Estimation of Leaf Area Index in vineyards by analysing projected shadows using UAV imagery

A few decades ago, farmers could precisely monitor their croplands just by walking over the fields, but this task becomes more difficult as farm size increases. Precision viticulture can help better understand the vineyard and measure some key structural parameters, such as the Leaf Area Index (LAI). Remote Sensing is a typical approach to monitoring vegetation which measures the spectral information directly emitted and reflected from vegetation. This study explores a new method for estimating LAI which measures the projected shadows of plants using UAV (unmanned aerial vehicle) imagery. A flight mission over a vineyard was scheduled in the afternoon (15:30 to 16:00 solar time), which is the optimal time for the projection of vine shadows on the ground. Real LAI was measured destructively by removing all the vegetation from the area. Then, the projected shadows in the image were detected using machine learning methods (k-means and random forest) and analysed at pixel level using a customised R code. A strong linear relationship (R² = 0.76, RMSE = 0.160 m² m-2 and MAE = 0.139 m² m-2) was found between the shaded area and the LAI per vine. This is a quick and simple method, which is non-destructive and gives accurate results; moreover, flights can be scheduled during other periods of the day than solar noon, such as in the morning or afternoon, thus enabling pilots to extend their working day. Therefore, it may be a viable option for determining LAI in vineyards trained on Vertical Shoot Positioned (VSP) systems.

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Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season

Plant Methods ◽

10.1186/s13007-021-00789-4 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Yan Gong ◽

Kaili Yang ◽

Zhiheng Lin ◽

Shenghui Fang ◽

Xianting Wu ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Unmanned Aerial Vehicle ◽

Growing Season ◽

Canopy Height ◽

Dynamic Monitoring ◽

Rice Cultivars ◽

Simple Method ◽

Area Index ◽

Aerial Vehicle

Abstract Background Rice is one of the most important grain crops worldwide. The accurate and dynamic monitoring of Leaf Area Index (LAI) provides important information to evaluate rice growth and production. Methods This study explores a simple method to remotely estimate LAI with Unmanned Aerial Vehicle (UAV) imaging for a variety of rice cultivars throughout the entire growing season. Forty eight different rice cultivars were planted in the study site and field campaigns were conducted once a week. For each campaign, several widely used vegetation indices (VI) were calculated from canopy reflectance obtained by 12-band UAV images, canopy height was derived from UAV RGB images and LAI was destructively measured by plant sampling. Results The results showed the correlation of VI and LAI in rice throughout the entire growing season was weak, and for all tested indices there existed significant hysteresis of VI vs. LAI relationship between rice pre-heading and post-heading stages. The model based on the product of VI and canopy height could reduce such hysteresis and estimate rice LAI of the whole season with estimation errors under 24%, not requiring algorithm re-parameterization for different phenology stages. Conclusions The progressing phenology can affect VI vs. LAI relationship in crops, especially for rice having quite different canopy spectra and structure after its panicle exsertion. Thus the models solely using VI to estimate rice LAI are phenology-specific and have high uncertainties for post-heading stages. The model developed in this study combines both remotely sensed canopy height and VI information, considerably improving rice LAI estimation at both pre- and post-heading stages. This method can be easily and efficiently implemented in UAV platforms for various rice cultivars during the entire growing season with no rice phenology and cultivar pre-knowledge, which has great potential for assisting rice breeding and field management studies at a large scale.

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Application of an Improved Method in Retrieving Leaf Area Index Combined Spectral Index with PLSR in Hyperspectral Data Generated by Unmanned Aerial Vehicle Snapshot Camera

ACTA AGRONOMICA SINICA ◽

10.3724/sp.j.1006.2017.00549 ◽

2017 ◽

Vol 43 (4) ◽

pp. 549

Author(s):

Lin GAO ◽

Gui-Jun YANG ◽

Chang-Chun LI ◽

Hai-Kuan FENG ◽

Bo XU ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Unmanned Aerial Vehicle ◽

Spectral Index ◽

Hyperspectral Data ◽

Improved Method ◽

Area Index ◽

Aerial Vehicle

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Estimation of Leaf Area Index in Chestnut Trees using Multispectral Data from an Unmanned Aerial Vehicle

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9324614 ◽

2020 ◽

Author(s):

Luis Padua ◽

Pedro Marques ◽

Luis Martins ◽

Antonio Sousa ◽

Emanuel Peres ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Unmanned Aerial Vehicle ◽

Area Index ◽

Multispectral Data ◽

Aerial Vehicle

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Wheat Leaf Area Index Prediction Using Data Fusion Based on High-Resolution Unmanned Aerial Vehicle Imagery

10.21203/rs.3.rs-1169494/v1 ◽

2021 ◽

Author(s):

Shuang Wu ◽

Lei Deng ◽

Lijie Guo ◽

Yanjie Wu

Keyword(s):

