Authors: Forkan Uddin Ahmed, Annesha Das, Md Zubair

Published on: March 28, 2024

Impact Score: 8.0

Arxiv code: Arxiv:2403.19273

Summary

• What is new: The development of a decision-supporting system utilizing machine learning to aid in crop selection and disease forecasting, specifically tailored to the agricultural conditions of Bangladesh.
• Why this is important: Farmers in Bangladesh face challenges in choosing high-yield crops and efficiently controlling crop diseases due to a lack of actionable insights.
• What the research proposes: A machine learning-based model that uses datasets on crop production, soil conditions, and meteorological factors to recommend optimal crops and forecast potential diseases.
• Results: The system successfully recommends crops based on soil nutrition and predicts crop diseases using weather predictions, aiding farmers in making informed decisions.

Technical Details

Technological frameworks used: Machine Learning

Models used: SARIMAX for weather prediction, Support Vector Classifier for disease forecasting, Decision Tree Regression for yield forecasting

Data used: Datasets on crop production, soil conditions, agro-meteorological regions, crop disease, and meteorological factors

Potential Impact

Agricultural technology firms, crop insurance companies, and agribusinesses could significantly benefit or face disruption from the adoption of this intelligent decision-supporting system.

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