Recent trends and advances in hyperspectral imaging techniques to estimate solar induced fluorescence for plant phenotyping

Mangalraj, P.; Cho, Byoung‐Kwan

doi:10.1016/j.ecolind.2022.108721

Cited by 12 publications

(2 citation statements)

References 108 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The advantages and disadvantages of this remote sensing technique are discussed in reviews [ 89 , 90 ], while studies [ 91 , 92 ] show the possibility of its practical application. However, authors believe that current data to assess the potential of this technique for the early detection of plant diseases are insufficient.…”

Section: New Technical Methods In Plant Protectionmentioning

confidence: 99%

Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review

Terentev

Долженко

2023

Sensors

View full text Add to dashboard Cite

The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection.

show abstract

Section: New Technical Methods In Plant Protectionmentioning

confidence: 99%

Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review

Terentev

Долженко

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Remote sensing technology has always been an important approach for crop yield estimation [2]. Traditional remote sensing crop yield estimation research platforms can be mainly divided into two major parts: satellite/airborne remote sensing and tower/ground remote sensing, corresponding to macroscopic monitoring and local observation, respectively [3]. However, macroscopic monitoring presents several challenges, such as (i) low spatial resolution [4], with rice yield estimation results being easily disturbed by background factors such as bare soil, shadows, and other non-plant targets; (ii) the revisit cycle is relatively short [5] and cannot match the growth cycle of rice in terms of time and frequency, thus it cannot guarantee the timeliness of yield estimation; (iii) heavy reliance on costly data types, with few hyperspectral satellite data sources available for civilian use [6].…”

Section: Introductionmentioning

confidence: 99%

Application of UAV-Borne Visible-Infared Pushbroom Imaging Hyperspectral for Rice Yield Estimation Using Feature Selection Regression Methods

Shen,

Yan,

Yang

et al. 2024

Sustainability

View full text Add to dashboard Cite

Rice yield estimation is vital for enhancing food security, optimizing agricultural management, and promoting sustainable development. However, traditional satellite/aerial and ground-based/tower-based platforms face limitations in rice yield estimation, and few studies have explored the potential of UAV-borne hyperspectral remote sensing for this purpose. In this study, we employed a UAV-borne push-broom hyperspectral camera to acquire remote sensing data of rice fields during the filling stage, and the machine learning regression algorithms were applied to rice yield estimation. The research comprised three parts: hyperspectral data preprocessing, spectral feature extraction, and model construction. To begin, the preprocessing of hyperspectral data involved geometric distortion correction, relative radiometric calibration, and rice canopy mask construction. Challenges in geometric distortion correction were addressed by tracking linear features during flight and applying a single-line correction method. Additionally, the NIR reflectance threshold method was applied for rice canopy mask construction, which was subsequently utilized for average reflectance extraction. Then, spectral feature extraction was carried out to reduce multicollinearity in the hyperspectral data. Recursive feature elimination (RFE) was then employed to identify the optimal feature set for model performance. Finally, six machine learning regression models (SVR, RFR, AdaBoost, XGBoost, Ridge, and PLSR) were used for rice yield estimation, achieving significant results. PLSR showed the best R2 of 0.827 with selected features, while XGBoost had the best R2 of 0.827 with full features. In addition, the spatial distribution of absolute error in rice yield estimation was assessed. The results suggested that this UAV-borne imaging hyperspectral-based approach held great potential for crop yield estimation, not only for rice but also for other crops.

show abstract

Solar-induced chlorophyll fluorescence extraction based on heterogeneous light distribution for improving in-situ chlorophyll content estimation

Zhao,

Tang,

et al. 2023

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

Recent trends and advances in hyperspectral imaging techniques to estimate solar induced fluorescence for plant phenotyping

Cited by 12 publications

References 108 publications

Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review

Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review

Application of UAV-Borne Visible-Infared Pushbroom Imaging Hyperspectral for Rice Yield Estimation Using Feature Selection Regression Methods

Solar-induced chlorophyll fluorescence extraction based on heterogeneous light distribution for improving in-situ chlorophyll content estimation

Contact Info

Product

Resources

About