High Resolution ◽

Data Fusion ◽

Leaf Area Index ◽

Leaf Area ◽

Unmanned Aerial Vehicle ◽

Prediction Accuracy ◽

Sensor Data ◽

Support Vector ◽

Area Index ◽

Aerial Vehicle

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

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Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle

Biosystems Engineering ◽

10.1016/j.biosystemseng.2013.02.002 ◽

2013 ◽

Vol 115 (1) ◽

pp. 31-42 ◽

Cited By ~ 56

Author(s):

Juan I. Córcoles ◽

Jose F. Ortega ◽

David Hernández ◽

Miguel A. Moreno

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Unmanned Aerial Vehicle ◽

Allium Cepa ◽

Area Index ◽

Allium Cepa L ◽

Aerial Vehicle

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A High-Throughput Model-Assisted Method for Phenotyping Maize Green Leaf Area Index Dynamics Using Unmanned Aerial Vehicle Imagery

Frontiers in Plant Science ◽

10.3389/fpls.2019.00685 ◽

2019 ◽

Vol 10 ◽

Cited By ~ 7

Author(s):

Justin Blancon ◽

Dan Dutartre ◽

Marie-Hélène Tixier ◽

Marie Weiss ◽

Alexis Comar ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Unmanned Aerial Vehicle ◽

High Throughput ◽

Green Leaf ◽

Area Index ◽

Throughput Model ◽

Aerial Vehicle ◽

Green Leaf Area

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Adjusting Ceptometer Data to Improve Leaf Area Index Measurements

Agronomy ◽

10.3390/agronomy9120866 ◽

2019 ◽

Vol 9 (12) ◽

pp. 866 ◽

Cited By ~ 4

Author(s):

Klára Pokovai ◽

Nándor Fodor

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Measurement Techniques ◽

Well Being ◽

Average Error ◽

Light Conditions ◽

Simple Method ◽

Area Index ◽

Expert Opinions ◽

Optimal Illumination

Leaf Area Index (LAI) is an important plant parameter for both farmers and plant scientists to monitor and/or model the growth and the well-being of plants. Since direct LAI measurement techniques are relatively laborious and time-consuming, various indirect methods have been developed and widely used since the early 1990s. The LP-80 ceptometer uses a linear array of PAR (photosynthetically active radiation) sensors for non-destructive LAI measurements that is backed by 15 years of research. Despite this, considerable discrepancy can be found between the expert opinions regarding the optimal illumination conditions recommended for the measurement. The sensitivity of ceptometer-based LAI values to PAR was investigated, and a simple method was devised to correct raw ceptometer data collected under non-ideal light conditions. Inadequate light conditions (PAR < 1700 µmol m−2 s−1) could cause an underestimation of LAI. Using the corrected LAI values, the ceptometer data showed a significantly better fit (higher R2, smaller mean average error and closer to zero mean signed error values) to the destructive LAI data for both wheat and maize. With the help of the correction equations, the use of the LP-80 ceptometer could be extended to days when light conditions are not ideal.

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Development and Evaluation of a Leaf Disease Damage Extension in Cropsim-CERES Wheat

Agronomy ◽

10.3390/agronomy9030120 ◽

2019 ◽

Vol 9 (3) ◽

pp. 120 ◽

Cited By ~ 3

Author(s):

Georg Röll ◽

William Batchelor ◽

Ana Castro ◽

María Simón ◽

Simone Graeff-Hönninger

Keyword(s):

Sensitivity Analysis ◽

Leaf Area Index ◽

Leaf Area ◽

Mean Squared Error ◽

Septoria Tritici Blotch ◽

Septoria Tritici ◽

Extended Model ◽

Simple Method ◽

Area Index ◽

The Impact

Developing disease models to simulate and analyse yield losses for various pathogens is a challenge for the crop modelling community. In this study, we developed and tested a simple method to simulate septoria tritici blotch (STB) in the Cropsim-CERES Wheat model studying the impacts of damage on wheat (Triticum aestivum L.) yield. A model extension was developed by adding a pest damage module to the existing wheat model. The module simulates the impact of daily damage on photosynthesis and leaf area index. The approach was tested on a two-year dataset from Argentina with different wheat cultivars. The accuracy of the simulated yield and leaf area index (LAI) was improved to a great extent. The Root mean squared error (RMSE) values for yield (1144 kg ha−1) and LAI (1.19 m2 m−2) were reduced by half (499 kg ha−1) for yield and LAI (0.69 m2 m−2). In addition, a sensitivity analysis of different disease progress curves on leaf area index and yield was performed using a dataset from Germany. The sensitivity analysis demonstrated the ability of the model to reduce yield accurately in an exponential relationship with increasing infection levels (0–70%). The extended model is suitable for site specific simulations, coupled with for example, available remote sensing data on STB infection.

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