Projects

Saiwa Inc., in collaboration with Mohawk College, built the Follomotion back-end services—a suite enabling real-time motion capture, camera calibration, and pose estimation. Delivered within one month and supported for six months, the project provided scalable, API-first infrastructure that enhanced precision, adaptability, and reliability. The solution empowers industries such as sports, fitness, healthcare, and rehabilitation with cutting-edge motion tracking technology.

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.

In partnership with Vineland Research and Innovation Centre and with funding support from the Ontario Centre of Innovation (OCI), Saiwa is developing an advanced computer vision and AI-powered platform for early stress detection in greenhouse-grown sweet peppers. This project replaces an earlier cucumber-focused initiative and targets sweet pepper crops—another economically significant greenhouse product in Ontario. By leveraging drone and robotic imagery acquisition, alongside real-time deep learning models, this initiative aims to reduce losses from disease, optimize intervention timing, and improve crop yield and quality.

Phragmites Detector is an AI-driven system that uses drone imagery and adaptive machine learning to identify and track invasive Phragmites in wetlands. Designed for conservation groups, it offers scalable, accurate, and user-friendly tools for early detection, monitoring, and ecological reporting.

In late 2024, Saiwa Co and Ducks Unlimited Canada launched a project to improve the detection and monitoring of the invasive aquatic plant Water Soldier using drone imagery and deep learning. The plant threatens biodiversity and waterway management in ecosystems like Ontario’s Trent-Severn Waterway. Traditional control methods have limited success, making early detection critical. The project uses UAV-captured RGB images and machine learning models to identify Water Soldier, even when partially submerged. Challenges include overlapping vegetation, variable water clarity, and lighting conditions. To address these, the team implemented a lightweight, customized deep learning solution optimized for real-time drone use. The outcome is a scalable, proactive approach to managing invasive species, reducing both ecological and economic impact.

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.

We at Saiwa Inc. are thrilled to announce the successful completion of an innovative project focused on the measurement of interpupillary distance (IPD) and pupil height. This groundbreaking solution was developed in collaboration with Innovation Venture Farm to address the growing need for precision in optical device fitting and design. By leveraging cutting-edge AI techniques and the Mediapipe framework, our solution detects and calculates critical parameters such as the center of the pupil and the lowest point of glasses lenses. These measurements are crucial for applications in eyeglasses, VR/AR headsets, and other optical technologies. The results are delivered with unmatched accuracy and efficiency, empowering manufacturers to deliver tailored products to their users. This project also integrates Saiwa’s Annotation Services, which ensure data labeling precision and scalability, making it easier for users to achieve reliable outcomes.

Powered by Sairone and funded by OAFRI, this project leverages AI and drone imagery to detect herbicide-tolerant Canada Fleabane in Ontario soybean fields. Designed to empower farmers with no-code tools, it transforms raw agricultural images into actionable insights—enabling geotagged weed detection, herbicide mapping, and data ownership. By lowering tech adoption barriers, this initiative supports sustainable, privacy-first farming across Canada.

Discover how Saiwa Inc. leveraged cutting-edge AI and deep learning to detect surface defects on both white and black paper with high precision. This project revolutionized quality control for a leading paper manufacturer—improving accuracy, reducing waste, and enabling real-time defect insights.

To enhance Ducks Unlimited Canada (DUC)‘s capability for UAV-based surveillance of European Water Chestnut (EWC) through machine learning, we at Saiwa have previously implemented the initial version of the EWC detector software. In the second stage, we are in the process of finalizing the product’s features and upgrading its interfaces.The two primary features to be incorporated in this stage are as follows: Incremental learning for gradually training the deep network over time. This feature enables us to rectify false positive and false negative detections over time. Reporting the 3D universal coordinates of EWC locations using drone configuration and temporal GPS data.

We at Saiwa Inc. are pleased to announce that we have successfully completed an aluminium surface defect detection project in collaboration with AI-innovate Company for CastTechnology in Canada. In this project, using machine learning techniques and the networks we provide in our Anomaly Detection service, we detect and localize the location of micro and macro defects on a casting line, including: crack, frost, frost patch, longitude frost and mold oscillation. This service is delivered via a simple user interface where users can run the defect detection APIs.

RFDs are DNA sequences that induce a cleavage (cutting) reaction in a substrate strand in response to the presence of a target. In this project, we at saiwa team segmented and measured regions of interest (the dots that constitute the microarray) relative to the background in printed RFD images. Printed microarrays can be used to test for the presence of specific targets (i.e., bacteria). We used around 100 RFD images that were provided by Didar Lab., McMaster uni.​