Kisaan ShadBaad

Kisaan ShadBad is a mobile application that will help our farmers to monitor a large area of crops by identifying the health of crops, water availability and detecting weeds clusters without putting any physical effort. The app will detect the cluster of weeds in the field by processing aerial

Project Title

Kisaan ShadBaad

Project Area of Specialization

Artificial Intelligence

Project Summary

Kisaan ShadBad is a mobile application that will help our farmers to monitor a large area of crops by identifying the health of crops, water availability and detecting weeds clusters without putting any physical effort. The app will detect the cluster of weeds in the field by processing aerial images and will identify interest points in the image. Moreover, our app will also monitor crop irrigation by identifying areas, where the water level is suspected. These features will be based on machine learning and image processing techniques. And it will generate report with health alerts and Suggestion for improvements.

Project Objectives

About 60% of Pakistan’s economy depends on agriculture, so efficient techniques to maintain the crop yield is highly necessary. For this purpose, modern agricultural techniques use airborne technology equipped with a smart multispectral camera, agriculture sensors and satellite imagery. Most of the applications are using this state-of-the-art technology which adds to the complexity and is costly as well since the cost range of mere sensors starts from $5000 and above. Hence, rendering it beyond the reach of common and average farmers. So we need a more generic app that is both cost-effective and accessible to every farmer. For this purpose, we have developed a mobile application that will be accessible to every farmer and will give the approximate result that the multispectral sensor delivers us but without using any costly sensors will help. User just need to capture aerial images with any camera device. In this regard, this application will be very beneficial for our farmers to manage their crops without using expensive and external resources.

Project Implementation Method

The Project is using the integeration of drone to capture aerial frames of the crops. Those frames as well as features of those frames will be pass through multiple difficult Neural Networks, to get the desired value of health, water level as well as weed type.

Models have been deisgned using Python and are trained using Google Collab. 

Models are than implemented offline in Mobile Application.

Reports are generated using image processing algorithms. 

Benefits of the Project

It will help Farmers to monitor their crops without any hustle and more precisely with proper alerts and suggestions to increase crops' yield and to avoid loss in production by knowing the status of crops prior to any kind of loss.

Technical Details of Final Deliverable

Our Final Deliverable of Project will be an offline working mobile application. It will input images and videos from Drone and it will take frames to identify the defeciencies in the crops by evaluating frames through Neural Networks as well as CNNs. Than it will generate the report, Alerts and suggestions using Algorithm.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Agriculture

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Required Resources

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