Machine Learning Based Agriculture Assistance System

We are going to predict yield and disease detection with the help of state of the art machine learning techniques. Yield prediction is very important for government as well as individual farmer. Currently no automated system is available in Pakistan to predict the yield of crops as well as the detec

Project Title

Machine Learning Based Agriculture Assistance System

Project Area of Specialization

Artificial Intelligence

Project Summary

We are going to predict yield and disease detection with the help of state of the art machine learning techniques. Yield prediction is very important for government as well as individual farmer. Currently no automated system is available in Pakistan to predict the yield of crops as well as the detection of diseased at early stage. Moreover, yield prediction, plays an important a critical role in taking timely decisions. The climate change and other environmental factors make it difficult to predict the yield with additional methods. With the help of agriculture sensors and machine learning algorithms this proposed methods will help the farmer and the government official in an effective manners . Cheap yield prediction is important for timely decision. Diseases have greatly impact on yield.

Project Objectives

Farmers will be able to predict their crops at an early stage and in case, any disease attacks on crops, the proposed system identify the disease and rectify it and get the solution according to disease. The objects are presented in the following list

1. Predict crop yield at an early stage.
2.  Predict the soil.
3. Detect the weather.
4. Provide better information for making a management decision.
5. Provide more details and useful farm records.
6. Reduces fertilizer costs.
7. Detection of diseases.
8. Rectify the disease.
9. Reduce time while detecting diseases.
10. Reduces pesticides costs.
11. The crop is ripe with the time frame.

Project Implementation Method

In order to implement the proposed method, we have to acquire the data first. For this purpose, the data is collected for multiple fields by installing different sensors. The data will be collected on hourly bases for 3 months during the top crop season. This data is then analyzed using data analysis technique and prepared for machine learning algorithms. Machine learning algorithms will then be implemented in mobile application for prediction of corp. yield. For disease detection various image processing methods will be deployed. The farmer will just take the image of crop and the app will assist the farmer to identify the disease.

Benefits of the Project

1. Predict crop yield at an early stage.
2.  Predict the soil.
3. Detect the weather.
4. Provide better information for making a management decision.
5. Provide more details and useful farm records.
6. Reduces fertilizer costs.
7. Detection of diseases.
8. Rectify the disease.
9. Reduce time while detecting diseases.
10. Reduces pesticides costs.
11. The crop is ripe with the time frame.

Technical Details of Final Deliverable

The complete deliverable package of the proposed system is comprised of hardware and mobile application. The farmer have to install the hardware equipment in the field at different position. The hardware is comprised of different sensors that collect the data. based on this data the farmer can observe every day yeild prediction. 

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Agriculture

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Big Data

Sustainable Development Goals

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com