Different Remote Sensing Data in Relative Biomass Determination and in Precision Fertilization Task Generation for Cereal Crops

Kaivosoja, Jere; Näsi, Roope; Hakala, Teemu; Viljanen, Niko; Honkavaara, Eija

doi:10.1007/978-3-030-12998-9_12

Cited by 4 publications

(5 citation statements)

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“…The results showed that visible atmospherically resistant index (VARI) had the best performance compared to other CIs in both training (R 2 = 0.74) and test datasets (RMSE = 1.14, MAE = 0.94). Calculated from DN values of all three color channels (R, G, and B), the VARI has demonstrated excellent feasibility for crop biomass estimation [47] and yield prediction [48] in previous studies. VARI-based LR model has a simple and explicable structure, which is convenient for technical implementation and practical application.…”

Section: Combination Of Color and Texture Information For Crop Lai Esmentioning

confidence: 88%

Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation

et al. 2019

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Leaf area index (LAI) is a fundamental indicator of plant growth status in agronomic and environmental studies. Due to rapid advances in unmanned aerial vehicle (UAV) and sensor technologies, UAV-based remote sensing is emerging as a promising solution for monitoring crop LAI with great flexibility and applicability. This study aimed to determine the feasibility of combining color and texture information derived from UAV-based digital images for estimating LAI of rice (Oryza sativa L.). Rice field trials were conducted at two sites using different nitrogen application rates, varieties, and transplanting methods during 2016 to 2017. Digital images were collected using a consumer-grade UAV after sampling at key growth stages of tillering, stem elongation, panicle initiation and booting. Vegetation color indices (CIs) and grey level co-occurrence matrix-based textures were extracted from mosaicked UAV ortho-images for each plot. As a solution of using indices composed by two different textures, normalized difference texture indices (NDTIs) were calculated by two randomly selected textures. The relationships between rice LAIs and each calculated index were then compared using simple linear regression. Multivariate regression models with different input sets were further used to test the potential of combining CIs with various textures for rice LAI estimation. The results revealed that the visible atmospherically resistant index (VARI) based on three visible bands and the NDTI based on the mean textures derived from the red and green bands were the best for LAI retrieval in the CI and NDTI groups, respectively. Independent accuracy assessment showed that random forest (RF) exhibited the best predictive performance when combining CI and texture inputs (R2 = 0.84, RMSE = 0.87, MAE = 0.69). This study introduces a promising solution of combining color indices and textures from UAV-based digital imagery for rice LAI estimation. Future studies are needed on finding the best operation mode, suitable ground resolution, and optimal predictive methods for practical applications.

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Section: Combination Of Color and Texture Information For Crop Lai Esmentioning

confidence: 88%

Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation

et al. 2019

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“…Successfully translating this assumption from the plot scale to the field scale can be difficult due to increases in spatial variability and practical difficulties of implementation (Colaço & Bramley, 2018). Also, it should be noted that, within this experiment, the highest pre-plant N rate at the Davis 2016-2017and Rio Vista 2016-2017 sites was approximately 60 kg N ha −1 , while other site-years had higher pre-plant rates within their treatment structures (Supplemental Table S1). Therefore, it is possible that the "sufficiency" reference for SI at these two sites may not have been at a theoretical maximum.…”

Section: Discussionmentioning

confidence: 96%

“…This may have contributed the higher yields and lower grain protein measured for UC Tahoe. Because of these dynamics, effectively interpreting and implementing reflectance-based classifications across heterogeneous agroecosystems requires some broader understanding of a site's yield potential, varietal growth patterns, and seasonal N budget, as has been argued previously (Colaço & Bramley, 2018;Franzen et al, 2016;Kaivosoja et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…The Zamora 2015-2016 site had 4.5 m by 4.5 m plots with harvested areas of 1.5 m by 4 m (n = 12). The Davis 2016-2017(n = 32), Rio Vista 2016-2017, and Esparto 2017-2018 (n = 96) plots were 3 m by 6 m planted in two sections with a 1.5 m planter. The Rio Vista site had 5 plots that were accidentally harvested by the grower cooperator during the harvest of the surrounding field and prior to the harvest of the experimental plots.…”

Section: Field Experimentsmentioning

confidence: 99%

“…All fertilizer applied pre-plant or at tillering was applied within 2 days prior to a 1.5 cm or greater rainfall or irrigation event to ensure incorporation into the root zone. Ammonium sulfate (0-0-21-24S) was used at the Davis 2016-2017and Rio Vista 2016-2017 sites while urea (46-0-0) was used at all other sites. The proportion of the total fertilizer N applied pre-plant and at tillering varied between site years (Supplemental Table S1).…”

Section: Field Experimentsmentioning

confidence: 99%

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Canopy reflectance informs in‐season malting barley nitrogen management: An ex‐ante classification approach

Nelsen¹,

Lundy

2020

Agronomy Journal

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Malting barley (Hordeum vulgare) requires precise nitrogen (N) fertilizer management to achieve a narrow range of grain protein content (∼9-10.5%) while maintaining yields, but practical tools to accomplish this are lacking. This study hypothesized that canopy reflectance (Normalized Difference Vegetation Index (NDVI)) measured at tillering (Feekes 2-3) and expressed as a sufficiency index (SI), can estimate the likelihood of a site-specific response to in-season N fertilizer in malting barley. Canopy reflectance was measured from plots at tillering with a GreenSeeker and unmanned aerial vehicle (UAV) borne multispectral cameras in trials across heterogeneous California agroecosystems. Field experiments included a range of N fertilizer application rates (0-168 kg N ha −1) and timings (pre-plant, tillering, or evenly split), and resulted in a range of crop N sufficiency/deficiency. NDVI-based SI measurements were categorized into one of three quantitative categories (low, medium, and high) without additional experimental context using Gaussian mixture modeling. Despite that 85% of variation in protein yield was due to site-year, the reflectance-based categories indicated whether N fertilizer applied in-season would increase protein yield (p < .01). Nitrogen application at tillering increased yield and protein for plots in the "low" and "medium" SI categories (45 and 4% for yield and 16 and 12% for protein, respectively) (p < .05), while "high" SI plots had neither yield (p = .23) nor protein (p = .26) increases. Importantly, the broader agronomic conditions of a site primarily determined whether response to in-season N manifested as increased yield or protein.

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The challenge of reproducing remote sensing data from satellites and unmanned aerial vehicles (UAVs) in the context of management zones and precision agriculture

et al. 2020

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Different Remote Sensing Data in Relative Biomass Determination and in Precision Fertilization Task Generation for Cereal Crops

Cited by 4 publications

References 94 publications

Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation

Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation

Canopy reflectance informs in‐season malting barley nitrogen management: An ex‐ante classification approach

The challenge of reproducing remote sensing data from satellites and unmanned aerial vehicles (UAVs) in the context of management zones and precision agriculture

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