Prompt สำหรับ Research methodology process

1. Find topics

ขอ 10 keywords เทรนปี 2024 เพื่อการทำ Literature Review ในสาขาวิศวกรรมคอมพิวเตอร์ร่วมกับวิศวกรรมเกษตร

Please provide 10 keywords for trends in 2024 for conducting a Literature Review in the field of Computer Engineering in conjunction with Agricultural Engineering.

ค้นหา 5 บทความวิชาการที่มี PDF ปี 2023 ขึ้นไป ที่เกี่ยวกับ AI in Crop Monitoring: Reviewing artificial intelligence methods for crop health assessment, disease detection, and yield prediction.

Search for 5 papers from 2023 onwards that are about AI in Crop Monitoring: Reviewing artificial intelligence methods for crop health assessment, disease detection, and yield prediction.

2 Literature review

สรุปแต่ละบทความ

You are an AI designed to extract and summarize key elements from academic articles with precision. Your role is to assist researchers, academics, and students in quickly understanding the core contributions, methodologies, results, and limitations of scientific papers. You will summarize 5 articles according to the following:
- main contributions
- novel methodologies employed
- analysis results and their significance compared to existing literature
- stated limitations

แปลด้วย Gemini

3 Topic comparing

จาก limitation คิดปัญหาใหม่ วิธีการใหม่ และ contribution ใหม่ 5 ปัญหา

From the limitations, try to think of 5 new problems, along with each new novel method that is expected to help solve the problem and each contribution that may arise.

ให้คะแนนแต่ละปัญหา 1 ถึง 10 ถ้าเป็นนักเรียน ป โท วิศวกรรมคอมพิวเตอร์ มาทำสิ่งนี้

Rate the suitability from 1 to 10 for each problem for Master's students in Computer Engineering who must complete their work within 2 years without any knowledge of these novel methods, requiring them to learn everything from scratch.

ออกแบบ 3 การทดลองสำหรับงานวิจัยนี้

Designing 3 experiments for this research

Research:
- Problem: Predicting Crop Yield Variability Across Fields
- Novel Method: Ensemble Learning with Regression Models
- Contribution: Combining multiple regression models (e.g., Random Forest, Gradient Boosting) can enhance yield prediction accuracy by accounting for diverse factors.

You should answer according to these topics:
- Objective
- Hypothesis
- Setup
- Metrics

ขอ 5 ชื่อ paper สำหรับส่ง วารสาร 'Computers and Electronics in Agriculture' สำหรับการวิจัยนี้

Please provide 5 creative and clear titles for submitting a paper to the 'Computers and Electronics in Agriculture' journal with this research.

Research:
Problem: Predicting Crop Yield Variability Across Fields
Novel Method: Ensemble Learning with Regression Models
Contribution: Combining multiple regression models (e.g., Random Forest, Gradient Boosting) can enhance yield prediction accuracy by accounting for diverse factors.
Objective:
To compare the predictive accuracy of individual regression models (e.g., Random Forest, Gradient Boosting) with ensemble learning methods in predicting crop yield variability across fields.
Hypothesis:
We hypothesize that the ensemble learning approach will outperform individual regression models in predicting crop yield variability due to its ability to account for diverse factors and reduce overfitting.
Setup:
1. Divide the dataset into training and testing sets.
2. Train individual regression models (e.g., Random Forest, Gradient Boosting) on the training set.
3. Train an ensemble model (e.g., Ensemble Random Forest, Ensemble Gradient Boosting) using the outputs of individual regression models on the training set.
4. Evaluate the predictive accuracy of individual models and the ensemble model on the testing set using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).
Metrics:
- Mean Absolute Error (MAE): Average of the absolute differences between predicted and actual crop yields.
- Root Mean Square Error (RMSE): Square root of the average of the squared differences between predicted and actual crop yields.
- Comparison of MAE and RMSE between individual regression models and the ensemble model.

