Summary of A Machine Learning Approach For Crop Yield and Disease Prediction Integrating Soil Nutrition and Weather Factors, by Forkan Uddin Ahmed (1) et al.
A Machine Learning Approach for Crop Yield and Disease Prediction Integrating Soil Nutrition and Weather Factors
by Forkan Uddin Ahmed, Annesha Das, Md Zubair
First submitted to arxiv on: 28 Mar 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 develops an intelligent agricultural decision-supporting system to help Bangladeshi farmers select the most productive crops and predict diseases. The model uses machine learning methods and real-world datasets on crop production, soil conditions, and meteorological factors. It first recommends crops based on soil nutrition, then predicts weather patterns using SARIMAX models, forecasts disease possibilities with support vector classifiers, and finally estimates crop yields with decision tree regression. By utilizing this system, farmers can make informed decisions to reduce output losses and improve agricultural practices in Bangladesh. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research aims to help Bangladeshi farmers by developing a system that recommends the best crops based on soil nutrition, predicts weather patterns, forecasts disease possibilities, and estimates crop yields. The system uses machine learning methods and real-world datasets to provide detailed information for decision-making. This can lead to better agricultural practices in Bangladesh. |
Keywords
* Artificial intelligence * Decision tree * Machine learning * Regression
