Soil Analysis using Machine Learning

Project Title

Soil Analysis using Machine Learning

Project Area of Specialization Artificial IntelligenceProject Summary

We are going to provide a technical solution for soil Analysis at farms. Our endproduct is an android mobile application. Farmers simply need to use our app and upload a picture of soil from their field. The image and their location are processed by the Machine Learning model which is trained on a certain dataset

Project Objectives

Agriculture plays a critical role in the economy of Pakistan. The agricultural sector directly funds the population of the country and accounts for 26 percent of the gross domestic product(GDP). We are not only economically dependent on agriculture but also basic food production. Pakistan should then have a solid base for a healthy and productive economy. Due to the lower production rate with the increase in population, food inflation rises rapidly in Pakistan. The decline in soil fertility is one of the major reasons for the decrease in crop production in Pakistan. Soil fertility depends on nutrients (Nitrogen, Potassium, and Phosphorus, etc.), pH (potential of hydrogen ), and EC (electrical conductivity). Farmers are concerned about soil fertility as it affects crop yield. Generally, typical farmers do not bother to know about the soil nutrients before cultivating plants and crops because the soil testing from laboratories is a very hectic process and rely on their past experiences. Based on these observations, they use Inappropriate fertilizers. We will provide a technical solution for soil analysis at farms. Our end product is an android application which will inform the farmers about the concentration of nutrient (Nitrogen, Phosphorus, and potassium) in soil, pH, EC, and soil texture. Farmers simply need to use our app and upload a picture of soil from their field. The image and their location are processed by the Machine Learning model which is trained on a certain dataset. We provide an optimal solution for soil testing at farms through which we can educate farmers on how to increase their soil productivity which will help to fulfill food production demands.

Project Implementation Method

We will provide a technical solution for soil analysis at farms. Our end product is an android application which will inform the farmers about the concentration of nutrient (Nitrogen, Phosphorus, and potassium) in soil, pH, EC, and soil texture. Farmers simply need to use ourapp and upload a picture of soil from their field. The image and their location are processed by the Machine Learning model which is trained on a certain dataset.

Benefits of the Project

We provide an optimal solution for soil testing at farms through which we can educate farmers on how to increase their soil productivity which will help to fulfill food production demands. Farmers don't have to follow the lab testing method which is a time-consuming and costly method. 

Technical Details of Final Deliverable

Android-based Mobile Application that provides soil analysis at farms.
Research paper in IEEE format
Final Report
Final Presentation

Final Deliverable of the Project Software SystemCore Industry ITOther Industries Agriculture Core Technology Artificial Intelligence(AI)Other Technologies Cloud InfrastructureSustainable Development Goals Zero Hunger, Decent Work and Economic Growth, Climate ActionRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	70000
Amazon Cloud services	Equipment	4	15000	60000
Data collection	Miscellaneous	1	10000	10000