3D Model of AgriDrone
Problems and Solutions
AgriDrone Front View
AgriDrone Side View
AgriDrone Top View
What it does
AgriDrone is a drone equipped with a 3D mapping remote camera capable of monitoring large farmlands by analyzing nutrient deficiencies and pest growth in crops. AgriDrone is a promising technology in agricultural sustainability and efficiency in the UAE.
Food security is impacted by the world population increase that will reach 8.5 billion in 2030, thus crop yield must be optimized to meet the future demand. The UAE has an arid climate that exposed the farmers to major challenges including the scarcity of water resources and lack of plant nutrients in soil due to high salinity. Self-sustainability and efficiency enhancement in agricultural sectors by adoption of new technologies help UAE to tackle challenges and achieve self-sufficiency to meet the food demand. The design of AgriDrone in early detection of plant deficiency is a significant tool that prevents damages to plants and crop waste.
How it works
AgriDrone is designed to monitor crop health and quality using advanced quadrotors equipped with a live high resolution camera for capturing crop images and are controlled with Raspberry Pi. This microcontroller collects the captured images and sends them to the installed app on farmers’ smartphones. The database of the app provides the medium to perform a comprehensive data management where obtained data are stored. The app sends the data from the drone to an external cloud for performing a thorough analysis. This analysis comprises image processing and deriving information with a model derived from Artificial Intelligence (AI), particularly Convolution Neural Network (CNN). The CNN model was derived by training the AI system with images of plants with deficiency. In case of any diagnosed problems, a well-described command is sent to the app and AgriDrone, so the drone guides the farmers to the location of the problem source and required actions can be taken.
The concept of this project is to design and develop a well-advanced drone that has the capability to monitor the crop health of large farmlands around the UAE. Throughout the initial market research in agricultural sectors, it was decided to implement Raspberry Pi microcontroller and add a high quality camera for capturing a minimum image resolution of 12.3 MP that is appropriate for farming applications. Based on the required criteria of the preliminary concept, the initial weight of the drone including the camera, its optimal dimension, and the type of the fuel were all selected in order to output the most possible sustainable design for the drone. Furtherly, the team has also analyzed various drone structures such as quadrotor and VTOL drones. However, since a relatively low payload was required and the aim was to use electric motors, the quadrotor was chosen as the most useful structure for the purpose of this project. As the next step, in order to decrease the cost and to widen the accessibility of our product to most of the users in the market, an initial custom model was designed for the quadrotor.
How it is different
AgriDrone and the developed data processing system ensure that all the nutrient deficiencies and crop quality are controlled in a timely manner in order to achieve an optimal crop production throughout the supply chain to meet all the consumer’s demand. This automated technology can revolutionize the monitoring techniques in agricultural lands since, as opposed to its traditional counterparts, it allows the farmer to regularly have access to the detected parameters and spontaneously act upon or enter the command into the platform. The platform sends alerts for any detected abnormal conditions and guides on-site workers through. Additionally, AgriDrone along with the automated platform provides the opportunity to large farmland owners and their crisis management team in the head office to have regular access to land’s information from anywhere in the world as the imaging analysis done by PC can be also sent to them through internet networks.
The next phase is to design a prototype of AgriDrone for testing its function in the agricultural land. Furthermore, the team is planning to optimize AgriDrone by adding more features for detecting plants' water content and humidity and also to implement a thermal sensing camera for collecting temperature related data that affect parameters such as plant disease and vegetation status. Future efforts will also be focused on adding solar panels to the drone and to build a portion of its parts from waste-based raw materials to make it sustainable and eco-friendly. Addition of epoxy or polyurea coatings to make it flexible and water-proof.