Hyperspectral imagery applications for precision agriculture - a systemic survey

Sethy, Prabira Kumar; Pandey, Chanki; Sahu, Yogesh Kumar; Behera, Santi Kumari

doi:10.1007/s11042-021-11729-8

Cited by 37 publications

(20 citation statements)

References 178 publications

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“…Selfing is the behavior that optimizes the gene sequence of rice seeds in the restorer line, which makes rice seeds gradually approach the best one, and the updated Equation is shown in (2).…”

Section: Selfingmentioning

confidence: 99%

“…Recently, hyperspectral remote sensing has been broadly and successfully applied in urban planning [1], precision agriculture [2], environmental monitoring [3] and other fields with the constantly increased spectral resolution of sensors. Hyperspectral remote sensing combines spectral features with spatial images that can accurately identify and detect ground objects, which provides strong technical support for ground feature extraction [4].…”

Section: Introductionmentioning

confidence: 99%

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A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm

Cai

Liu

et al. 2022

Symmetry

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Hyperspectral image (HSI) analysis has become one of the most active topics in the field of remote sensing, which could provide powerful assistance for sensing a larger-scale environment. Nevertheless, a large number of high-correlation and redundancy bands in HSI data provide a massive challenge for image recognition and classification. Hybrid Rice Optimization (HRO) is a novel meta-heuristic, and its population is approximately divided into three groups with an equal number of individuals according to self-equilibrium and symmetry, which has been successfully applied in band selection. However, there are some limitations of primary HRO with respect to the local search for better solutions and this may result in overlooking a promising solution. Therefore, a modified HRO (MHRO) based on an opposition-based-learning (OBL) strategy and differential evolution (DE) operators is proposed for band selection in this paper. Firstly, OBL is adopted in the initialization phase of MHRO to increase the diversity of the population. Then, the exploitation ability is enhanced by embedding DE operators into the search process at each iteration. Experimental results verify that the proposed method shows superiority in both the classification accuracy and selected number of bands compared to other algorithms involved in the paper.

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Section: Selfingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm

Cai

Liu

et al. 2022

Symmetry

View full text Add to dashboard Cite

show abstract

“…W ITH intrinsic high spectral resolution and recently enhanced spatial resolution; hyperspectral imaging has been providing a powerful technology in depicting and differentiating a variation of materials of targets. In particular, hyperspectral imagery has become an advanced technique for earth observation-related applications, such as safety and security [1], environmental protection [2], precision agriculture [3], and medical science [4]. Among various applications, target detection (TD) has drawn increasing attention from researchers [5].…”

Section: Introductionmentioning

confidence: 99%

Regularized Tensor Representative Coefficient Model for Hyperspectral Target Detection

Shang

Jouni

et al. 2023

IEEE Geosci. Remote Sensing Lett.

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Target detection based on the representation of the hyperspectral image (HSI) has drawn the attention of researchers, given its powerful detection performance. The matrix-based approach inevitably loses spatial information and fails to explore the intrinsic multimodal structure of an HSI cube. In this paper, we propose a regularized tensor-based model without altering the data structure. The model assumes that an observed third-order HSI tensor is decomposed into the sum of a Total Variationregularized Low-rank background tensor and a Sparse (TVLrS) target tensor. The two tensors are then represented as the mode-3 product of a third-order tensor called the Tensor Representation Coefficient (TRC), by a matrix representing the spectra dictionary. The model is coined as TVLrS-TRC. Since the construction of a pure dictionary is essential for the background extraction, here, the background dictionary atoms are selected by the kernel spectral angle mapper (KSAM), while the target dictionary construction is done through the target atoms prior information. The tensor nuclear norm (TNN) is utilized to characterize the low rankness of the TRC subspaces, the TV norm is performed on each frontal slice of the TRC to encode its spatial smoothness, and the sparsity of the target TRC is characterized by the ℓ 1 -norm. Extensive experiments on two real hyperspectral data sets demonstrate the advantage of the proposed detector in comparison with several conventional and state-of-the-art target detectors.

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“…However, the accurate identification of plant seedlings is affected by the complex background environment such as light changes, weather, weeds, and terrain [2][3][4]. As an important data source for precision agriculture, drone remote sensing images can be used to protect crops, measure the quality of crops, and realize plant seedling identification [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

High-Precision Seedling Detection Model Based on Multi-Activation Layer and Depth-Separable Convolution Using Images Acquired by Drones

Zhang

Wang

et al. 2022

Drones

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Crop seedling detection is an important task in the seedling stage of crops in fine agriculture. In this paper, we propose a high-precision lightweight object detection network model based on a multi-activation layer and depth-separable convolution module to detect crop seedlings, aiming to improve the accuracy of traditional artificial intelligence methods. Due to the insufficient dataset, various image enhancement methods are used in this paper. The dataset in this paper was collected from Shahe Town, Laizhou City, Yantai City, Shandong Province, China. Experimental results on this dataset show that the proposed method can effectively improve the seedling detection accuracy, with the F1 score and mAP reaching 0.95 and 0.89, respectively, which are the best values among the compared models. In order to verify the generalization performance of the model, we also conducted a validation on the maize seedling dataset, and experimental results verified the generalization performance of the model. In order to apply the proposed method to real agricultural scenarios, we encapsulated the proposed model in a Jetson logic board and built a smart hardware that can quickly detect seedlings.

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Hyperspectral imagery applications for precision agriculture - a systemic survey

Cited by 37 publications

References 178 publications

A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm

A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm

Regularized Tensor Representative Coefficient Model for Hyperspectral Target Detection

High-Precision Seedling Detection Model Based on Multi-Activation Layer and Depth-Separable Convolution Using Images Acquired by Drones

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