Dr. Ahmad Al-Mallahi
Project title: Development of rock mapping system for rock picking robotic arm
Removing rocks off an agricultural field is a typical farm operation that takes place at the beginning of each season, which is accomplished usually manually. Due to ageing and the shortage of labourers, there is a need to mechanize this operation. In this project, the objective is to develop a smart mechanism to locate rocks in a field before dispatching a robotic arm to pick the rocks. At the first stage of this project, a camera will be programmed and mounted on a potato planter to scan the field as the planter drops and buries potato under soil. The images taken by the camera will be processed to detect the rocks. Meanwhile, GPS data collected from the planter will be matched with the processing results to create a rock distribution map. This work will take place at the DAL-AC campus as well as at a commercial potato field in New Brunswick. The field work will include collecting samples of rocks and bringing them back to campus for analysis and the beginning of a design of an end-effector to grip the rocks and lift them. Students interested in this project will work with a team of graduate students at the Department of Engineering. The requirements for this project are basic knowledge of computer programming, the basics of electric circuits, and Engineering drawing using CAD or other software.
Project title: Backend computation of nutrient concentration in plants using nutrient sensor
In-season concentration of nutrients in plants is an essential piece of information to determine the fertilization scheme. The objective of this project is to develop a sensor to detect nutrients in plants based on spectroscopy to replace manual and chemical measurements which take weeks to deliver results. This project includes scanning potato plants in field using a spectrophotometer connected to the internet, developing nutrient sensing models plugged in a computation cloud, and delivering nutrient results to mobiles via an App. The research work in this project includes collecting samples of leaves and petioles of potato plants to build up datasets, testing and validating the sensing models plugged on the cloud, and streamlining the process of data delivery via the cloud to the mobile. This work will take place at the DAL-AC campus as well as at a commercial potato field in New Brunswick. The field work will include collecting leaf and petiole samples and handling them, using a spectrophotometer to scan dried and fresh leaves, and development of real-time results notification system to a mobile via the cloud. Students interested in this project will work with a team of graduate students at the Department of Engineering as well as the R&D Department at McCain Foods. The requirements for this project are basic knowledge of computer programming and the basics of statistics. Prior experience working in an agriculture field will be considered as an asset.
Project title: Machine vision to detect beetles and larvae in potato fields for autonomous spraying
Spot application is a term used in agriculture when the sprayer is able to detect solely the pest and avoid spraying unnecessarily. Nevertheless, the practical application of this technology is faced by several challenges including the ability to detect pests correctly especially the ones characterized as tiny and / or augmented. In potato cropping system Colorado potato beetle is an aggressive pest which should be sprayed when the beetle is in the larvae stage. Trying to detect larvae using a camera mounted on a sprayer may seem a logical method to detect larvae but its tiny size in comparison to leaves and the existence of other tiny objects in the image background makes it a difficult object to detect. In this project, using a set of cameras mounted on a sprayer, more images will be taken to annotate larvae in preparation to building an image processing software to detect them. Meanwhile, using an existing subset of images, an initial version of the software will be developed. This work will take place at the DAL-AC campus as well as at a commercial potato field in New Brunswick. The field work will include operating machine vision system mounted on a sprayer, using software to annotate objects, and helping in writing software to detect them. Students interested in this project will work with a team of graduate students at the Department of Engineering. The requirements for this project are basic knowledge of computer programming and electric circuits.
Project title: Smart control for autonomous spraying based on Controller Area Network
Real-time autonomous spraying based on sensor is an interesting application of precision agriculture that are faced by many challenges to be realized. One of them is the mechanical constraints due to the slow response of mechanical components in comparison to the speed of data flow. In this research, a smart controller that can pro-actively regulate flow of pesticides in pipes in response to data generated by machine vision is being developed. The flow of data is based on a communication protocol called Controller Area Network found in all modern automobiles characterized by the virtual lack of communication errors. In the project, a control unit that consists of microcontrollers will be developed and tested in lab and field. Also, the design of pumping system of pesticides will be investigated. . This work will take place at the DAL-AC campus as well as at a commercial potato field in New Brunswick. The field work will include learning the basics of machine communication, helping in testing the controller, data arrangement and illustration, and possible field work mounting hardware on actual sprayer. Students interested in this project will work with a team of graduate students at the Department of Engineering. The requirements for this project are basic knowledge of computer programming, the basics of electric circuits, and Engineering drawing using CAD or other software.
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Dr. Yunfei Jiang
Department of Plant, Food, and Environmental Sciences
yunfei.jiang@dal.ca
Project 1: Pea-brassica intercrops
The objective of this project is to explore underlying mechanisms driving outyielding potential in pea-brassica intercrops compared to sole crops, as well as to evaluate the carryover effects of pea-brassica intercrops on the subsequent carrots/potatoes. Field trials will be conducted at the Plumdale Field Station on College Road, Bible Hill, NS during the growing season of 2025.
Project 2: Effect of enhanced efficiency fertilizer on soil microbiology
There are potential agronomic and environmental benefits of enhanced efficiency fertilizers (EEF), such as reducing N loss and greenhouse gas emission as well as improving crop yield. However, limited information is available regarding the effect of the coating materials and inhibitors of these EEF products on soil microbiology. This proposed project will fill in the gap by studying the effect of EEF products on soil microbiology. This is a lab-based project and will be co-supervised by Dr. Rhea Lumactud and Dr. Yunfei Jiang.
Project 3: Winter canola and |
