期刊
APPLIED SCIENCES-BASEL
卷 12, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/app12125919
关键词
precision agriculture; smart farming; deep learning; CNN; RNN; SVM
类别
资金
- Sensor Networks and Cellular Systems (SNCS) Research Center at the University of Tabuk
Agriculture is an important field with significant economic impact in several countries. This study analyzed recent research articles on deep learning techniques in agriculture over the past five years and discussed important contributions and challenges. The researchers investigated agriculture parameters monitored by the internet of things and used them for analysis with deep learning algorithms.
Agriculture is considered an important field with a significant economic impact in several countries. Due to the substantial population growth, meeting people's dietary needs has become a relevant concern. The transition to smart agriculture has become inevitable to achieve these food security goals. In recent years, deep learning techniques, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), have been intensely researched and applied in various fields, including agriculture. This study analyzed the recent research articles on deep learning techniques in agriculture over the previous five years and discussed the most important contributions and the challenges that have been solved. Furthermore, we investigated the agriculture parameters being monitored by the internet of things and used them to feed the deep learning algorithm for analysis. Additionally, we compared different studies regarding focused agriculture area, problems solved, the dataset used, the deep learning model used, the framework used, data preprocessing and augmentation method, and results with accuracy. We concluded in this survey that although CNN provides better results, it lacks in early detection of plant diseases. To cope with this issue, we proposed an intelligent agriculture system based on a hybrid model of CNN and SVM, capable of detecting and classifying plant leaves disease early.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据