Unmanned aerial vehicle-based assessment of rice leaf chlorophyll content dynamics across genotypes

Gu, Qing; Huang, Fudeng; Lou, Weidong; Zhu, Yihang; Hu, Hao; Zhao, Yiying; Zhou, Hongkui; Zhang, Xiaobin

doi:10.1016/j.compag.2024.108939

Cited by 2 publications

(2 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…gronomy 2024, 14, x FOR PEER REVIEW 13 of 21 according to visual assessment by the assessors, Nred in the visible range is shown to be an effective index for the estimation formula of the BLB damage assessment. Nred is the absorption band of chlorophyll; the higher the damage, the lower the chlorophyll content and the higher the reflectance, which may confirm a positive correlation [45][46][47]. In the case of BLB disease, the leaves fade, with yellowing at the lea margins and a grayish white color in the case of advanced infection.…”

Section: Relationship Between Normalized Reflectance For Each Band An...mentioning

confidence: 87%

“…Research using hyperspectral data has reported that BLB-infected rice plants show peculiar changes between 757 nm and 1039 nm, corresponding to the near-infrared region, and that reflectance decreases with increasing severity [49]; the same trend was confirmed in this study. The NDVI is the Nred is the absorption band of chlorophyll; the higher the damage, the lower the chlorophyll content and the higher the reflectance, which may confirm a positive correlation [45][46][47]. In the case of BLB disease, the leaves fade, with yellowing at the leaf margins and a grayish white color in the case of advanced infection.…”

Section: Relationship Between Normalized Reflectance For Each Band An...mentioning

confidence: 89%

See 1 more Smart Citation

Efficient Damage Assessment of Rice Bacterial Leaf Blight Disease in Agricultural Insurance Using UAV Data

Hongo,

Isono,

Sigit

et al. 2024

Agronomy

View full text Add to dashboard Cite

In Indonesia, where the agricultural insurance system has been in full operation since 2016, a new damage assessment estimation formula for rice diseases was created through integrating the current damage assessment method and unmanned aerial vehicle (UAV) multispectral remote sensing data to improve the efficiency and precision of damage assessment work performed for the payments of insurance claims. The new method can quickly and efficiently output objective assessment results. In this study, UAV images and bacterial leaf blight (BLB) rice damage assessment data were acquired during the rainy and dry seasons of 2021 and 2022 in West Java, Indonesia, where serious BLB damage occurs every year. The six-level BLB score (0, 1, 3, 5, 7, and 9) and damage intensity calculated from the score were used as the BLB damage assessment data. The relationship between normalized UAV data, normalized difference vegetation index (NDVI), and BLB score showed significant correlations at the 1% level. The analysis of damage intensities and UAV data for paddy plots in all cropping seasons showed high correlation coefficients with the normalized red band, normalized near-infrared band, and NDVI, similar to the results of the BLB score analysis. However, for paddy plots with damage intensities of 70% or higher, the biased numbering of the BLB score data may have affected the evaluation results. Therefore, we conducted an analysis using an average of 1090 survey points for each BLB score and confirmed a strong relationship, with correlation coefficients exceeding 0.9 for the normalized red band, normalized near-infrared band, and NDVI. Through comparing the time required by the current assessment method with that required by the assessment method integrating UAV data, it was demonstrated that the evaluation time was reduced by more than 60% on average. We are able to propose a new assessment method for the Indonesian government to achieve complete objective enumeration.

show abstract

Section: Relationship Between Normalized Reflectance For Each Band An...mentioning

confidence: 87%

Section: Relationship Between Normalized Reflectance For Each Band An...mentioning

confidence: 89%

Efficient Damage Assessment of Rice Bacterial Leaf Blight Disease in Agricultural Insurance Using UAV Data

Hongo,

Isono,

Sigit

et al. 2024

Agronomy

View full text Add to dashboard Cite

show abstract

Spectral-Frequency Conversion Derived from Hyperspectral Data Combined with Deep Learning for Estimating Chlorophyll Content in Rice

Du,

Luo

2024

Agriculture

View full text Add to dashboard Cite

As a vital pigment for photosynthesis in rice, chlorophyll content is closely correlated with growth status and photosynthetic capacity. The estimation of chlorophyll content allows for the monitoring of rice growth and facilitates precise management in the field, such as the application of fertilizers and irrigation. The advancement of hyperspectral remote sensing technology has made it possible to estimate chlorophyll content non-destructively, quickly, and effectively, offering technical support for managing and monitoring rice growth across wide areas. Although hyperspectral data have a fine spectral resolution, they also cause a large amount of information redundancy and noise. This study focuses on the issues of unstable input variables and the estimation model’s poor applicability to various periods when predicting rice chlorophyll content. By introducing the theory of harmonic analysis and the time-frequency conversion method, a deep neural network (DNN) model framework based on wavelet packet transform-first order differential-harmonic analysis (WPT-FD-HA) was proposed, which avoids the uncertainty in the calculation of spectral parameters. The accuracy of estimating rice chlorophyll content based on WPT-FD and WPT-FD-HA variables was compared at seedling, tillering, jointing, heading, grain filling, milk, and complete periods to evaluate the validity and generalizability of the suggested framework. The results demonstrated that all of the WPT-FD-HA models’ single-period validation accuracy had coefficients of determination (R2) values greater than 0.9 and RMSE values less than 1. The multi-period validation model had a root mean square error (RMSE) of 1.664 and an R2 of 0.971. Even with independent data splitting validation, the multi-period model accuracy can still achieve R2 = 0.95 and RMSE = 1.4. The WPT-FD-HA-based deep learning framework exhibited strong stability. The outcome of this study deserves to be used to monitor rice growth on a broad scale using hyperspectral data.

show abstract

Unmanned aerial vehicle-based assessment of rice leaf chlorophyll content dynamics across genotypes

Cited by 2 publications

References 46 publications

Efficient Damage Assessment of Rice Bacterial Leaf Blight Disease in Agricultural Insurance Using UAV Data

Efficient Damage Assessment of Rice Bacterial Leaf Blight Disease in Agricultural Insurance Using UAV Data

Spectral-Frequency Conversion Derived from Hyperspectral Data Combined with Deep Learning for Estimating Chlorophyll Content in Rice

Contact Info

Product

Resources

About