While the world is dealing with the pandemic in these troubling times we would like to express our support and appreciation to those that are working to safeguard the vulnerable and our consolation to everyone effected.
Due to the disruptions in international travel we have had to delay our scheduled project demonstration and are continuing to work towards improving our solution during these troubling times.
Our solution has adapted and advanced with our research on this project while remaining firmly focused on the benefits we can provide to end users.
We have created a system to provide farmers with precise information about the state of their fields utilising artificial intelligence and drone technology. Based upon feedback from our partners that the drone surveys would need to be provided as a service to the farmers we have created two seperate mobile applications: one for the farmers to enter and recieve information about their fields and another for a company offering drone surveys to the farmer.
In our system the farmer can order a survey (either indepnedently or based our a recommendation from the system) which is then picked up by an associated third-party service provider. This provider utilises our second application to receive information on all the surveys required and completed by the company. It then provides navigation to the field location and provides automated control of the drone to survey the field, without intensive training. The results are processed on the mobile device showing an immmediate overlay of the troubled areas and once uploaded to our cloud server, the same results are distributed to the farmer
The video below shows our mobile applications in action with the navigation and scouting displayed at an increaded speed for brevity
We have been working hard to optimise our disease segmentation model and embed it in our mobile application. This will allow us to provide the user with the segmentation result even when there is no internet available.
We have also been working on improving the model to work with lower resolutions so that we can a map overlay based on the frames extracted from the lower resolution video streamed from the drone during the flight.
As you can see from the screenshots we have made excellent progress in this task by managing to successfully achieve both objectives although the current analysis speed is slow. This means the on-device segmentation will be used as a backup to the cloud service while we work on futher optimisation.
Today we held a meeting to demonstrate the current progress of the project with representatives from numerous industry partners. The goal was to determine how successfully our solution was in solving the issue of Bacterial Leaf Blight detection in rice fields and to discuss ways of improving it to meet the needs of different end users.
The discussion went well with great interest on behalf of the industry partners and excellent feedback to further develop and refine the project.
During a visit to a rice paddy we used our mobile application to automatically survey the field. The literal field test was a great success, our application connected to the Phantom 4 Pro drone without issue, generated a flight path and uploaded it to the drone. The drone was then able to photograph the entire field without any user input. Afterwards the photos were downloaded to the mobile device before being analysed by out disease segmentation model.
We gathered some useful data about improvements to the system and the mobile application while demonstrating the ability to easily control the drone without direct user intervention.
As our disease segmentation model is currently cloud-based the drone survey application has to upload the images to the cloud for analysis after they have been downloaded from the drone.
As our aim is to provide immediate feedback we have developed an efficient on device classification model that uses frames extracted from the low resolution video stream sent from the drone during the flight.
This allows us to immediately highlight which areas have disease before getting the advanced segementation results
As part of our shift to providing a mobile application for a drone survey provider we thought about how such as service would work.
Our vision is that businesses will be able to register as service providers with our system and then farmers will be able to link their account to different providers based on their needs.
So after a farmer has requested a survey, an employee of the service provider could log in to our drone survey application, recieve a list of all the surveys assigned to their company and view them on a map. Selecting the one they wish to survey, the application provides GPS navigation to the field, where they can connect the drone and have it automatically survey the field.
To that end we have added the navigation utility to the application using MapBox
Professor Liangxiu Han gave a presentation, “Precision Agriculture – A Big Data Driven Approach to Crop Disease Detection”, at the Third International Workshop on “Precision Agriculture: Data Driven Approach to Crop Monitoring and Disease Diagnosis”, held in Beijing, China, 25th – 27th September 2019