&amp;lt;i&amp;gt;Crop canopy measurement using laser and ultrasonic sensing integration&amp;lt;/i&amp;gt;

Buelvas, Roberto M.; Adamchuk, Viacheslav I.

doi:10.13031/aim.201701002

Cited by 2 publications

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“…The agricultural applications considering laser sensors, a LiDAR (Light Detection and Ranging) technology, started in silviculture during the 1960s to estimate forest biomass, such as eucalyptus, by means of aerial platforms named as ALS (Airborne Laser Scanning) . Buelvas and Adamchuk (2017) indicated that the main advantage of using LiDAR is the capability to determine the growth vegetation dynamics, which allows, to enhance logistical aspects of harvest planning. The research perspective is to integrate the spectroscopy technique (multispectral data), ranging sensors, and crop/soil data to establish management zones within the field.…”

Section: Introductionmentioning

confidence: 99%

High-resolution data for mapping the spatio-temporal variability of sugarcane fields

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Section: Introductionmentioning

confidence: 99%

High-resolution data for mapping the spatio-temporal variability of sugarcane fields

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Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network

Gang,

Sutthanonkul,

Lee

et al. 2024

Sensors

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Estimating canopy volumes of strawberry plants can be useful for predicting yields and establishing advanced management plans. Therefore, this study evaluated the spatial variability of strawberry canopy volumes using a ResNet50V2-based convolutional neural network (CNN) model trained with RGB images acquired through manual unmanned aerial vehicle (UAV) flights equipped with a digital color camera. A preprocessing method based on the You Only Look Once v8 Nano (YOLOv8n) object detection model was applied to correct image distortions influenced by fluctuating flight altitude under a manual maneuver. The CNN model was trained using actual canopy volumes measured using a cylindrical case and small expanded polystyrene (EPS) balls to account for internal plant spaces. Estimated canopy volumes using the CNN with flight altitude compensation closely matched the canopy volumes measured with EPS balls (nearly 1:1 relationship). The model achieved a slope, coefficient of determination (R2), and root mean squared error (RMSE) of 0.98, 0.98, and 74.3 cm3, respectively, corresponding to an 84% improvement over the conventional paraboloid shape approximation. In the application tests, the canopy volume map of the entire strawberry field was generated, highlighting the spatial variability of the plant’s canopy volumes, which is crucial for implementing site-specific management of strawberry crops.

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<i>Crop canopy measurement using laser and ultrasonic sensing integration</i>

Cited by 2 publications

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High-resolution data for mapping the spatio-temporal variability of sugarcane fields

High-resolution data for mapping the spatio-temporal variability of sugarcane fields

Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network

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