สมมุติว่าคุณคือ Reviewer ของวารสาร 'Computers and Electronics in Agriculture' ลองให้คะแนน 1 ถึง 10 สำหรับแต่ละชื่อเรื่อง papers

Suppose you are a reviewer for the journal 'Computers and Electronics in Agriculture.' Please assign scores from 1 to 10 for each paper title:
1. "Harvesting Insights: Enhancing Crop Yield Prediction with Ensemble Learning"
   - This title highlights the use of ensemble learning methods to improve crop yield prediction accuracy.
2. "Fieldwise Crop Yield Forecasting: A Comparative Study of Regression Models and Ensemble Techniques"
   - Emphasizes the comparison between individual regression models and ensemble approaches for predicting crop yield variability across fields.
3. "Fusion of Forces: Boosting Crop Yield Prediction Accuracy through Model Ensemble"
   - A metaphorical title that underscores the synergy achieved by combining diverse regression models.
4. "Beyond the Single Seed: Ensemble Learning Unlocks Crop Yield Patterns"
   - Suggests that ensemble methods reveal hidden patterns in crop yield data.
5. "From Forests to Orchestrations: Ensemble Models for Robust Crop Yield Prediction"
   - Draws an analogy between Random Forests and ensemble orchestrations, highlighting their collective strength.

4 Timeline schedule

วางแผน 2 ปี สำหรับงานวิจัยนี้

Suppose you are an expert in planning. You are planning for graduate students to be able to complete their studies within 2 years, starting from conducting this research project until writing the paper and taking the defense examination.
Give me the plan a 2-year timeline for this research project:

Research:
Title: Beyond the Single Seed: Ensemble Learning Unlocks Crop Yield Patterns
Problem: Predicting Crop Yield Variability Across Fields
Novel Method: Ensemble Learning with Regression Models
Contribution: Combining multiple regression models (e.g., Random Forest, Gradient Boosting) can enhance yield prediction accuracy by accounting for diverse factors.
Objective:
To compare the predictive accuracy of individual regression models (e.g., Random Forest, Gradient Boosting) with ensemble learning methods in predicting crop yield variability across fields.
Hypothesis:
We hypothesize that the ensemble learning approach will outperform individual regression models in predicting crop yield variability due to its ability to account for diverse factors and reduce overfitting.
Setup:
1. Divide the dataset into training and testing sets.
2. Train individual regression models (e.g., Random Forest, Gradient Boosting) on the training set.
3. Train an ensemble model (e.g., Ensemble Random Forest, Ensemble Gradient Boosting) using the outputs of individual regression models on the training set.
4. Evaluate the predictive accuracy of individual models and the ensemble model on the testing set using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).
Metrics:
- Mean Absolute Error (MAE): Average of the absolute differences between predicted and actual crop yields.
- Root Mean Square Error (RMSE): Square root of the average of the squared differences between predicted and actual crop yields.
- Comparison of MAE and RMSE between individual regression models and the ensemble model.

เอาหัวข้อลง Gantt Chart

Put each topic into the Gantt Chart in Tabular format.
column name should be month 1, 2, 3 , ... 24

5 Draft thesis proposal

ร่างเค้าโครงวิทยานิพนธ์

Read this research details and draft the thesis proposal according to the following topics:

Research:
Problem: Predicting Crop Yield Variability Across Fields
Novel Method: Ensemble Learning with Regression Models
Contribution: Combining multiple regression models (e.g., Random Forest, Gradient Boosting) can enhance yield prediction accuracy by accounting for diverse factors.
Objective:
To compare the predictive accuracy of individual regression models (e.g., Random Forest, Gradient Boosting) with ensemble learning methods in predicting crop yield variability across fields.
Hypothesis:
We hypothesize that the ensemble learning approach will outperform individual regression models in predicting crop yield variability due to its ability to account for diverse factors and reduce overfitting.
Setup:
1. Divide the dataset into training and testing sets.
2. Train individual regression models (e.g., Random Forest, Gradient Boosting) on the training set.
3. Train an ensemble model (e.g., Ensemble Random Forest, Ensemble Gradient Boosting) using the outputs of individual regression models on the training set.
4. Evaluate the predictive accuracy of individual models and the ensemble model on the testing set using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).
Metrics:
- Mean Absolute Error (MAE): Average of the absolute differences between predicted and actual crop yields.
- Root Mean Square Error (RMSE): Square root of the average of the squared differences between predicted and actual crop yields.
- Comparison of MAE and RMSE between individual regression models and the ensemble model.


Topics:
1. Topic name outline in Thai and English
2. Principles and reasons
3. Objectives
4.Relevant research results
5. Research methods
5.1 Population
5.2 Sample group
5.3 Tools
5.4 Data collection
5.5 Data analysis
6. Scope and limitations of the research
7. Research location
8. Expected benefits
9. Action plan regarding research activities and period.
10. Budget