Background: 

Precision agriculture is evolving rapidly, with real-time plant tissue analysis emerging as a valuable tool for optimizing crop inputs and nutrition. Traditional plant tissue sampling relies on laboratory analysis, which can delay corrective actions. The Canadian company, Picketa Systems' Leaf Evaluated Nutrient System (LENS) offers real-time, in-field nutrient analysis, helping growers make timely decisions to enhance yield and sustainability. Haggerty AgRobotics collaborated with Picketa Systems in 2024 to compare corn and winter wheat tissue samples analyzed by the LENS™ sensor against traditional lab-based methods. The partnership also contributed to the early development of a winter wheat model.  

In 2025, further trials will refine the technology for broader adoption in grain crops. 

Objectives:

Evaluate Picketa’s LENS™ technology as an alternative to traditional tissue sampling. 

Support the development of a predictive model for winter wheat nutrient analysis. 

Assess the system's ability to deliver real-time, georeferenced nutrient results. 

Determine the theoretical impact of real-time analysis on agronomic decision-making and yield potential. 

*Note: Funding for this project was provided by the Ontario Agri-Food Innovation Alliance,  Innovation Farms ON, powered by AgExpert, and the Fresh Vegetable Growers of Ontario. The FarmDroid FD20 and Naïo Orio were provided by Haggerty AgRobotics Inc.

Background:

There is a shortage of agricultural labour for crop production in Canada, including for vegetable production in Ontario. This project will evaluate the battery-powered Naio Orio and the solar-powered FarmDroid, which are commercially available robots. The use of agricultural robots will replace hand weeding, and also reduce the hours needed to operate tractors. Hand weeding is done by workers in the target vegetable crops because there are few or no herbicides available or there are herbicide-resistant weeds. This project will evaluate the robots on vegetables grown on muck soil - carrots, onions and beets - and on carrots and beets on mineral soil.  

Objectives:

These trials involved measuring crop yield and management performance with the use of two leading autonomous systems – the Naio Orio, and FarmDroid FD20. Haggerty AgRobotics supported the technology and implement control system integration, farmer Ian Smith provided the land and control trial management, and the University of Guelph - Muck Research Station designed and conducted the trials and performed analysis on the results. This project began in 2023 with plans to continue to 2025 to measure operational costs and efficiencies, agronomic performance, and farmer adoption. The data for the ROI for the FarmDroid & Orio is being compiled by Mary Ruth McDonald’s team. 

Commercial Stream: Weather Network for Growers 

Innovation Farms Ontario through Haggerty AgRobotics can establish a regional weather station network throughout desired areas within Ontario in order to provide local weather, crop staging and disease modeling insights to its grower network. This initiative will support proactive decision-making by integrating real-time, localized data into farm management practices such as scouting and spraying. 

Key Benefits: 

• Enhanced Regional Models: Improved accuracy in staging and disease forecasts for 

Innovation Farms ON and growers. 

• Logistics & Operations Planning: Utilize ForeSite’s data for optimized timing on crop staging, disease pressure, and weather analytics. 

• Task Optimization: Data-driven insights to help growers with scouting, spraying schedules, and field accessibility for machinery. 

• Grower Engagement: Know who to talk to and when through allowing your staff and growers access local weather and local modelling information. 

Note: This project is a partnership between Innovation Farms Ontario and Area X.O, who are conducting this research with Haggerty AgRobotics, Ottawa Smart Farm, Foster Family Farms, Algonquin College and the University of Ottawa.

Background: 

Pumpkin fields are prone to aggressive weeds like ragweed and pigweed, requiring intensive manual removal. Pumpkin farming is highly labor-intensive, especially when it comes to weed management. Early in the season, emerging weeds (barnyard grass, ragweed, pigweed, etc.) can quickly overrun young pumpkin plants, threatening yields. Traditionally, farmers rely on crews to hand-weed and hoe between rows, often spending 30–60 labor-hours per acreon hand-weeding in organic systems. This manual labor is not only costly but increasingly hard to find – a challenge that has been worsening due to farm labour shortages. By integrating an autonomous weeding robot (Naio Orio), the project aims to alleviate the labour bottleneck, maintain healthier fields, and protect pumpkin yields through consistent, efficient weed management. 

Objectives:

1. Labour Cost Reduction and Efficiency Gains: A primary goal is to dramatically cut the hours of manual weeding. We expect the Orio robot to replace a significant portion of hand-hoeing crews. Success will be measured by the reduction in labor hours per acre and associated cost savings. 

2. Weed Suppression and Efficacy: The effectiveness of weed control will be quantified by measuring weed density and regrowth. KPIs include the percentage of weeds removed by the robot and the weed pressure reduction over the season compared to control plots. We are targeting a weed removal efficacy of 90% or higher, based on prior field robot results (evaluations of NAIO’s earlier Dino robot showed it could remove ~90% of weeds). 

3. Crop Yield and Quality Improvements: By reducing weed competition during the crucial growth period, the project expects improved pumpkin yields. We will measure pumpkin yield per acre (number of marketable pumpkins or weight in tonnes) and compare robot-weeded plots to traditionally managed plots. 

4. Environmental Impact Metrics: While this is not the primary target of the project, we propose to track the following sustainability KPIs to quantify the relative environmental benefits and expand the impact of the project: 

1. Herbicide use reduction: documented through any decrease in herbicide applications per season 

2. Soil health and disturbance: documented either through observing the soil structure or absence of soil erosion 

3. Carbon footprint and energy use: documented through the reduction in fuel consumption and the corresponding cut in CO2 emissions 

4. Battery performance at scale: documented based on hours of operation per charge and acres covered per charge 

Oz works completely autonomously thanks to its RTK GPS guidance system. Helps with weeding and hoeing chores.