Aerial Monitoring of Plant Stress in Greenhouse Grown Seedlings and High-Wire Sweet Pepper
Development
Jul 31, 2025
Aug 10, 2026
Objective
The core goal is to elevate Saiwa’s plant health monitoring system from prototype to validated, production-ready software. Specifically, we aim to:
Detect early signs of plant stress, including chlorosis (leaf yellowing) and wilting, in high-wire and seedling-stage sweet pepper crops.
Provide greenhouse operators with spatial maps of affected zones.
Reduce reliance on prophylactic pesticide applications through targeted, data-driven interventions.
Technology and Innovation
The platform integrates:
RGB and multispectral imagery from greenhouse environments.
Zero-shot and few-shot learning techniques for stress detection.
Deep learning models enhanced by anomaly detection modules.
Real-time image preprocessing and mapping, converting raw data into actionable orthomosaics.
Cloud-based access via Saiwa’s Sairone platform.
The models localize stressed plants and notify operators via an intuitive interface—triggering robotic or manual intervention.
Partner: Vineland Research and Innovation Centre
Vineland is a non-profit research organization located in Ontario that delivers innovation through science and technology for the horticulture sector. Their team is providing:
Greenhouse space and crop cultivation for experimental sweet pepper trials.
Specialized equipment setup, including robotic and multispectral imaging.
Controlled stress induction for creating labeled training datasets.
Ground-truthing expertise to validate AI model outputs.
Their support ensures the trials represent real-world greenhouse conditions, which is crucial for model performance and commercial viability.
Visit Vineland Research and Innovation Centre to learn more.
Funding and Support: Ontario Centre of Innovation (OCI)
This project is supported by OCI through its CIT – Development and Commercialization (DC) Program, which fosters commercialization of Ontario-based innovations.
OCI Project Number: 36441
Learn more about OCI's programs at www.oc-innovation.ca.
Anticipated Outcomes
By project completion, Saiwa will deliver:
A validated software prototype for early stress detection in sweet pepper crops.
An API-ready AI model for integration into greenhouse management systems.
Support for robotic and manual inspection workflows.
A scalable module deployable via Sairone, Saiwa's precision agriculture platform.
The technology will ultimately improve greenhouse crop productivity while reducing input costs and enhancing sustainability practices.
Related Projects
Hemaa.ai, in partnership with Tarmeez, delivers an AI-powered solution for date palm plantations that enables early health monitoring, precise tree counting, and targeted weed detection using drone and satellite imagery. Leveraging advanced deep learning models, this platform enhances operational efficiency, reduces costs, and promotes sustainable agriculture through timely alerts and accurate spatial data mapping.
Collaboration with
Paramoria Agri-Science IncMolecular and Machine Learning-Based Monitoring of Fusarium Dry Rot in Potatoes
Jul 2025
The project introduces a groundbreaking solution to combat Fusarium dry rot, a major issue for potato producers in Alberta, causing millions in annual losses. The solution combines two advanced technologies: eDNA monitoring to track Fusarium spore populations and AI-powered wounding detection to identify mechanical damage during harvest. With industry partnerships, the project aims to develop species-specific Fusarium detection kits, integrate real-time monitoring, and reduce storage waste. By providing predictive insights, the project seeks to improve storage yield retention and offer proactive disease management tools to producers.
Collaboration with
Windward DronesAI-Powered Tree Identification & Counting from Drone Imagery
Jan 2026→Feb 2026
Saiwa collaborated with Windward Drones to develop an AI-powered solution that automatically identifies, counts, and maps individual trees from high-resolution drone imagery. By combining advanced computer vision with aerial data collection, the system transforms thousands of images into accurate, geo-referenced forestry insights in just minutes. The solution helps forestry professionals, landowners, and environmental organizations streamline inventory management, monitor large landscapes more efficiently, and make faster, data-driven decisions with detailed visual reports and spatial analytics.
GreenTech Projects partnered with Saiwa to develop an AI-powered computer vision workflow for automated seedling tray analysis using drone imagery. The project focused on detecting and localizing seedling blocks, identifying empty cavities within propagation trays, and improving the speed and consistency of nursery monitoring operations. By combining aerial imagery with machine learning–based image analysis, the project demonstrated the potential of automated nursery assessment for large-scale propagation environments.
The solution utilized RGB drone imagery collected over seedling fields and propagation areas. Saiwa developed and trained custom machine learning models capable of identifying tray structures and detecting empty cells across nursery blocks. The project was successfully completed and validated the feasibility of applying AI-based image processing to greenhouse and nursery production workflows
In early 2025, Saiwa and Airwyse collaborated to automate counting of pollination bags in pine orchards using autonomous drones and AI-driven image analysis. This innovative solution replaced labor-intensive manual counting, improving accuracy and efficiency in yield prediction. Tested on 44 pine trees, the system demonstrated promising results, paving the way for scalable orchard management automation.
Discover how Saiwa developed an AI-powered turf weed detection solution for Hirsala Golf, using aerial imagery and computer vision to identify and map weed-affected areas across golf-course turf and support more targeted, data-driven weed management.