A Methodology for Selection of Optimal Viewing Angles for an Accurate Estimation of Leaf Area Index based on Information Theory

Multiple factors influence rice yield. Developing management practices that increase crop yield and an efficient use of resources are challenging to modern agriculture. Consequently, the aim of this study was to evaluate biological nitrogen fixation and bacterial phosphorous solubilization (biofertilization) practices with the selection of the sowing date. Three sowing dates (May, July and August) were evaluated when interacting with two mineral nutrition treatments using a randomized complete block design in a split-plot arrangement. Leaf carbon balance, leaf area index, interception and radiation use efficiency, harvest index, dry matter accumulation, nutritional status, and yield were quantified. Results showed that the maximum yield was obtained in the sowing date of August. Additionally, yield increased by 18.92% with the biofertilization treatment, reaching 35.18% of profitability compared to the local production practice. High yields were related to a higher carbon balance during flowering, which was 11.56% and 54.04% higher in August than in July and May, respectively, due to a lower night temperature. In addition, a high efficient use of radiation, which in August was 17.56% and 41.23% higher than in July and May, respectively, contributed to obtain higher yields and this behavior is related to the selection of the sowing date. Likewise, a rapid development of the leaf area index and an optimum foliar nitrogen concentration (>3%) were observed. This allowed for greater efficient use of radiation and is attributed to the activity of nitrogen-fixing and phosphate solubilizing bacteria that also act as plant growth promoters.

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An automated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application to an examination of the relationship between remotely sensed and field measurements of LAI

Functional Plant Biology ◽

10.1071/fp08045 ◽

2008 ◽

Vol 35 (10) ◽

pp. 1070 ◽

Cited By ~ 46

Author(s):

Sigfredo Fuentes ◽

Anthony R. Palmer ◽

Daniel Taylor ◽

Melanie Zeppel ◽

Rhys Whitley ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Gap Analysis ◽

Digital Camera ◽

Field Measurements ◽

Accurate Estimation ◽

Image Capture ◽

Area Index ◽

Modis Lai ◽

Matlab Programming

Leaf area index (LAI) is one of the most important variables required for modelling growth and water use of forests. Functional–structural plant models use these models to represent physiological processes in 3-D tree representations. Accuracy of these models depends on accurate estimation of LAI at tree and stand scales for validation purposes. A recent method to estimate LAI from digital images (LAID) uses digital image capture and gap fraction analysis (Macfarlane et al. 2007b) of upward-looking digital photographs to capture canopy LAID (cover photography). After implementing this technique in Australian evergreen Eucalyptus woodland, we have improved the method of image analysis and replaced the time consuming manual technique with an automated procedure using a script written in MATLAB 7.4 (LAIM). Furthermore, we used this method to compare MODIS LAI values with LAID values for a range of woodlands in Australia to obtain LAI at the forest scale. Results showed that the MATLAB script developed was able to successfully automate gap analysis to obtain LAIM. Good relationships were achieved when comparing averaged LAID and LAIM (LAIM = 1.009 – 0.0066 LAID; R2 = 0.90) and at the forest scale, MODIS LAI compared well with LAID (MODIS LAI = 0.9591 LAID – 0.2371; R2 = 0.89). This comparison improved when correcting LAID with the clumping index to obtain effective LAI (MODIS LAI = 1.0296 LAIe + 0.3468; R2 = 0.91). Furthermore, the script developed incorporates a function to connect directly a digital camera, or high resolution webcam, from a laptop to obtain cover photographs and LAI analysis in real time. The later is a novel feature which is not available on commercial LAI analysis softwares for cover photography. This script is available for interested researchers.

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Examination of the extinction coefficient in the Beer–Lambert law for an accurate estimation of the forest canopy leaf area index

Forest Science and Technology ◽

10.1080/21580103.2012.673744 ◽

2012 ◽

Vol 8 (2) ◽

pp. 67-76 ◽

Cited By ~ 12

Author(s):

Taku M. Saitoh ◽

Shin Nagai ◽

Hibiki M. Noda ◽

Hiroyuki Muraoka ◽

Kenlo Nishida Nasahara

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Extinction Coefficient ◽

Forest Canopy ◽

Accurate Estimation ◽

Area Index

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Simulating the Leaf Area Index of Rice from Multispectral Images

Remote Sensing ◽

10.3390/rs13183663 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3663

Author(s):

Shenzhou Liu ◽

Wenzhi Zeng ◽

Lifeng Wu ◽

Guoqing Lei ◽

Haorui Chen ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Estimation Accuracy ◽

