Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery

Zhang, Tianxiang; Xu, Zhiyong; Su, Jinya; Yang, Zhifang; Liu, Cunjia; Chen, Wenhua; Li, Jiangyun

doi:10.3390/rs13193892

Cited by 27 publications

(19 citation statements)

References 51 publications

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“…Multispectral and hyperspectral cameras could be an effective tool for disease symptoms automatic detection. They are more robust than RGB cameras against different illumination conditions making them more reliable to distinguish between healthy and stressed plants Zhang et al [ 87 ]. According to Théau et al [ 77 ], it is difficult to distinguish between different crop stress types from multispectral data.…”

Section: Uav-based Visual Remote Sensing Systems Used To Identify Cro...mentioning

confidence: 99%

“…Several recent studies on crop diseases detection from UAV imagery are based on deep learning models to overcome the limitations of traditional techniques, especially Convolutional Neural Network (CNN) algorithms. Most of these studies targeted subsistence crops such as wheat Pan et al [ 56 ], Su et al [ 72 ], Zhang et al [ 87 ], maize Wiesner-Hanks et al [ 80 ], Stewart et al [ 71 ], potato Théau et al [ 77 ], Siebring et al [ 66 ], and tomato Abdulridha et al [ 3 ], Abdulridha et al [ 2 ].…”

Section: Deep Learning Algorithms To Identify Crop Diseases From Uav-...mentioning

confidence: 99%

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A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

et al. 2022

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The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers’ naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is based on the use of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) provides an effective solution to achieve agriculture applications, including plant disease identification and treatment. In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end, in this review, we analyze the recent advances in the use of computer vision techniques that are based on deep learning algorithms and UAV technologies to identify and treat crop diseases.

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Section: Uav-based Visual Remote Sensing Systems Used To Identify Cro...mentioning

confidence: 99%

Section: Deep Learning Algorithms To Identify Crop Diseases From Uav-...mentioning

confidence: 99%

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

et al. 2022

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“…U-Net is a convolutional network architecture for fast and precise segmentation of images [34], which has been applied for yellow rust disease mapping in [5,32]. In this study, U-Net is used as the representative deep-learning model for satellite image classification.…”

Section: U-net Trainingmentioning

confidence: 99%

“…Terrestrial laser scanning is also used in [2] to investigate physically based characterization of mixed floodplain vegetation. Meanwhile, the interest in agricultural applications of remote sensing technology has also been exponentially growing since 2013 [3], where the main applications of remote sensing in agriculture include phenotyping, land-use monitoring, crop yield forecasting, precision farming and the provision of ecosystem services [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Snow Coverage Mapping by Learning from Sentinel-2 Satellite Multispectral Images via Machine Learning Algorithms

Wang

Zhai

et al. 2022

Remote Sensing

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Snow coverage mapping plays a vital role not only in studying hydrology and climatology, but also in investigating crop disease overwintering for smart agriculture management. This work investigates snow coverage mapping by learning from Sentinel-2 satellite multispectral images via machine-learning methods. To this end, the largest dataset for snow coverage mapping (to our best knowledge) with three typical classes (snow, cloud and background) is first collected and labeled via the semi-automatic classification plugin in QGIS. Then, both random forest-based conventional machine learning and U-Net-based deep learning are applied to the semantic segmentation challenge in this work. The effects of various input band combinations are also investigated so that the most suitable one can be identified. Experimental results show that (1) both conventional machine-learning and advanced deep-learning methods significantly outperform the existing rule-based Sen2Cor product for snow mapping; (2) U-Net generally outperforms the random forest since both spectral and spatial information is incorporated in U-Net via convolution operations; (3) the best spectral band combination for U-Net is B2, B11, B4 and B9. It is concluded that a U-Net-based deep-learning classifier with four informative spectral bands is suitable for snow coverage mapping.

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“…During the epidemic period, wheat stripe rust will cause the wheat yield to decrease by more than 40% [1,2]. It is a low-temperature, high-moisture, high-light fungal disease that produces fungal spores on leaves infected by this pathogen and forms narrow yellow stripes parallel to the leaf veins, with dashed stripes [3]. In the later stages of spore development, the epidermis will rupture and a rust-colored powdery substance will appear [2].…”

Section: Introductionmentioning

confidence: 99%

Combining Random Forest and XGBoost Methods in Detecting Early and Mid-Term Winter Wheat Stripe Rust Using Canopy Level Hyperspectral Measurements

et al. 2022

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Appropriate modeling methods and feature selection algorithms must be selected to improve the accuracy of early and mid-term remote sensing detection of wheat stripe rust. In the current study, we explored the effectiveness of the random forest (RF) algorithm combined with the extreme gradient boosting (XGboost) method for early and mid-term wheat stripe rust detection based on the vegetation indices extracted from canopy level hyperspectral measurements. Initially, 21 vegetation indices that were related to the early and mid-term winter wheat stripe rust were calculated on the basis of canopy level hyperspectral reflectance. Subsequently, the optimal vegetation index combination for disease detection was determined using correlation analysis (CA) combined with RF algorithms. Then, the disease severity detection model of early and mid-term winter wheat stripe rust was constructed using XGBoost method based on the optimal vegetation index combination. For the evaluation and comparison of the initial results, three commonly used classification methods, namely, RF, backpropagation neural network (BPNN), and support vector machine (SVM), were utilized. The vegetation index combinations determined by the single CA algorithm were also used to construct detection models. Compared with the detection models based on the vegetation index combination obtained using the single CA algorithm, the overall accuracy of the four detection models based on the optimal vegetation index combination based on CA combined with RF algorithms increased by 16.1% (XGBoost), 9.7% (RF), 8.1% (SVM), and 8.1% (BPNN). Among the eight models, the XGBoost detection model based on the optimal vegetation index combination using CA combined with RF algorithms, CA-RF-XGBoost, achieved the highest overall accuracy of 87.1% and the highest kappa coefficient of 0.798. Our results indicate that the RF combined with XGBoost can improve the detection accuracy of early and mid-term winter wheat stripe rust effectively at canopy scale.

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Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery

Cited by 27 publications

References 51 publications

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

Snow Coverage Mapping by Learning from Sentinel-2 Satellite Multispectral Images via Machine Learning Algorithms

Combining Random Forest and XGBoost Methods in Detecting Early and Mid-Term Winter Wheat Stripe Rust Using Canopy Level Hyperspectral Measurements

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