Summary of Developing An Optimal Model For Predicting the Severity Of Wheat Stem Rust (case Study Of Arsi and Bale Zone), by Tewodrose Altaye
Developing an Optimal Model for Predicting the Severity of Wheat Stem Rust (Case study of Arsi and Bale Zone)
by Tewodrose Altaye
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research combines three types of artificial neural networks (ANNs) – Backpropagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and General Regression Neural Network (GRNN) – to predict the severity of stem rust in wheat. The models consider various environmental factors, including temperature, rainfall, and humidity, as well as different wheat varieties. While BPNN and RBFNN showed some predictive capabilities, GRNN demonstrated the most effective results, requiring less training time. Moreover, the study found that total seasonal rainfall positively impacts the development of stem rust. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses special kinds of computer networks called artificial neural networks (ANNs) to try to predict how bad a type of wheat disease called stem rust will be. The scientists used three different types of ANNs and looked at things like temperature, rain, and humidity in the air. They also tested different types of wheat. They found that one type of ANN was really good at making predictions and didn’t take too long to learn. They also learned that having a lot of rain helps keep the disease from getting worse. |
Keywords
* Artificial intelligence * Backpropagation * Neural network * Regression * Temperature
