CNN and Random Forest for Maize Diseases Identification

Prity Kumari, Anmol Sharma, Fazle Ali,Vinay Kukreja

2024 International Conference on Automation and Computation (AUTOCOM)(2024)

引用 0|浏览0
暂无评分
摘要
A significant maize crop cultivated all through the globe, maize, also known as Zea mays, serves as vital to both financial wellness and the security of food. The purpose of this research paper is to deliver an in-depth study of maize diseases, with an emphasis on trendy detection approaches, environmentally friendly management measurements, and factors affecting how prevalent they are. Neural networks, which are commonly referred to as convolutional neural networks or neural networks, are a kind of machine learning architecture that features a layer within and an outer layer for pooling. Their effectiveness is improved by this quality. A wide range of statistics, such as recalls, precision, and support, are used in this update. The minimum accuracy in Class 7 is 0.94 and the maximum accuracy in Class 3 is 0.97. The macro average for precision is 82.99, recall is 83.19 and the F1-Score is 83.00 respectively. The weighted average for precision is 83.24, recall is 83.01 and F1-Score is 83.04 respectively. The Micro Average for precision, recall, and F1-Score is 83.0, 83.01, and 83.01 respectively. In this, there is 7 classes. The farmers gain from it because it reduces both time and money.
更多
查看译文
关键词
corn diseases,CNN,random forest,deep learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要