| Idea Name: |
AgriVision |
| Slogan: |
Crop Disease Scouting using AIIOT |
| Supervisor Name: |
Dr. Rafia Mumtaz |
| Supervisor Designation: |
Associate Professor |
| Supervisor School: |
SEECS |
| Supervisor Department: |
Department of Computing |
| Contact number: |
03425563343 |
| Email ID: |
[email protected] |
| Abstract: |
| AgriVision is an automated multi-platform crop scouting system employing several state-of-the-art technologies like AI, IoT, CV, Edge AI, Cloud Computing for timely detection of wheat rust diseases |
| What is the unmet need in society that your idea will fulfill ? |
| The rust disease in wheat crops usually causes up to 20% reduction in crop yield. For its timely detection thousands of hours of knowledge worker time is required. Therefore, there is a dire need of an automated system that can do timely identification of rust diseases. |
| Who needs it ? How many would benefit ? |
| It is primarily needed by farmers and agriculture experts, but the need extends to a much wider spectrum, Pakistan being an agricultural country relies mainly on agriculture, therefore such a system is the need of the entire country for feeding the population |
| How will the solution works |
| A high quality camera takes the wheat leaf image which is passed through a pipeline. First segmentation is done using deep learning-based segmentation model called U2-Net. Auto cropping is then used to extract the region of interest. Deep Learning models will be trained on the cropped dataset, and this trained model will be capable of performing real time crop disease detection. Deployment will be done on AWS connected with Nvidia Jetson Nano, Android / iOS applications, and web portal. |
| Who are your competitors ? How is your solution different |
| Currently in Pakistan, there are no other fully automated cross platform solutions deployed for increasing the crop yield, making us a first-mover in the market |
| Status: |
new |
| Entry Date & Time: |
2021-12-14 (1507) |
