Ms Houda Orchi | Crop Diseases | FutureFarming Leadership Award
🎓 Academic Qualification:
2019 – Present: PhD student in Artificial Intelligence and Computer Vision at The National School of Electricity and Mechanics, University of Hassan II Casablanca, supervised by Dr. Mohamed Sadik and Dr. Mohammed Khaldoun. Thesis Title: Development of Detection and Identification of Crop Diseases Platform. 2018 – 2019: German Certificate B2 from the Goethe Institute. 2014 – 2018: Engineer Degree in Mechatronics Engineering at the National School of Applied Sciences of Tetouan. 2012 – 2014: Preparatory Cycle at the National School of Applied Sciences. 2011 – 2012: High School Diploma in Mathematical Sciences A.
💼 Professional Experience:
31 October 2023 – Present: Research Assistant at Computer Science Laboratory, UQAM. 01 February – 30 June 2018: Mechatronics Engineer at OCP Jorf Lasfar – worked on clock synchronization and data exchange automation. 01 July – 01 September 2017: Internship at Renault Nissan – responsible for the design and installation of a conveyor in the assembly department. 02 August – 02 September 2016: Initiation internship at Savola Maroc – focused on repair and maintenance of engines and pumps. 01 July – 01 August 2016: Observation internship at Sopriam Peugeot – engaged in automotive diagnostics and engine repair.
🔧 Skills and Technical Knowledge:
Technical Skills: Artificial Intelligence, Computer Vision, Internet of Things, Mechanical Production, Machine Design, Mechatronics Systems, Robotics, Theory of Mechanisms, Industrial Design, General Technology, Automotive Technology, Automation, and Instrumentation. Additional Knowledge: Embedded Systems, Electrical Networks, Signals, Project and Quality Management, Production Management, Electrical Engineering, Power Electronics, Economy, Relational Database, General Accounting.
Research Focus: Crop Diseases
Houda’s current research is centered on the development of a platform for the detection and identification of crop diseases using artificial intelligence and computer vision technologies. This innovative work aims to enhance agricultural productivity and sustainability by providing precise and timely identification of various crop diseases, thereby enabling better management and treatment strategies. 🌱📊
- On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey
- Authors: H. Orchi, M. Sadik, M. Khaldoun
- Journal: Agriculture, Volume 12, Issue 1, Article 9, 2021
- Citations: 98
- Real-Time Detection of Crop Leaf Diseases Using Enhanced YOLOv8 Algorithm
- Authors: H. Orchi, M. Sadik, M. Khaldoun, E. Sabir
- Conference: 2023 International Wireless Communications and Mobile Computing (IWCMC)
- Citations: 10
- Automation of Crop Disease Detection Through Conventional Machine Learning and Deep Transfer Learning Approaches
- Authors: H. Orchi, M. Sadik, M. Khaldoun, E. Sabir
- Journal: Agriculture, Volume 13, Issue 2, Article 352, 2023
- Citations: 9
- A General Survey on Plants Disease Detection Using Image Processing, Deep Transfer Learning, and Machine Learning Techniques
- Authors: H. Orchi, M. Sadik, M. Khaldoun
- Conference: Ubiquitous Networking: 7th International Symposium, UNet 2021, Virtual Event
- Citations: 4
- A Novel Hybrid Deep Learning Model for Crop Disease Detection Using BEGAN
- Authors: H. Orchi, M. Sadik, M. Khaldoun
- Conference: International Symposium on Ubiquitous Networking, 267-283, 2022
- Citations: 2