning;random forest;accuracy;convolutional neural network (cnn);Support Vector Machine (SVM)
作者:Cho, Ok-Hue
作者单位:Sangmyung Univ
期刊名称:LEGUME RESEARCH
期刊影响因子:0.0
出版年份:2024
出版刊次:47(4)
原文传递申请:江苏省科技资源(工程技术文献)统筹服务平台

  1. 编译服务:智慧农业
  2. 编译者:虞德容
  3. 编译时间:2025-03-31