Smarter Robotic Sprayer System for Precision Agriculture

Baltazar, André Rodrigues; Santos, Filipe Neves dos; Moreira, António Paulo; Valente, António; Boaventura‐Cunha, José

doi:10.3390/electronics10172061

Cited by 26 publications

(11 citation statements)

References 33 publications

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“…For decades, intelligent sprayers have been used in precision agriculture to increase crop yields and aid in management decisions. [29][30][31]. The development of these systems began with component-level improvements including nozzle droplet size and material use and management, and it has evolved to full system integration utilizing cameras and sensors for plant-specific application.…”

Section: Opportunity To Adapt Agricultural Technologymentioning

confidence: 99%

Reducing Distracted Driving and Improving Consistency with Brine Truck Automation

Mahlberg,

Mathew,

Desai

et al. 2024

Electronics

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Salt brine is routinely used by transportation agencies to pre-treat critical infrastructure such as bridges, ramps, and underpasses in advance of winter storms. This requires an operator turning on and off brine controls while driving at highway speeds, introducing driver distraction and consistency challenges. In urban areas, such as Indianapolis, a 5500-gallon tractor trailer with a gross vehicle weight of 80,000 pounds is typically used and the driver may have 1200 on/off activations while covering 318 miles during a pre-treatment shift. This study conducted in collaboration with Indiana Department of Transportation has worked with their truck upfitters to adapt geo-fenced agriculture spraying controls to seven trucks that use the Global Positioning System (GPS) position of the truck to activate the sprayer valves when the trucks enter and exit geo-fenced areas that require pre-treatment. This automated brine system enhances safety, reduces driver workload, and ensures the consistent application of brine in designated areas. Furthermore, as additional environmental constraints and reporting requirements evolve, this system has the capability of reducing application rates in sensitive areas and provides a comprehensive geo-coded application history. The Indiana Department of Transportation has scaled deployment for treating interstates and major arterials with brine. This deployment on 5500-gallon tankers, used on I-64/65/69/70/74, and 465, eliminates over 10,000 driver distraction events during every statewide pre-treatment event.

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Section: Opportunity To Adapt Agricultural Technologymentioning

confidence: 99%

Reducing Distracted Driving and Improving Consistency with Brine Truck Automation

Mahlberg,

Mathew,

Desai

et al. 2024

Electronics

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“…Haar feature-based cascade classifiers have been used to detect three commonly occurring weed types in a maize field. In [10], with the help of a Support Vector Machine (SVM) classifier and features extracted from image histograms, authors have created a crop perception system that determines the leaf density for the quantity of spray required on the plant. The accuracy score of the leaf density classifier varies from 80% to 85%.…”

Section: State Of the Artmentioning

confidence: 99%

“…Advantages offered by electric sprayers as compared to traditional methods have been summarized in [10]. As mentioned, despite their existing limitations such as limited battery power and the challenge of achieving a sufficient level of ingress protection for field conditions, electric vehicles supplemented by solar energy harvesting will revolutionize the field of precision agriculture in the near future.…”

Section: Field Robot Subsystems Overviewmentioning

confidence: 99%

“…The inner loop uses the rate of change of the pressure as feedback and u o as a reference point to develop a set point for the secondary proportional controller with a proportional gain C v . The inner loop governing equation is given by Eq (9) and the accumulative control action is calculated as a PWM signal and is given by Eq (10).…”

Section: Spraying Control Systemmentioning

confidence: 99%

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Precision agricultural robotic sprayer with real-time Tobacco recognition and spraying system based on deep learning

Nasir¹,

Tufail

Haris³

et al. 2023

PLoS ONE

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Precision agricultural techniques try to prevent either an excessive or inadequate application of agrochemicals during pesticide application. In recent years, it has become popular to combine traditional agricultural practices with artificial intelligence algorithms. This research presents a case study of variable-rate targeted spraying using deep learning for tobacco plant recognition and identification in a real tobacco field. An extensive comparison of the detection performance of six YOLO-based models for the tobacco crop has been performed based on experimentation in tobacco fields. An F1-score of 87.2% and a frame per second rate of 67 were achieved using the YOLOv5n model trained on actual field data. Additionally, a novel disturbance-based pressure and flow control method has been introduced to address the issue of unwanted pressure fluctuations that are typically associated with bang-bang control. The quality of spray achieved by attenuation of these disturbances has been evaluated both qualitatively and quantitatively using three different spraying case studies: broadcast, and selective spraying at 20 psi pressure; and variable-rate spraying at pressure varying from 15-120 psi. As compared to the broadcast spraying, the selective and variable rate spray methods have achieved up to 60% reduction of agrochemicals.

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“…Sprayers and agricultural machines are also equipped with vision systems that ensure precise spraying and targeted cultivation [4,5]. Thus, it can be noticed that the control technology of autonomous vehicles [6][7][8] is beginning to be used for practical applications on arable plantations [9][10][11]. The level of robotic autonomy is raised by introducing control systems using intelligent data processing [12,13].…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Vision System for Supporting Autonomous Orchard Spraying

Góral,

Pawłowski,

Piniarski

et al. 2024

Electronics

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In this article, the authors propose a multi-agent vision system supporting the autonomous spraying of orchards and analyze the condition of trees and occurrence of pests and diseases. The vision system consists of several agents: first, for the detection of pests and diseases of fruit crops; second, for the estimation of the height of trees to be covered with spraying; third, for the classification of the developmental status of trees; and fourth, for the classification of tree infections by orchard diseases. For the classification, modified deep convolutional neural networks were used: Xception and NasNetLarge. They were trained using transfer learning and several additional techniques to avoid overfitting. Efficiency tests performed on the datasets with real orchard photos, showing accuracies ranging from 96.88% to 100%. The presented solutions will be used as part of an intelligent autonomous vehicle for orchard works, in order to minimize harm to the environment and reduce the consumption of water and plant protection products.

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Smarter Robotic Sprayer System for Precision Agriculture

Cited by 26 publications

References 33 publications

Reducing Distracted Driving and Improving Consistency with Brine Truck Automation

Reducing Distracted Driving and Improving Consistency with Brine Truck Automation

Precision agricultural robotic sprayer with real-time Tobacco recognition and spraying system based on deep learning

Multi-Agent Vision System for Supporting Autonomous Orchard Spraying

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