Wheat Spikes Height Estimation Using Stereo Cameras

Zaji, Amir Hossein; Liu, Zheng; Xiao, Gaozhi; Bhowmik, Pankaj; Sangha, Jatinder Singh; Ruan, Yuefeng

doi:10.1109/tafe.2023.3262748

Cited by 6 publications

(2 citation statements)

References 71 publications

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“…For example, Tanaka et al [24] attained a ground sampling distance (GSD) of 2 mm per pixel through ground-based image acquisition, while other researchers such as Zhang et al [25], reported GSD values in the centimeter range per pixel for various flight altitudes of UAV imagery in the context of agricultural monitoring. Several studies have employed stereo vision-based approaches with rigidly installed camera configurations to monitor the morphological and geometric parameters over time [26][27][28][29]. However, current research shows limitations in terms of insufficient spatio-temporal resolution to effectively track dynamic changes in geometric plant traits, such as growth spurts, plant elasticity, or fructification.…”

Section: Introductionmentioning

confidence: 99%

Automated Workflow for High-Resolution 4D Vegetation Monitoring Using Stereo Vision

Kobe,

Elias,

Merbach

et al. 2024

Remote Sensing

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Precision agriculture relies on understanding crop growth dynamics and plant responses to short-term changes in abiotic factors. In this technical note, we present and discuss a technical approach for cost-effective, non-invasive, time-lapse crop monitoring that automates the process of deriving further plant parameters, such as biomass, from 3D object information obtained via stereo images in the red, green, and blue (RGB) color space. The novelty of our approach lies in the automated workflow, which includes a reliable automated data pipeline for 3D point cloud reconstruction from dynamic scenes of RGB images with high spatio-temporal resolution. The setup is based on a permanent rigid and calibrated stereo camera installation and was tested over an entire growing season of winter barley at the Global Change Experimental Facility (GCEF) in Bad Lauchstädt, Germany. For this study, radiometrically aligned image pairs were captured several times per day from 3 November 2021 to 28 June 2022. We performed image preselection using a random forest (RF) classifier with a prediction accuracy of 94.2% to eliminate unsuitable, e.g., shadowed, images in advance and obtained 3D object information for 86 records of the time series using the 4D processing option of the Agisoft Metashape software package, achieving mean standard deviations (STDs) of 17.3–30.4 mm. Finally, we determined vegetation heights by calculating cloud-to-cloud (C2C) distances between a reference point cloud, computed at the beginning of the time-lapse observation, and the respective point clouds measured in succession with an absolute error of 24.9–35.6 mm in depth direction. The calculated growth rates derived from RGB stereo images match the corresponding reference measurements, demonstrating the adequacy of our method in monitoring geometric plant traits, such as vegetation heights and growth spurts during the stand development using automated workflows.

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Section: Introductionmentioning

confidence: 99%

Automated Workflow for High-Resolution 4D Vegetation Monitoring Using Stereo Vision

Kobe,

Elias,

Merbach

et al. 2024

Remote Sensing

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“…Traditional methods for detecting wheat lodging are manual that rely on visual and subjective rating of the plants’ stature, which is highly laborious phenotyping and a time consuming activity ( Ali et al., 2023 ; Modi et al., 2023 ; Zaji et al., 2023 ). These methods are more prone to error depending on a number of factors, including the expertise of the person rating the lodging and, the time of rating after the lodging, to some extent, affected by extreme environmental conditions during the assessment, affecting the accuracy and reliability of the results ( Ali et al., 2019a ).…”

Section: Introductionmentioning

confidence: 99%

LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming

Ali,

Mohammed,

Bais

et al. 2023

Front. Plant Sci.

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Wheat lodging is a serious problem affecting grain yield, plant health, and grain quality. Addressing the lodging issue in wheat is a desirable task in breeding programs. Precise detection of lodging levels during wheat screening can aid in selecting lines with resistance to lodging. Traditional approaches to phenotype lodging rely on manual data collection from field plots, which are slow and laborious, and can introduce errors and bias. This paper presents a framework called ‘LodgeNet,’ that facilitates wheat lodging detection. Using Unmanned Aerial Vehicles (UAVs) and Deep Learning (DL), LodgeNet improves traditional methods of detecting lodging with more precision and efficiency. Using a dataset of 2000 multi-spectral images of wheat plots, we have developed a novel image registration technique that aligns the different bands of multi-spectral images. This approach allows the creation of comprehensive RGB images, enhancing the detection and classification of wheat lodging. We have employed advanced image enhancement techniques to improve image quality, highlighting the important features of wheat lodging detection. We combined three color enhancement transformations into two presets for image refinement. The first preset, ‘Haze & Gamma Adjustment,’ minimize atmospheric haze and adjusts the gamma, while the second, ‘Stretching Contrast Limits,’ extends the contrast of the RGB image by calculating and applying the upper and lower limits of each band. LodgeNet, which relies on the state-of-the-art YOLOv8 deep learning algorithm, could detect and classify wheat lodging severity levels ranging from no lodging (Class 1) to severe lodging (Class 9). The results show the mean Average Precision (mAP) of 0.952% @0.5 and 0.641% @0.50-0.95 in classifying wheat lodging severity levels. LodgeNet promises an efficient and automated high-throughput solution for real-time crop monitoring of wheat lodging severity levels in the field.

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Effective plant disease diagnosis using Vision Transformer trained with leafy-generative adversarial network-generated images

Singh,

Rao,

Chattopadhyay

et al. 2024

Expert Systems with Applications

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Wheat Spikes Height Estimation Using Stereo Cameras

Cited by 6 publications

References 71 publications

Automated Workflow for High-Resolution 4D Vegetation Monitoring Using Stereo Vision

Automated Workflow for High-Resolution 4D Vegetation Monitoring Using Stereo Vision

LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming

Effective plant disease diagnosis using Vision Transformer trained with leafy-generative adversarial network-generated images

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