Conference Agenda

Traditional farming methods for fruits and vegetables often involve labor-intensive tasks such as planting, harvesting, and sorting, which can be time-consuming and expensive. While automation technologies have made significant advancements in other sectors of agriculture, adapting these technologies to the unique requirements of fruits and vegetable production poses challenges. Recent advancements in robotics, computer vision, and machine learning are helping to overcome these challenges. However, there remains a notable gap in the adoption of automation technologies, specifically lack of technical skills among the agricultural workforces. Our research focused on the adaptive modular automation for adaptive transformation. Modular components would easily mount on a self-propelled unit (tractor, ATV, or others) as an auxiliary component or attach to fully autonomous equipment (UGV) to perform the intended operation. In this study, will present the performance intelligent cutting mechanism for autonomous lettuce harvester as a modular automation system. It is essential to maintain the consistent cutting height for supermarket quality products. An electrohydraulic control has been designed for autonomous height adjustment for consistent cut. Performance will be evaluated for control system accuracy and lettuce harvesting accuracy at the lab setting. Lettuce cutting will be evaluated for three different blade types, two different blade speed and two different forward speed. Results will be presented during the conference.

This study focuses on the development of an ISOBUS (i.e. ISO 11783) compatible Electronic Control Unit (ECU) for seamless integration between a boom sprayer and machine vision to control individual nozzles for targeted pest spraying. The ECU, powered by a Raspberry Pi 4B and a CAN transceiver, utilizes the AgIsoStack++ (formerly known as ISOBUS++), an open-source library for ISOBUS communication. Through this library, we register our ECU to the ISOBUS network with a manufacturer ID (0x1C) and all parameters of ISOBUS NAME from a registered and inactive manufacturer under the Agricultural Industry Electronics Foundation (AEF). The developed algorithm is specifically ISOBUS Section Control Function (SC) which manages the individual sections of implements, such as nozzle sections on a sprayer controlled by individual cameras for precise spraying operations. The interactive Virtual Terminal (VT) screen design utilizes another freely available software, ISO-Designer. After successful handshaking messages holding 0x1C as the source or destination ID, the designed VT screen becomes visible. The ECU can adjust and control parameters such as camera and nozzle ratios through the ISOBUS compatible VT. Deployment across three different ISOBUS systems resulted in zero error messages, ensuring seamless network registration. The ECU evaluation reveals a 7.1% busload increase on the ISOBUS. The maximum observed busload over those three systems reached 21.3%, remaining within the acceptable limit of 25%. Despite the increase in busload, the ECU increases robustness by integrating the machine vision to VT and simplifies operation by reducing the need for additional displays and interfaces, thereby enhancing farm efficiency.

Wild blueberries (Vaccinium angustifolium Ait.) are a perennial crop in northeastern North America. Conventional management relies on costly broadcast applications of herbicides to manage weeds. There is an opportunity to reduce agrochemical usage by implementing targeted spray applications with the help of deep learning. A machine vision system for targeting weeds in wild blueberry fields was developed as a retrofit package for commercial boom sprayers. The YOLOv5n convolutional neural network (CNN) was trained to detect hair fescue (Festuca filiformis Pourr.) in images of wild blueberry fields. Cameras with an integrated deep learning processor were used to capture images and detect target weeds in real-time. An ISOBUS individual nozzle control system consisting of addressable nozzle bodies and a rate controller was connected to the cameras using a local area network and a custom-built machine vision node. This machine vision spraying system was installed on a 12-nozzle prototype research sprayer built on a John Deere Gator XUV 825i. Testing occurred in Nova Scotia wild blueberry fields in November and December 2023, then April and May 2024. Future work will involve testing the machine vision system on a 27-nozzle commercial wild blueberry sprayer and testing CNNs for targeting different species of weeds in wild blueberry fields. The machine system is adaptable to other crops by retraining the CNN with new images of the desired target weeds. A machine vision smart sprayer will allow growers to achieve substantial cost savings by selectively applying herbicides based on real-time visual data.

Since 1972, wild blueberry harvesters have relied on 0.86 m wide picking heads to optimize field efficiency while ensuring maximum yields. Recently, Doug Bragg Enterprises introduced a 1.47 m picking head, and the purpose of this study was to compare its field efficiency to the conventional 0.86 m header and hand raking. The experiment used a randomized complete block design with four blocks in each field. Within each block, a 75 m strip was harvested with the 0.86 m and 1.47 m heads and the total harvested berry weights were compared to the harvested area. For hand raking, 10, 1 m by 1 m, randomly positioned quadrats were harvested per 75 m strip. In terms of harvested yield by area, there were no significant differences between the 0.86 m head, the 1.47 m head, or hand raking (p = 0.676). This result is encouraging, considering the 1.47 m picking head can harvest 0.166 ha h-1 while the 0.86 m head can only harvest 0.097 ha h-1 at a typical harvesting speed of 0.31 m s-1. Finally, the tested fields had maximum slopes of 9.4 degrees, making them reasonably flat among Nova Scotian wild blueberry fields, which often range up to 30 degrees of slope. Future research should consider header performance in fields with more uneven topography.

Remote supervision of autonomous agricultural machines involves the interaction of the human supervisor and these autonomous machines through an interface. These interfaces need to provide humans with accessible and effective information to perform their supervisory roles, which include setting tasks, allocating resources, monitoring the execution of tasks, and intervening in cases of emergencies. Various warning methods, specifically visual, auditory and tactile cues have been used to inform remote supervisors about any abnormalities in the autonomous machines. Failure to adequately understand the warning information may lead to system breakdown or crop damage. The present study assessed which warning method (visual, auditory or vibrotactile) is most effective in conveying comprehension of machine malfunctions to remote supervisors of autonomous agricultural machines. Twenty-five (25) participants were recruited and asked to interact with a simulation of an autonomous sprayer. Three (3) unimodal warning methods (visual, auditory and tactile sensory cues) were evaluated. The effectiveness of the warning methods was assessed based on the modality’s ability to convey comprehension (which is a measure of level two situation awareness) as a function of accuracy of the participants’ responses. At this time data analysis is being conducted; we plan to share preliminary results at the conference. This study will inform designs that can enhance performance and minimize hazards experienced by remote supervisors during field operation with the autonomous agricultural machines.

With the rise of semi-autonomous tractors in agriculture, there might be a chance to rethink traditional tractor cab designs. This research involves transforming these cabs into ergonomic office spaces, acknowledging that the operator's focus has shifted away from driving. In the initial phase of the project, the emphasis was on conducting an ergonomic assessment of the existing tractor cab design, specifically concentrating on the factors contributing to discomfort. This examination involved analyzing the duration spent on rearward observation, the sequence and frequency of control usage, and ergonomic triggers leading to discomfort. The investigation into tractor ergonomics encompassed interviews with farmers and design engineers, as well as practical experiences of operating tractors through ride-along. Subsequent research delved into an anthropometric analysis, examining the proportions of the human body, and identifying areas prone to discomfort, with a particular emphasis on hand positioning. The results of the ergonomic analysis have pinpointed several issues, including neck and back pain resulting from frequent backward glances, prolonged exposure to vibrations, restricted legroom, and discomfort arising from complex button layouts. Simultaneously, the findings of the anthropometric analysis are being consolidated to generate recommendations tailored for engineers. These recommendations offer insights into key considerations for enhancing human comfort during tractor operation. The ultimate objective of the project's concluding phase is to integrate the insights gained from both ergonomic evaluations and anthropometric analysis into a conceptual design of a comfortable environment for operators of semi-autonomous tractors, effectively transforming the tractor cab into a workspace resembling of an office setting.