A review on the combination of deep learning techniques with proximal hyperspectral images in agriculture

Barbedo, Jayme Garcia Arnal

doi:10.1016/j.compag.2023.107920

Cited by 37 publications

(9 citation statements)

References 147 publications

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“…However, the challenge resides in adeptly managing extensive datasets, extracting meaningful features, and establishing effective models. Hence, researchers have developed various data preprocessing methods, such as principal component analysis (PCA), successive projections algorithm (SPA), competitive adaptive reweighting sampling (CARS), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN) etc [5] .…”

Section: Introductionmentioning

confidence: 99%

Study of progress on application of hyperspectral imaging combined with deep learning approaches in detecting foods content

Huang,

Huang

2024

Advanced Fiber Laser Conference (AFL2023)

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Predicting food component content is one of the important research directions in the field of food science and engineering, and it is also an important indicator for evaluating food quality. With the increasing attention to food quality and safety issues, the prediction of food component content is of great significance for quality control in the food industry and food safety monitoring. Traditional prediction of food component content is usually carried out through chemical analysis and physical measurement techniques, which have disadvantages such as long detection times and high costs, and may be influenced by environmental factors and sample variability. They cannot meet the requirements for non-destructive, rapid prediction of multidimensional food component content. Therefore, the development of non-destructive, rapid, and multidimensional detection methods is crucial. Hyperspectral imaging technology is a non-destructive, rapid, and highprecision technology that can obtain a large amount of spectral information from food. Deep learning, as a powerful machine learning method, has the ability to handle large-scale data and extract complex features. With the development of hyperspectral imaging technology and deep learning, an increasing number of studies are combining the two for application in non-destructive food detection. This paper reviews the application of hyperspectral imaging technology combined with deep learning in food quality analysis, including nutrient analysis, traceability identification and maturity assessment.Secondly, the research progress of hyperspectral imaging technology combined with deep learning algorithms in data preprocessing, model building and evaluation is summarised to provide reference for improving the accuracy and efficiency of food analysis.

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Section: Introductionmentioning

confidence: 99%

Study of progress on application of hyperspectral imaging combined with deep learning approaches in detecting foods content

Huang,

Huang

2024

Advanced Fiber Laser Conference (AFL2023)

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“…In the last few years, Deep Learning (DL) methods have been applied to identify, classify, and quantify the diseases, pests, and stress on different crops [8]. 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].…”

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

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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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“…These subtle changes affect the optical properties of the seeds. Hyperspectral imaging technology is used to detect imperceptible internal variations that are not visible to the naked eyes by capturing detailed spectral and spatial information in the visible and near-infrared spectra regions ( Yu et al., 2018 ; Barbedo, 2023 ). Hyperspectral imaging is a promising technique for rapidly and non-destructive assessment seed vigor.…”

Section: Introductionmentioning

confidence: 99%

An end-to-end seed vigor prediction model for imbalanced samples using hyperspectral image

Pang,

Chen,

et al. 2023

Front. Plant Sci.

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Hyperspectral imaging is a key technology for non-destructive detection of seed vigor presently due to its capability to capture variations of optical properties in seeds. As the seed vigor data depends on the actual germination rate, it inevitably results in an imbalance between positive and negative samples. Additionally, hyperspectral image (HSI) suffers from feature redundancy and collinearity due to its inclusion of hundreds of wavelengths. It also creates a challenge to extract effective wavelength information in feature selection, however, which limits the ability of deep learning to extract features from HSI and accurately predict seed vigor. Accordingly, in this paper, we proposed a Focal-WAResNet network to predict seed vigor end-to-end, which improves the network performance and feature representation capability, and improves the accuracy of seed vigor prediction. Firstly, the focal loss function is utilized to adjust the loss weights of different sample categories to solve the problem of sample imbalance. Secondly, a WAResNet network is proposed to select characteristic wavelengths and predict seed vigor end-to-end, focusing on wavelengths with higher network weights, which enhance the ability of seed vigor prediction. To validate the effectiveness of this method, this study collected HSI of maize seeds for experimental verification, providing a reference for plant breeding. The experimental results demonstrate a significant improvement in classification performance compared to other state-of-the-art methods, with an accuracy up to 98.48% and an F1 score of 95.9%.

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A review on the combination of deep learning techniques with proximal hyperspectral images in agriculture

Cited by 37 publications

References 147 publications

Study of progress on application of hyperspectral imaging combined with deep learning approaches in detecting foods content

Study of progress on application of hyperspectral imaging combined with deep learning approaches in detecting foods content

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

An end-to-end seed vigor prediction model for imbalanced samples using hyperspectral image

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