Located in North Africa, Algeria has sought to support agriculture because of its potential in this sector. Indeed, it has put in place several agricultural policies and the objective was to achieve food security by substituting local production for imported products. One of the most important goals is the development of modern and sustainable protected greenhouse cultivation in Algeria. Managing greenhouses by means of AI technologies allows growers to be more focused on their crops and provides control at their fingertips. Proposed by the University of Ain Temouchent, Algeria, this project aims to develop ML models for the management of intelligent greenhouses.
The management of all equipment under one control system, including heating, venting, and irrigation, is a hard task in terms of systems management and data collection. As a seasonal grower with product cycles of up to two years in duration, patience is necessary. It takes time to collect the data for these systems to work and learn.
Greenhouse environments are also challenging for technology implementation due to broad temperature and humidity ranges, which influence both the electronic and mechanical components that contribute to their ongoing development. This can be a frustration for staff trying to complete their weekly plans.
AI solutions for greenhouse growers are still in their initial phases of development. The integration of intelligent control systems requires changes to processes, which can be disruptive to production, so flexibility and managing expectations are important to manage the greenhouses effectively.
This project will be divided into two parts:
First part: 4 weeks in total
2 weeks for Hybrid workshops (online and in-person at the University of Ain Temouchent, Algeria)
2 weeks for hands-on experience, including dividing participants into teams for building and replicating projects related to the topic. Final presentations from the participants are suggested.
[week 1] Data Collection
Understanding the problem
Data pre-processing and data labeling
Deployment of Application
1. Data collection;
2. Data Processing;
3. Labeling of Data;
4. ML Model for controlling the parameters inside the greenhouse;
5. ML Model for classification of plants diseases;
6. Deployment of the whole system.