Applications of Machine Learning techniques in Agricultural Engineering

Instructor: Hossein Bonakdari, PhD, P.Eng.
Department of Soil and Agri-Food Engineering, Laval University, Quebec, Canada

Date (part 1): Monday May 17 2021, 10 a.m. to 2 p.m. EDT
Date (part 2): Tuesday May 18 2021, 10 a.m. to 2 p.m. EDT
Location: Virtual
Language: This workshop will be in english
Cost: see Registration 

Workshop description:

Artificial intelligence (AI) techniques and machine learning approaches will revolutionize many aspects of future agriculture engineering field. AI can be used as a promising tool to tackle different problems but related aspects of agricultural practical cases as great concern all over the world. The main focus of this course is to understand and discuss the recent developments in AI applications relating to practical engineering application. This course introduces a variety of different topics in AI approaches and learning methods in modeling and prediction of complex datasets. All codes are user friendly and trainees after this course will be able to use them for their cases. 

Course Overview

This introductory one day workshop covers key topics in Artificial Intelligence application in Agricultural Engineering:

  1. Data acquisition/Preprocessing:
    • outliers detection,
    • transferring raw information into usable data,
    • splitting the data into training & testing sets
  2. Classification:
    • decision tree,
    • support vector machine.
  3. Modeling tools:
    • Multilinear Regression (MLR)
    • Multi-layer perceptrons (MLP)
    • Adaptive Network-based Fuzzy Inference System (ANFIS),
    • Extreme Learning Machines (ELM),
  4. Post processing:
    • analysis of statistical indices,
    • scatter plot

Following completion of this course, trainee should

  • have an understanding of major AI techniques,
  • have a working knowledge of how to apply AI technologies to real-world datasets,
  • have gained experience designing and applying AI techniques in Agricultural Engineering practical problems

Who can attend?

Anyone who wants to learn about practical application of machine learning techniques can attend the workshop. No knowledge about programming is required.

Please note that the participants should bring their own laptops with minimum specifications as described in following section.

Technical requirements:

Participants are requested to bring their own laptops with MATLAB software.

About the instructor

BonakdariHossein Bonakdari, Ph.D, P.Eng., earned his Ph.D in Civil Engineering at the University of Caen-France. He has worked for several organizations like most recently as faculty member of department of Soils and Agri-Food Engineering at Laval University, Quebec. He has supervised several PhD and MSc students with teaching experience of more than 12 years in field of Artificial Intelligence application in practical Engineering applications. His fields of specialization and interest include: practical application of soft computing techniques in engineering problems. Results obtained from his researches have been published in more than 180 papers in international journals (h-index=26). He has also more than 150 presentations in national and international conference. He published two books. Dr. Bonakdari is currently leading several research projects in collaboration with industrials partners.

For more information, please contact This email address is being protected from spambots. You need JavaScript enabled to view it.

Program