IoT-enabled AI-driven Smart Agriculture
As the restrictions on travel have lessened our project partners have been able to spend more time with the system evaluating it in the field.
The system is working well to automatically survey the Rice paddies and detect the areas of Bacterial Leaf Blight.
We have been working closely with our domain experts to try and get the system used out in the fields in Malaysia. This has proven difficult due to the impact of Covid-19 but some test flights have been successfully performed.
After some initial teething problems relating to the hardware setup, we facilitated testing the system using the drone to simulate a flight around a university field before the Phantom drone was actually flown over the university field without issue.
Following this subsequent flights were made at another local site as retrictions permitted
Great to see the automated system working as intended actually out in the field
Unfortunately due to the current pandemic and travel restrictions, both in the UK and Malaysia, it has proven impossible for us to visit our partners to evaluate our final system.
We are working closely on providing a remote solution that can be used by out Malaysian Partners as sion as they are able
While our project demonstration has been delayed due to Covid-19 restrictions, we have spent some time building a browser based interface for our solution.
This web application allows farmers to log in to the system and have access to all the same information as is available in the mobile application.
They can view all their fields and crops on the map along with all the details associated in the system. All uploaded images and videos are available with any analyses performed.
The farmer can order or cancel a drone survey for a particular field in the same way as in the mobile application with the results shown as a detailed map overlay.
In addition they have access to all the communication and plant/disease information available to inform their decision making.
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.
After adding the message functionality to communicate alerts and advice to farmers we created a dashboard start page to bring together important information in the farmers application.
The dashboard shows the user location and current weather, any active alerts and messages, as well as the last health check image taken and quick links to take more.
General user interface improvements were made to both applications bringing them together in terms of style and simplicity while promoting intuitive use
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
One of the outcomes of our successful project meeting in August was the realisation that the small holder farmers may not be the target for our mobile application and that we should instead be targeting third party organisations such as farmers associations or drone survey companies. These would be able to afford to purchase drones and organise for them to be transported to the farmers fields as needed through out the season.
This change in focus has resulted in the development of a completely new application incorporating the automated drone control technology previously worked on but aimed instead at an end user envisaged to be an individual working for such an organisation.
Initial development has been focused on providing the user with a list of fields to be surveyed and displaying their location on a map. Once at the field the user can initiate the automated survey and see the result along with any recommended actions. The same information will then be passed on the the fields owner directly by the platform.
Future development will be focused on providing further utility to the user such as better searches and navigation to fields that need surveying, current and historical comparisons of the survey results and more actions on disease detection.
AgriONE 