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.
The mobile application has continued development to present end users with all the relevant information about their holdings. The following video demonstrates the current state of the application enabling the user to monitor multiple fields and crops over time so they can see current sensor data, the disease analysis of uploaded images and videos as well as the results of drone surveys of the fields.
The application aims to empower the user to improvetheir crops by linking them to relevant information on how to treat and prevent diseases as they are detected as well as providing access to general advice on crop maintenance.
We have also included a forum facility to take advantage of the local community prompting the user to post images that have been unsuccessfully analysed or are outside the scope of our models, for other people to advise on. This can then feed back into our data gathering facilities
We have developed a real-time crop disease detection tool designed to provide the rapid diagnosis of disease in crops. The process utilises artificial intelligence and drone imagery to show a visual representation of the disease while providing an estimation of the severity of the problem.
The video demonstrates this technique on a Malaysian rice paddy detecting bacterial leaf blight (BLB)
We have started development of the automatic drone control by out mobile application. The following video shows the initial user interface design allowing the user to instruct the drone to survey the field, followed by automatic waypoint generation and a simulated drone flight.
The drones location is mapped for the user to see and the images taken are displayed before being uploaded to the data platform for analysis witht he resulting stitched overlays displayed on the map for the user