A Review on Plant Disease Detection Using Hyperspectral Imaging

Rayhana, Rakiba; Ma, Zhenyu; Liu, Zheng; Xiao, Gaozhi; Ruan, Yuefeng; Sangha, Jatinder S.

doi:10.1109/tafe.2023.3329849

Cited by 3 publications

(3 citation statements)

References 298 publications

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“…However, in addition to simple demonstration, the dataset also serves as a fundamental resource for pre-training models, with the potential for subsequent fine-tuning using a custom dataset tailored to alternative diffraction gratings placed into the 3D-printed module. A modification to the diffraction grating with a new one capable of capturing data beyond the visible spectrum holds considerable promise for HI, enabling comprehensive analyses of diverse agricultural practices, including diseases, pests, fungi, and overall crop health [12,15]. Furthermore, due to the lack of readily available datasets that meet our specific needs, this approach emerges as the most feasible.…”

Section: Sparse Trainingmentioning

confidence: 99%

“…Moreover, numerous studies have been conducted by combining AI and hyperspectral imaging (HI) to monitor and improve performance in agricultural applications [9,10], while various publications prove the importance of multispectral and hyperspectral imaging [11][12][13][14]. Hyperspectral imaging is a powerful tool for analyzing biological samples and enabling precision agriculture, leading to cost savings, time efficiency, and a reduction in chemical fertilizer use [11,15]. It is a helpful tool for more easily recognizing agricultural diseases (e.g., by capturing and analyzing images of leaves or crops) and enables the more precise and timely identification of their physiological condition [11].…”

Section: Introductionmentioning

confidence: 99%

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A Citizen Science Tool Based on an Energy Autonomous Embedded System with Environmental Sensors and Hyperspectral Imaging

Kouzinopoulos,

Pechlivani,

Giakoumoglou

et al. 2024

JLPEA

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Citizen science reinforces the development of emergent tools for the surveillance, monitoring, and early detection of biological invasions, enhancing biosecurity resilience. The contribution of farmers and farm citizens is vital, as volunteers can strengthen the effectiveness and efficiency of environmental observations, improve surveillance efforts, and aid in delimiting areas affected by plant-spread diseases and pests. This study presents a robust, user-friendly, and cost-effective smart module for citizen science that incorporates a cutting-edge developed hyperspectral imaging (HI) module, integrated in a single, energy-independent device and paired with a smartphone. The proposed module can empower farmers, farming communities, and citizens to easily capture and transmit data on crop conditions, plant disease symptoms (biotic and abiotic), and pest attacks. The developed HI-based module is interconnected with a smart embedded system (SES), which allows for the capture of hyperspectral images. Simultaneously, it enables multimodal analysis using the integrated environmental sensors on the module. These data are processed at the edge using lightweight Deep Learning algorithms for the detection and identification of Tuta absoluta (Meyrick), the most important invaded alien and devastating pest of tomato. The innovative Artificial Intelligence (AI)-based module offers open interfaces to passive surveillance platforms, Decision Support Systems (DSSs), and early warning surveillance systems, establishing a seamless environment where innovation and utility converge to enhance crop health and productivity and biodiversity protection.

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Section: Sparse Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Citizen Science Tool Based on an Energy Autonomous Embedded System with Environmental Sensors and Hyperspectral Imaging

Kouzinopoulos,

Pechlivani,

Giakoumoglou

et al. 2024

JLPEA

View full text Add to dashboard Cite

show abstract

“…The rich spectral information helps to accurately identify these observed targets, which is beneficial to fine classification, and the image information retains the spatial distribution of the scene, providing context support for the subsequent interpretation. Therefore, hyperspectral images are increasingly and successfully applied in the fields of agriculture [1][2][3][4], ecological science [5,6], military [7][8][9][10], and atmospheric detection [11][12][13]. However, constrained by the law of conservation of energy and imaging capability of the sensors, hyperspectral data have the problems of lower spatial resolution and smaller imaging widths, universally.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Hyperspectral Image Collaborative Mapping Model Based on Adaptive Learning for Fine Classification

Zhang,

Liu,

Zhang

et al. 2024

Remote Sensing

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Hyperspectral (HS) data, encompassing hundreds of spectral channels for the same area, offer a wealth of spectral information and are increasingly utilized across various fields. However, their limitations in spatial resolution and imaging width pose challenges for precise recognition and fine classification in large scenes. Conversely, multispectral (MS) data excel in providing spatial details for vast landscapes but lack spectral precision. In this article, we proposed an adaptive learning-based mapping model, including an image fusion module, spectral super-resolution network, and adaptive learning network. Spectral super-resolution networks learn the mapping between multispectral and hyperspectral images based on the attention mechanism. The image fusion module leverages spatial and spectral consistency in training data, providing pseudo labels for spectral super-resolution training. And the adaptive learning network incorporates spectral response priors via unsupervised learning, adjusting the output of the super-resolution network to preserve spectral information in reconstructed data. Through the experiment, the model eliminates the need for the manual setting of image prior information and complex parameter selection, and can adjust the network structure and parameters dynamically, eventually enhancing the reconstructed image quality, and enabling the fine classification of large-scale scenes with high spatial resolution. Compared with the recent dictionary learning and deep learning spectral super-resolution methods, our approach exhibits superior performance in terms of both image similarity and classification accuracy.

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Detection of Verticillium infection in cotton leaves using ATR-FTIR spectroscopy coupled with machine learning algorithms

Li,

Zhang,

Zhang

et al. 2025

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

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A Review on Plant Disease Detection Using Hyperspectral Imaging

Cited by 3 publications

References 298 publications

A Citizen Science Tool Based on an Energy Autonomous Embedded System with Environmental Sensors and Hyperspectral Imaging

A Citizen Science Tool Based on an Energy Autonomous Embedded System with Environmental Sensors and Hyperspectral Imaging

A Multi-Hyperspectral Image Collaborative Mapping Model Based on Adaptive Learning for Fine Classification

Detection of Verticillium infection in cotton leaves using ATR-FTIR spectroscopy coupled with machine learning algorithms

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