In commercial production, potatoes are stored in boxes or bulk depending on several factors, such as the variety of the potatoes and their marketability. While bulk storage is more economical, traceability is more difficult compared to boxed storage. Accordingly, this research aims to develop a system that combines the simplicity and traceability of both bulk and boxed storage, respectively. Using ESP32 ultra-wideband (UWB) transmitters within the storage facility, we plan to track the orientation and position of the bin-piler, which is a conveyor that arranges the potatoes in piles by dropping them from different heights and degrees of rotation while moving backwards. By locating the tip of the piler, we can identify the approximate location of tagged potatoes. The potatoes, in turn, are tagged by a machine vision station that detects specific quality attributes before entering the storage facility. The synchronization of tuber location data and corresponding quality attributes is done by tracking the speed of the conveyors and using wireless communication between the machine vision and UWB transmitters. The real-time locating system was tested at Dalhousie University, and using 4 anchors, a tag device was located throughout a 16m² 3D grid, with an average accuracy of 82 cm. Improvements on this initial result are planned before deploying the locating system at a storage facility to study the effects of weather conditions, and expanding tuber piles on locating accuracy.

Organic farming faces many productivity and profitability challenges. In sweet corn crop, Trichogramma, tiny parasitoid wasps, can control Ostrinia nubilalis (Hübner), European corn borer, as effectively as insecticides. However, trichocards (envelope containing stuck-on parasitized eggs) used to release these natural predators has limitations: manufacturing cost and labor time for manual installation. To address this problem, the release of Trichogramma pupae on a large scale using a spray solution of water and xanthan and guar gums was mechanized using an innovative spraying approach developed at the Department of Soils and Agri-Food Engineering of Université Laval. An injection system was designed and mounted on the prototype leading to a successful release of Trichogramma from a 57 L modular secondary tank to the flat fan nozzles. Moreover, a unique design of an agitation mobile allowed dissolving the gums in less than six minutes and achieved a concentration up to 12 g/L. During field trials, spraying of pupae under the foliage (800,000 ind. ha-1) using hose drops was first compared to a control treatment and confirmed a significant parasitism (18.54±4.38%) of the sentinel masses compared to that in the control plots (0%). Then, a 6 m spaced spray path treatment (800,000 ind. ha-1) was compared to a trichocard treatment (400,000 ind. ha-1) and resulted in comparable European corn borer control and more cost-effective method on a large scale ($120/ha vs $179/ha). This inventive and easy-to-use technology will promote the use of Trichogramma on a large scale, both in the field and in the greenhouse!

Many feedlot hydrology studies have been conducted that include rainfall simulations that generate runoff. In these simulations, rainfall is generally applied to a small area and few full-scale trials have been conducted. The focus of this trial was to simulate rainfall in a research pen at the University of Saskatchewan Livestock and Forage Centre of Excellence (LFCE) and determine the amount of rainfall required to generate runoff. This information is useful as it may impact the regulation of runoff controls associated with feedlots.

Feedlot pen surfaces are often uneven due to bedding mounds and hoof imprints. Bedding and manure, organic matter, in the pens will also affect the total precipitation that is required before runoff is generated. The water storage capacity of the feedlot pen will be impacted by these factors.

Full-scale rainfall simulations were conducted on two LFCE research pens. The rainfall is being simulated using several sprinklers attached to a distribution header, which is supplied by a pump and several large water tanks. Equivalent precipitation was monitored using several rain gages disbursed throughout the pens. Pen surface sampling was conducted throughout the experiment to monitor changing moisture conditions in the pen.

Additional rainfall simulations will be conducted in the spring of 2023. Initial experimental results will be presented and discussed.

This study aims to develop an electronic control unit (ECU) named machine vision node (MVN) that standardizes nozzle control for targeted pest spraying by bridging machine vision or vision systems and boom spraying systems. The MVN serves as a communication intermediary between the two systems via the controller area network (CAN) bus, a communication protocol used in automotive including agricultural machinery for high-speed serial communication between microcontrollers and devices, allowing for reliable data transmission. The MVN consists of two single-board microcontrollers that are connected to a CAN transceiver. Two algorithms are developed and deployed on the microcontroller boards. The first algorithm receives protocol messages from vision systems and identifies regions where pests are visible within the camera's field of view (FOV). The FOV is divided into multiple sections, where each section corresponds to a spraying area covered by a single nozzle. The second algorithm receives an array of nozzle sections and generates a corresponding CAN frame to control the nozzles. CAN frames are messages transmitted over the CAN bus to control different components, such as nozzles. The MVN is capable to read protocol messages from vision systems and concurrently sends CAN frames to the CAN bus instantaneously. The MVN offers the flexibility to integrate different vision systems with CAN-compatible boom sprayers, enabling real-time site-specific spraying. Overall, this work provides a standardized approach to controlling individual nozzles on boom sprayers using machine vision systems, promoting precision agriculture practices.