Gradient Boosting ◽

Accurate Estimation ◽

Area Index ◽

Red Edge ◽

Extreme Gradient Boosting

Accurate estimation of the leaf area index (LAI) is essential for crop growth simulations and agricultural management. This study conducted a field experiment with rice and measured the LAI in different rice growth periods. The multispectral bands (B) including red edge (RE, 730 nm ± 16 nm), near-infrared (NIR, 840 nm ± 26 nm), green (560 nm ± 16 nm), red (650 nm ± 16 nm), blue (450 nm ± 16 nm), and visible light (RGB) were also obtained by an unmanned aerial vehicle (UAV) with multispectral sensors (DJI-P4M, SZ DJI Technology Co., Ltd.). Based on the bands, five vegetation indexes (VI) including Green Normalized Difference Vegetation Index (GNDVI), Leaf Chlorophyll Index (LCI), Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Optimization Soil-Adjusted Vegetation Index (OSAVI) were calculated. The semi-empirical model (SEM), the random forest model (RF), and the Extreme Gradient Boosting model (XGBoost) were used to estimate rice LAI based on multispectral bands, VIs, and their combinations, respectively. The results indicated that the GNDVI had the highest accuracy in the SEM (R2 = 0.78, RMSE = 0.77). For the single band, NIR had the highest accuracy in both RF (R2 = 0.73, RMSE = 0.98) and XGBoost (R2 = 0.77, RMSE = 0.88). Band combination of NIR + red improved the estimation accuracy in both RF (R2 = 0.87, RMSE = 0.65) and XGBoost (R2 = 0.88, RMSE = 0.63). NDRE and LCI were the first two single VIs for LAI estimation using both RF and XGBoost. However, putting more than one VI together could only increase the LAI estimation accuracy slightly. Meanwhile, the bands + VIs combinations could improve the accuracy in both RF and XGBoost. Our study recommended estimating rice LAI by a combination of red + NIR + OSAVI + NDVI + GNDVI + LCI + NDRE (2B + 5V) with XGBoost to obtain high accuracy and overcome the potential over-fitting issue (R2 = 0.91, RMSE = 0.54).

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Genetic parameters and line selection of Cucurbita pepo based on selection indices

Bioscience Journal ◽

10.14393/bj-v36n0a2020-53607 ◽

2020 ◽

Vol 36 ◽

Author(s):

Igor Forigo Beloti ◽

Gabriel Mascarenhas Maciel ◽

Fernando Cezar Juliatti ◽

Rafael Resende Finzi ◽

Daniel Bonifácio Oliveira Cardoso

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Genetic Parameters ◽

Leaf Temperature ◽

Area Index ◽

Balanced Distribution ◽

Multiple Characteristics ◽

Simultaneous Selection ◽

Selection Indexes ◽

Selection Of

In the improvement of pumpkins, the selection based on one or a few characters of interest tends to be less efficient, leading to a superior product only compared to the few characters selected. To maximize the simultaneous selection of multiple characteristics of interest, selection indexes are used to obtain a numerical value resulting from the combination of the characters on which the simultaneous selection is to be practiced. The objective of this study was to determine genetic parameters and the most appropriate selection indexes in strains of Summer squash (C. pepo). Statistical analyzes were performed based on 65 genotypes belonging to the vegetable germplasm bank of the Federal University of Uberlândia. The variables analyzed were: leaf area index, precocity, SPAD index, productivity. plant-1, number of fruits. Plant-1, leaf temperature, NDVI index and NDRE index. The indexes were used: Smith (1936) and Hazel (1943), the sum of “Ranks” by Mulamba and Mock (1978), and Willians (1962). The selection methodologies selected ten individuals (15% of the genotypes). The values found for h² (%) ranged from 36.92% (SPAD) to 59.65% (cycle). The values obtained in the CVg / CVe quotient were below 1, varying from 0.18 for leaf temperature to 0.70 for the cycle, with the other variables close to 0.5. The CVg genetic variation coefficient (%) was also low, ranging from 1.84% for leaf temperature to 30.94% for productivity. The greatest gains obtained with direct and indirect selection were for the characters productivity (35.92%), NDRE (33.04%), number of fruits (28.93%) and leaf area index (22.72%). The Mulamba and Mock (1978) index showed the highest total selection gain value, providing a balanced distribution of selection gains, choosing the genotypes: 8, 31, 34, 38, 42, 64, 65, 66, 67 and 68.

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Optimal solar/viewing geometry for an accurate estimation of leaf area index and leaf angle distribution from bidirectional canopy reflectance data

International Journal of Remote Sensing ◽

10.1080/01431168508948297 ◽

1985 ◽

Vol 6 (9) ◽

pp. 1493-1520 ◽

Cited By ~ 24

Author(s):

NARENDRA S. GOEL ◽

RICHARD L. THOMPSON

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Accurate Estimation ◽

Leaf Angle ◽

Canopy Reflectance ◽

Angle Distribution ◽

Area Index ◽

Leaf Angle Distribution ◽

Reflectance Data

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Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery

Remote Sensing ◽

10.3390/rs13061046 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1046

Author(s):

Gregoriy Kaplan ◽

Lior Fine ◽

Victor Lukyanov ◽

V. S. Manivasagam ◽

Nitzan Malachy ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Water Consumption ◽

Optical Sensors ◽

Crop Coefficient ◽

Accurate Estimation ◽

Area Index ◽

Crop Height ◽

Processing Tomato ◽

Sentinel 2

Crop monitoring throughout the growing season is key for optimized agricultural production. Satellite remote sensing is a useful tool for estimating crop variables, yet continuous high spatial resolution earth observations are often interrupted by clouds. This paper demonstrates overcoming this limitation by combining observations from two public-domain spaceborne optical sensors. Ground measurements were conducted in the Hula Valley, Israel, over four growing seasons to monitor the development of processing tomato. These measurements included continuous water consumption measurements using an eddy-covariance tower from which the crop coefficient (Kc) was calculated and measurements of Leaf Area Index (LAI) and crop height. Satellite imagery acquired by Sentinel-2 and VENµS was used to derive vegetation indices and model Kc, LAI, and crop height. The conjoint use of Sentinel-2 and VENµS imagery facilitated accurate estimation of Kc (R2 = 0.82, RMSE = 0.09), LAI (R2 = 0.79, RMSE = 1.2), and crop height (R2 = 0.81, RMSE = 7 cm). Additionally, our empirical models for LAI estimation were found to perform better than the SNAP biophysical processor (R2 = 0.53, RMSE = 2.3). Accordingly, Sentinel-2 and VENµS imagery was demonstrated to be a viable tool for agricultural monitoring.

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