Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard Operations

Corno, Matteo; Furioli, Sara; Cesana, Paolo; Savaresi, Sergio M.

doi:10.3390/agronomy11020287

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…In the case of vision-based autonomous navigation in orchards, several algorithms have been developed that take advantage of terrestrial structures such as tree trunks and foliage. Many approaches combine data from two or more sensors in order to detect and locate objects, while in recent years RGB-D, Kinect and other sensors have been widely used [104]. Even though methods with sensor fusion perform satisfactorily in orchards, they are challenged by shading, the altering angle of the sun, the chromatic similarity of crops, the visibility of tree trunks in the adjacent rows, etc.…”

Section: Discussionmentioning

confidence: 99%

Computer Vision in Self-Steering Tractors

et al. 2022

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Automatic navigation of agricultural machinery is an important aspect of Smart Farming. Intelligent agricultural machinery applications increasingly rely on machine vision algorithms to guarantee enhanced in-field navigation accuracy by precisely locating the crop lines and mapping the navigation routes of vehicles in real-time. This work presents an overview of vision-based tractor systems. More specifically, this work deals with (1) the system architecture, (2) the safety of usage, (3) the most commonly faced navigation errors, (4) the navigation control system of tractors and presents (5) state-of-the-art image processing algorithms for in-field navigation route mapping. In recent research, stereovision systems emerge as superior to monocular systems for real-time in-field navigation, demonstrating higher stability and control accuracy, especially in extensive crops such as cotton, sunflower, maize, etc. A detailed overview is provided for each topic with illustrative examples that focus on specific agricultural applications. Several computer vision algorithms based on different optical sensors have been developed for autonomous navigation in structured or semi-structured environments, such as orchards, yet are affected by illumination variations. The usage of multispectral imaging can overcome the encountered limitations of noise in images and successfully extract navigation paths in orchards by using a combination of the trees’ foliage with the background of the sky. Concisely, this work reviews the current status of self-steering agricultural vehicles and presents all basic guidelines for adapting computer vision in autonomous in-field navigation.

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Section: Discussionmentioning

confidence: 99%

Computer Vision in Self-Steering Tractors

et al. 2022

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“…To solve the above problems, this study proposes a method based on a sensor that can measure distance and can thus determine the measured object's diameter, height, and agronomic planting spacing requirements to establish the corresponding recognition model. Ultrasonic sensors are more adaptable to the environment [24]. Laser-ranging sensors are characterized by high accuracy, high speed, and high stability, can avoid the influence of light, shadow, and other environmental factors, are low cost, and are more effective in use [25].…”

Section: Introductionmentioning

confidence: 99%

Research and Experiment on Soybean Plant Identification Based on Laser Ranging Sensor

Ye,

Xue,

Sun

et al. 2023

Agronomy

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When endeavoring to study the complex growth conditions of soybean plants under natural conditions, a problem arises due to the similar appearances of both soybean plants and weeds. To address this issue, a soybean plant recognition model based on a laser ranging sensor is proposed. To demonstrate the applicability of the soybean plant recognition model, experiments are conducted using ultrasonic sensors and laser ranging sensors to analyze the diameter, height, and spacing conditions in the model. A test environment is built, and during the pre-test, the laser range sensor detects objects with diameters of 3 mm and 5 mm with two and three measurement points, respectively, at a speed of 0.2 m/s. At a speed of 0.3 m/s, there is one measurement point for objects with 3 mm diameter and two measurement points for objects with 5 mm diameter. At 0.4 m/s, there are also one and two measurement points for objects with diameters of 3 mm and 5 mm, respectively. These results demonstrate that the laser range sensor can more accurately recognize the diameter conditions of soybean plants and weeds and can distinguish between the diameters of soybean plants and weeds. Subsequently, the recognition rate of the model is evaluated by observing whether the weeding mechanism can synchronize seedling avoidance after the soybean plant passes through the sensor. The recognition rates of the optimized model at speeds of 0.2 m/s, 0.3 m/s, and 0.4 m/s are 100%, 98.75%, and 93.75%, respectively. Upon comprehensive analysis, the soybean plant recognition model is determined to achieve a recognition rate of 98.75% at a speed of 0.3 m/s, which is considered a moderate speed, and demonstrates more stable recognition of plant diameters. The test further verifies the reliability and effectiveness of the method for distinguishing between soybean plants and weeds. The research results can serve as a reference for recognizing soybean plants based on the use of laser ranging sensors.

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“…The fast pacing diffusion of automatic systems and controls for agricultural tractors is a trend that has been undergoing for decades now: modern tractors are provided with more and more driving assistance features and autonomous systems, as evident from recent works in the area [1,2] but also from a review of future challenges and perspectives in the Internet-of-Things (IoT) [3].…”

Section: Introductionmentioning

confidence: 99%

Driving Style Assessment System for Agricultural Tractors: Design and Experimental Validation

2022

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The diffusion of electronics and sensors in agricultural vehicles is enabling a revolution in the field, leading—among the rest—to the introduction of advanced driving-assistance systems (ADAS). From this perspective, the three key performance indicators (KPI) in a tractor are indeed the driving safety, fuel consumption, and operator comfort. Such indexes describe the way the driver interacts with the vehicle, the environment, and other vehicles, respectively. Therefore, such information would be particularly valuable if promptly provided to the driver, e.g., on a dashboard visualizer, so as to adapt the driving style accordingly. Within this context, we propose an algorithmic solution for the on-line estimation of such KPIs. More specifically, by using an off-the-shelf smart-sensor equipped with an Electronic Control Unit (ECU), the chassis accelerations are first processed to extract physics-inspired features and then used to assess the safety and comfort levels; similarly, the speed profile is used to evaluate the economicity of the driving style. The developed method is based upon a cheap setup, and thus it is industrially amenable for its simplicity and robustness. A sensitivity analysis to establish the best sensor placement is finally carried out, together with an extensive experimental campaign considering offroad, urban, and circuit paths.

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Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard Operations

Cited by 9 publications

References 21 publications

Computer Vision in Self-Steering Tractors

Computer Vision in Self-Steering Tractors

Research and Experiment on Soybean Plant Identification Based on Laser Ranging Sensor

Driving Style Assessment System for Agricultural Tractors: Design and Experimental Validation

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