Role of digital, hyper spectral, and SAR images in detection of plant disease with deep learning network

Bhujade, Vaishali G; Sambhe, Vijay

doi:10.1007/s11042-022-13055-z

Cited by 11 publications

(4 citation statements)

References 88 publications

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“…Moreover, the exploration of alternative methods for detecting maize leaf diseases has led to the consideration of multi-spectral and hyper-spectral imaging [28] , [29] . These advanced techniques involve capturing images of maize leaves at various wavelengths and subsequently analyzing these images to identify disease symptoms.…”

Section: Related Workmentioning

confidence: 99%

A multi-scale feature fusion neural network for multi-class disease classification on the maize leaf images

Liu,

Qiao,

Chang

et al. 2024

Heliyon

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Section: Related Workmentioning

confidence: 99%

A multi-scale feature fusion neural network for multi-class disease classification on the maize leaf images

Liu,

Qiao,

Chang

et al. 2024

Heliyon

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“…It can challenge even experts in visual differentiation [26]. Likewise, computer vision algorithms encounter difficulties in detecting these diseases [27]. In the case of weed detection, weeds with the same leaf colour as the plant to be protected, spectral features could lead to a low performance in detecting the weeds [28].…”

Section: Challenges In Agricultural Image Datasetsmentioning

confidence: 99%

Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network

Modak,

Heil,

Stein

2024

Remote Sensing

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Image preprocessing and fusion are commonly used for enhancing remote-sensing images, but the resulting images often lack useful spatial features. As the majority of research on image fusion has concentrated on the satellite domain, the image-fusion task for Unmanned Aerial Vehicle (UAV) images has received minimal attention. This study investigated an image-improvement strategy by integrating image preprocessing and fusion tasks for UAV images. The goal is to improve spatial details and avoid color distortion in fused images. Techniques such as image denoising, sharpening, and Contrast Limited Adaptive Histogram Equalization (CLAHE) were used in the preprocessing step. The unsharp mask algorithm was used for image sharpening. Wiener and total variation denoising methods were used for image denoising. The image-fusion process was conducted in two steps: (1) fusing the spectral bands into one multispectral image and (2) pansharpening the panchromatic and multispectral images using the PanColorGAN model. The effectiveness of the proposed approach was evaluated using quantitative and qualitative assessment techniques, including no-reference image quality assessment (NR-IQA) metrics. In this experiment, the unsharp mask algorithm noticeably improved the spatial details of the pansharpened images. No preprocessing algorithm dramatically improved the color quality of the enhanced images. The proposed fusion approach improved the images without importing unnecessary blurring and color distortion issues.

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“…For example, in industry, it can be used to predict soil content in mines [8]. In agriculture, hyperspectral technology can be used for plant classification [9] and identification [10], crop yield prediction [11], and plant disease analysis [12]. In the medical field, hyperspectral technology has become an auxiliary tool for disease diagnosis [13].…”

Section: Introductionmentioning

confidence: 99%

A Calculation Method for the Hyperspectral Imaging of Targets Utilizing a Ray-Tracing Algorithm

Cao,

et al. 2024

Remote Sensing

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This paper proposes a hyperspectral imaging simulation method based on a ray-tracing algorithm. The algorithm combines calculations based on solar and atmospheric visible light radiation as well as the spectral bidirectional reflection distribution function (BRDF) of the target surface material and can create its own scenarios for simulation calculations on demand. Considering the presence of multiple scattering between the target and background, using the ray-tracing algorithm enables the precise computation of results involving multiple scattering. To validate the accuracy of the algorithm, we compared the simulated results with the theoretical values of the visible light scattering intensity from a Lambertian sphere. The relative error obtained was 0.8%. Subsequently, a complex scene of engineering vehicles and grass was established. The results of different observation angles and different coating materials were calculated and analyzed. In summary, the algorithm presented in this paper has the following advantages. Firstly, it is applicable to geometric models composed of any triangular mesh elements and accurately computes the effects of multiple scattering. Secondly, the algorithm combines the spectral BRDF information of materials and improves the efficiency of multiple scattering calculations using nonuniform sampling. The computed hyperspectral scattering data can be applied to simulate airborne or space-borne remote sensing data.

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Role of digital, hyper spectral, and SAR images in detection of plant disease with deep learning network

Cited by 11 publications

References 88 publications

A multi-scale feature fusion neural network for multi-class disease classification on the maize leaf images

A multi-scale feature fusion neural network for multi-class disease classification on the maize leaf images

Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network

A Calculation Method for the Hyperspectral Imaging of Targets Utilizing a Ray-Tracing Algorithm

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