Fertilizer Recommendation System to Ensure High Crop Yields in Bangladesh

Local Project Bangladesh Chapter

Coordinated by the Lead of Bangladesh, Saiful Islam Shawon Kazi , Munir Fahad ,

Status: Completed

Project Duration: 09 Sep 2022 - 10 Oct 2022

Open Source resources available from this project

Project background.

According to a recent study by the UN’s Food and Agriculture Organization about 520 million people are without food security. The pandemic worsened access to food for these large numbers of people. As much as 31.9 percent of the population in Bangladesh experienced moderate to severe food security. As a direct consequence of rapid growth in population and urbanization, the situation is deteriorating rapidly. It is assumed the land will be reduced by 5% in 2030 and by 8% in 2050. The research shows that the total demand for rice in 2030 and 2050 under the BAU (Bangladesh Bureau of Statistics) scenario would be 40.11 million tons and 46.15 million tons. The total direct demand of rice for consumption in 2030 will be 32.4 million metric tons (MMT), which is a 14% increase from the 2015 level. As for that, the share of rice will decrease from 82% in 2010 to 79% in 2030 and 78.6% in 2050.

There is a pressing need and opportunity to improve food security by enacting effective and coordinated Artificial Intelligence-driven (AI) solutions and actions, which will necessitate significant improvements in the relevant sectors, such as crop classification, fertilizer recommendation, crop productivity, etc.

The problem.

SOLUTION?

To ensure food security the most important aspect arises is high crop yield. The goal of this project is to utilize remote sensing data for building a fertiliser recommendation system targeting crop productivity. The objective of this study is to analyze certain crops such as rice, wheat, etc. of various regions in Bangladesh, and measurement of the fertilization uptake for high yield.

The project aims to deliver a data-driven solution for fertilizer recommendation of the interested crop fields in Bangladesh. This recommendation system will be live through web app where users will get fertilizer suggestions of their interested fields. The results will be made open source. This will help farmers to increase crop productivity.

Project goals.

To ensure food security the most important aspect arises is high crop yield. The goal of this project is to utilize remote sensing data for building a fertilizer recommendation system targeting crop productivity. The objective of this study is to analyze certain crops such as rice, wheat, etc. of various regions in Bangladesh, and measurement of the fertilization uptake for high yield.The project aims to deliver a data-driven solution for fertilizer recommendation of the interested crop fields in Bangladesh. This recommendation system will be live through a web app where users will get fertilizer suggestions from their interested fields. The results will be made open source. This will help farmers to increase crop productivity.The goal of the project is to1. Collect data from open source satellite images of Bangladesh and extract necessary information with remote sensing analysis. 2. Process the data following a systematic methodology, and do exploratory data analysis of harvested crops. 3. Develop an automatic recommendation system for appropriate fertilization requirements of certain crops. 4. Outline an AI-driven solution to build a system to improve crop yield for the farmers. 5. A web app where users would get their field statistics and recommendation for their crops.

Project plan.

  • Week 1

    Data Collection

    Brainstorming

    GIS Processing

  • Week 2

    Remote sense analysis

    Exploratory Data Analysis(EDA)

    ML Preprocessing

  • Week 3

    Pre-processing Completion.

    Building Machine Learning models.

    Evaluation of models in certain regions.

    Building Recommendation System

  • Week 4

    Creation of maps of certain crops across Bangladesh.

    Georeferencing the classified maps.

    Web app for fertilizer recommendation.

Learning outcomes.

1. Remote Sense Analysis.
2. Data and GIS Preprocessing.
3. Data Visualizations.
4. Machine Learning.
5. Web app development with Georeference API development.
6. Real-world impact to improve the agriculture sector.

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