Detection of White Leaf Disease in Sugarcane Crops Using UAV-Derived RGB Imagery with Existing Deep Learning Models

Narmilan, A.; Gonzalez, Felipé; Salgadoe, Arachchige Surantha Ashan; Sandino, Juan; Powell, Kevin

doi:10.3390/rs14236137

Cited by 21 publications

(15 citation statements)

References 79 publications

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“…This transition marks a significant advancement in monitoring techniques, with UAVs providing centimetre scale spatial resolution [ 10 ]. UAVs have been used in remote sensing to detect and segment numerous types of objects and different environments, such as agricultural fields, urban areas, forests, and bodies of water, providing valuable data for various applications including environmental monitoring, disaster management, and infrastructure inspection [ 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. These technological tools, often accompanied by classical machine learning (ML) and deep learning (DL) methodologies, enhance the accuracy and efficiency of different vegetation mapping [ 18 , 19 , 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Antarctica’s Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI

Raniga,

Amarasingam,

Sandino

et al. 2024

Sensors

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Vegetation in East Antarctica, such as moss and lichen, vulnerable to the effects of climate change and ozone depletion, requires robust non-invasive methods to monitor its health condition. Despite the increasing use of unmanned aerial vehicles (UAVs) to acquire high-resolution data for vegetation analysis in Antarctic regions through artificial intelligence (AI) techniques, the use of multispectral imagery and deep learning (DL) is quite limited. This study addresses this gap with two pivotal contributions: (1) it underscores the potential of deep learning (DL) in a field with notably limited implementations for these datasets; and (2) it introduces an innovative workflow that compares the performance between two supervised machine learning (ML) classifiers: Extreme Gradient Boosting (XGBoost) and U-Net. The proposed workflow is validated by detecting and mapping moss and lichen using data collected in the highly biodiverse Antarctic Specially Protected Area (ASPA) 135, situated near Casey Station, between January and February 2023. The implemented ML models were trained against five classes: Healthy Moss, Stressed Moss, Moribund Moss, Lichen, and Non-vegetated. In the development of the U-Net model, two methods were applied: Method (1) which utilised the original labelled data as those used for XGBoost; and Method (2) which incorporated XGBoost predictions as additional input to that version of U-Net. Results indicate that XGBoost demonstrated robust performance, exceeding 85% in key metrics such as precision, recall, and F1-score. The workflow suggested enhanced accuracy in the classification outputs for U-Net, as Method 2 demonstrated a substantial increase in precision, recall and F1-score compared to Method 1, with notable improvements such as precision for Healthy Moss (Method 2: 94% vs. Method 1: 74%) and recall for Stressed Moss (Method 2: 86% vs. Method 1: 69%). These findings contribute to advancing non-invasive monitoring techniques for the delicate Antarctic ecosystems, showcasing the potential of UAVs, high-resolution multispectral imagery, and ML models in remote sensing applications.

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Section: Introductionmentioning

confidence: 99%

Monitoring of Antarctica’s Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI

Raniga,

Amarasingam,

Sandino

et al. 2024

Sensors

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“…Research on agricultural UAVs includes aspects such as agricultural monitoring and disaster assessment using remote sensing technology [13][14][15][16]. In terms of UAV operations, applications such as fertilization, pesticide spraying, and seeding have been explored [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Design and Experimentation of Rice Seedling Throwing Apparatus Mounted on Unmanned Aerial Vehicle

Yuan,

Yang,

Wei

et al. 2024

Agriculture

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In order to further exploit the production advantages of rice throwing, this paper proposes a systematically designed throwing device suitable for integration with unmanned aerial vehicles (UAVs). The device primarily comprises a seedling carrying and connection system, a seedling pushing mechanism, and a seedling guiding device. The operational principles and workflow of the device are initially elucidated. Subsequently, an analysis of factors influencing rice throwing effectiveness is conducted, with throwing height, working speed, and the bottom diameter of the seedling guide tube identified as key factors. Seedling spacing uniformity and seedling uprightness are designated as performance indicators. A three-factor, three-level response surface experiment is conducted, yielding regression models for the experimental indicators. Through an analysis of the response surface, the optimal parameter combination is determined to be a throwing height of 142.79 cm, a working speed of 55.38 r/min, and a bottom diameter of the seedling guide tube of 43.51 mm. At these parameters, the model predicts a seedling spacing uniformity of 88.43% and a seedling uprightness of 88.12%. Field experiments validate the accuracy of the optimized model results. Experimental data indicate that under the optimal operational parameters calculated by the regression model, the seedling spacing uniformity is 86.7%, and the seedling uprightness is 84.2%. The experimental results meet the design requirements, providing valuable insights for UAV rice-throwing operations.

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“…Satellites, unoccupied aerial vehicles, and rovers have been used with different types of sensors for disease detection (Raza et al ., 2020; Sandino et al ., 2018; Su et al ., 2020). Red-Green-Blue (RGB) cameras capture visible light from 400-700 nm and can be used to detect disease symptoms (Amarasingam et al ., 2022). Hyperspectral cameras capture wavelengths across the electromagnetic radiation spectrum and have been used to identify plant disease before visible symptoms occur (Behmann et al ., 2014).…”

Section: Introductionmentioning

confidence: 99%

An RGB based deep neural network for high fidelity Fusarium head blight phenotyping in wheat

Cooper,

Du,

Beaver

et al. 2023

Preprint

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Fusarium head blight (FHB) in wheat is an economically important disease, which can cause yield losses exceeding 50% and the causal pathogen that infects spikes produces harmful mycotoxins. Breeding for host resistance remains the most effective disease control method; but time, labor, and human subjectivity during disease scoring limits selection advancements. In this study we describe an innovative, high-throughput phenotyping rover for capturing in-field RGB images and a deep neural network pipeline for wheat spike detection and FHB disease quantification. The image analysis pipeline successfully detects wheat spikes from images under variable field conditions, segments spikes and diseased tissue in the spikes, and quantifies disease severity as the region of intersection between spike and disease masks. Model inferences on an individual spike and plot basis were compared to human visual disease scoring in the field and on imagery for model evaluation. The precision and throughput of the model surpassed traditional field rating methods. The accuracy of FHB severity assessments of the model was equivalent to human disease annotations of images, however individual spike disease assessment was influenced by field location. The model was able to quantify FHB in images taken with different camera orientations in an unseen year, which demonstrates strong generalizability. This innovative pipeline represents a breakthrough in FHB phenotyping, offering precise and efficient assessment of FHB on both individual spikes and plot aggregates. The model is robust to different conditions and the potential to standardize disease evaluation methods across the community make it a valuable tool for studying and managing this economically significant fungal disease.

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Detection of White Leaf Disease in Sugarcane Crops Using UAV-Derived RGB Imagery with Existing Deep Learning Models

Cited by 21 publications

References 79 publications

Monitoring of Antarctica’s Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI

Monitoring of Antarctica’s Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI

Design and Experimentation of Rice Seedling Throwing Apparatus Mounted on Unmanned Aerial Vehicle

An RGB based deep neural network for high fidelity Fusarium head blight phenotyping in wheat

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