Khulood Alawadi
About
Khulood Alawadi is an interdisciplinary designer passionate about working at the intersections of design with science, engineering and society and exploring tools for merging them. She is committed to recontextualising design for communities and environments that are seldom designed for, motivated by personal experiences and world events.
Previously, she worked as a designer, maker and communicator for science research institutes, cultural organisations and healthcare providers.
Her IDE group project, Fallback: Designing of Global Internet Shutdowns, has been featured in multiple publications including Dezeen, Creative Applications, ACM Interaction and awarded the Core77 Student Notable in the Design for Social Impact category. In the RCA x CERN Grand Challenge collaboration, her group project, KALA: A cross-cultural language game, was a finalist in the Social and Economic Disparities category.
Degree Details
Statement
The circumstances of the world during the development of this project emphasised the importance of adapting to new tools for prototyping and testing, recreating natural environments and conditions virtually and making engineering tools accessible to a broader audience and skillset.
This experience was also a lesson in empathy. It highlighted the disparities in human connectivity, accessibility to specific communities and environments as well as challenges with access to information and technologies to bring design and engineering concepts to life.
The Date Palm
The Date palm is one of the few and important crops cultivated in the arid regions of the Middle East and especially the Arabian Peninsula. It represents a central element of the heritage in the area and a pivot of cultural, social and economic life.
The globalisation of trade has created significant growth in the date cultivation industry, rapidly increasing the number of new monoculture plantations which has unfortunately created an ideal ecological niche for biotic stresses to manifest. At the same time, the increased awareness of the risks of chemical pesticides and demand for organic produce encourages a new approach to agriculture, and more specifically, pest management.
Palmspector: A Robotic Monitoring and Inspection System for Date Palm Plantations
Palmspector was developed by converging a diverse network of people and skills to design not only for a specific environmental context but also a socio-cultural and economic one. This project is part of a bigger vision for the understanding of the challenges of agriculture in an arid region with the added impacts of climate change while working towards food security and sufficiency for a growing population, vast urbanisation and unprecedented global circumstances.
This project applied design principles to develop a deep understanding of the context and pain points from the human and environmental perspective in order to be able to complement that with innovation in engineering and technology.
Technologies were validated through the use of computation and engineering simulation tools while the system sought feedback from key governmental and agricultural organizations, commercial farms, farmers and consumers. The validation showed great eagerness and acceptance towards autonomous technology alongside traditional agriculture, united by the common goal of better food and the protection of the limited natural resources.
Navigation & Mapping
The Palmspector system knows its accurate position in 3D space and can approach target trees and avoid obstacles during the inspection.
This process focused on identifying the right technologies and designing the algorithms to navigate this complex environment (irregular terrain, vegetation, lighting conditions) that might vary from farm to farm or from season to season.
In addition to the real-life outdoor experiments, I built a simulator to create a realistic scenario of the date palm plantation to simulate the inspection process and tree approach protocol. This process included designing the virtual robot and programming the world; the palm trees, terrain and the physical attributes and interactions of each as well as integrating virtual sensors to react to the virtual environment. The algorithms designed on the simulator will be able to transfer directly to a real-world robot for evaluation and testing.
Detection
Palmspector provides real-time computation of a detection algorithm for a robust index of suspicion using inference from a custom deep neural network that has been trained by a sensor fusion technology dedicated for date palm anomalies.
This prototype captures data from multiple dedicated inputs of the identified healthy and infested trees. This method acquires data points simultaneously following a uniform protocol and collates the output into organized datasets. These were then successfully used as training datasets for Supervised Deep Learning to detect a healthy or an infested tree