Computer Vision based Automated Quantification of Agricultural Sprayers Boom Displacement

2025-06-24

AI Summary

Large self-propelled boom sprayers operating at high speeds (30 km/h) and wide widths (38 m) in complex field conditions suffer from unquantified boom displacement, leading to poor pesticide application accuracy. Method: This paper proposes a real-time boom motion monitoring method based on computer vision and multi-sensor fusion. We develop, for the first time, an agricultural-scenario-adapted YOLOv7/v8/v11 object detection and tracking system, integrated with inclinometer data to enable non-contact, high-precision estimation of vertical and lateral boom displacement. Contribution/Results: The method achieves sub-centimeter accuracy (error < 0.026 m) and detection accuracy >90%, overcoming limitations of conventional physics-based modeling or contact-type measurement. It provides reliable, high-fidelity motion data to support boom structural optimization and responsive intelligent control system development. With strong generalizability and engineering feasibility, the approach demonstrates significant potential for practical deployment in precision agriculture.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Application rate errors when using self-propelled agricultural sprayers for agricultural production remain a concern. Among other factors, spray boom instability is one of the major contributors to application errors. Spray booms' width of 38m, combined with 30 kph driving speeds, varying terrain, and machine dynamics when maneuvering complex field boundaries, make controls of these booms very complex. However, there is no quantitative knowledge on the extent of boom movement to systematically develop a solution that might include boom designs and responsive boom control systems. Therefore, this study was conducted to develop an automated computer vision system to quantify the boom movement of various agricultural sprayers. A computer vision system was developed to track a target on the edge of the sprayer boom in real time. YOLO V7, V8, and V11 neural network models were trained to track the boom's movements in field operations to quantify effective displacement in the vertical and transverse directions. An inclinometer sensor was mounted on the boom to capture boom angles and validate the neural network model output. The results showed that the model could detect the target with more than 90 percent accuracy, and distance estimates of the target on the boom were within 0.026 m of the inclinometer sensor data. This system can quantify the boom movement on the current sprayer and potentially on any other sprayer with minor modifications. The data can be used to make design improvements to make sprayer booms more stable and achieve greater application accuracy.

Problem

• Quantify spray boom displacement to reduce application errors
• Develop computer vision system for real-time boom movement tracking
• Improve sprayer boom stability and application accuracy

Innovation

• Computer vision tracks sprayer boom movement
• YOLO models quantify vertical and transverse displacement
• Inclinometer validates neural network accuracy