Precision Disease Control in Bed-Grown Crops

Zande, J.C. van de; Achten, V.T.J.M.; Schepers, H.T.A.M.; Lans, A. van der; Kempenaar, C.; Michielsen, J.G.P.; Stallinga, H.; Velde, P. van

doi:10.1007/978-90-481-9277-9_25

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…A number of studies exist in literature on vision and AI based site-specific agricultural spraying. The work reported in [11] presents treatment based on the plant density and foliage shape. For autonomous spraying systems in greenhouses, the work published in [12] has employed a 270-degree laser scanning sensor to find targets with complicated shapes.…”

Section: State Of the Artmentioning

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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Section: State Of the Artmentioning

confidence: 99%

“…The response of disturbance attenuation function, d n is added to the control action of the cascaded controller, u, the resultant control action, is expressed by the Eq (11).…”

Section: Plos Onementioning

confidence: 99%

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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“…The first proposed solutions applied spatially variable herbicide doses [3]. However, it later evolved to control the amount of water and pesticide used based on factors such as foliage shape and volume [4].…”

Section: Introductionmentioning

confidence: 99%

2D LiDAR-Based System for Canopy Sensing in Smart Spraying Applications

Baltazar

Santos

Sousa³

et al. 2023

IEEE Access

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The efficient application of phytochemical products in agriculture is a complex issue that demands optimised sprayers and variable rate technologies, which rely on advanced sensing systems to address challenges such as overdosage and product losses. This work developed a system capable of processing different tree canopy parameters to support precision fruit farming and environmental protection using intelligent spraying methodologies. This system is based on a 2D light detection and ranging (LiDAR) sensor and a Global Navigation Satellite System (GNSS) receiver integrated into a sprayer driven by a tractor. The algorithm detects the canopy boundaries, allowing spray only in the presence of vegetation. The spray volume spared evaluates the system's performance compared to a Tree Row Volume (TRV) methodology. The results showed a 28% reduction in the overdosage of spraying product. The second step in this work was calculating and adjusting the amount of liquid to apply based on the tree volume. Considering this parameter, the saving obtained had an average value for the right and left rows of 78%. The volume of the trees was also monitored in a georeferenced manner with the creation of a occupation grid map. This map recorded the trajectory of the sprayer and the detected trees according to their volume.INDEX TERMS Agricultural robotics, canopy characterization, precision spraying, robotics.

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“…This strategy allows to better limit the spray liquid to the targeted crop and to minimise losses between the beds. Spray patterns for different bed widths and the ability to apply different dose rates depending on the crop canopy density were studied [17][18][19] However, these studies did not consider the possibility of adjusting nozzle spacing and boom height to the canopy width that increases during the growing season. The benefit of spray configurations with adjustable nozzle spacing and height is still to be demonstrated in terms of canopy deposition, ground losses, and spray drift, possibly further reducing PPP use and environmental contamination, as was the goal in this study.…”

Section: Introductionmentioning

confidence: 99%

Performance of a Prototype Boom Sprayer for Bed-Grown Carrots Based on Canopy Deposition Optimization, Ground Losses and Spray Drift Potential Mitigation in Semi-Field Conditions

Lamare

Zwertvaegher²,

Nuyttens³

et al. 2022

Applied Sciences

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The H2020-project OPTIMA concept of smart sprayer relies on several functionalities, including variable nozzle spacing for bed-grown carrots, based on an air-assisted boom sprayer. A prototype boom was designed and evaluated though canopy deposition, ground losses, and spray drift potential. Four bed spray configurations, including various nozzle types, angles, and sizes (XR8004, combination of AIUB8504/AI11004, AI8004, and XR8002) at the most appropriate nozzle spacing and height, were tested and compared to a broadcast application (XR11004). Deposition measurements were performed on carrots in bins at early and full-grown stages with respective target zone width of 1.4 m and 2.2 m. Spray drift potential measurements were performed following ISO 22401, 2015. The spray boom was equipped with an air sleeve providing different air speeds (0, 4, 8 m s−1). The relative depositions at both growth stages showed a significant effect of spray configuration and lowest values were found for the broadcast application. The configurations consisting of air inclusion nozzles generated the lowest drift potential compared to the broadcast application, although not significantly different. Bed spray configurations can thus improve canopy depositions and spray drift potential compared to a conventional broadcast application when the boom height and the nozzle spacing are adjusted to the growth stage.

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Precision Disease Control in Bed-Grown Crops

Cited by 6 publications

References 6 publications

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

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

2D LiDAR-Based System for Canopy Sensing in Smart Spraying Applications

Performance of a Prototype Boom Sprayer for Bed-Grown Carrots Based on Canopy Deposition Optimization, Ground Losses and Spray Drift Potential Mitigation in Semi-Field Conditions

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