UAV-Based Vegetation Indices to Evaluate Coffee Crop Response after Transplanting Seedlings Grown in Different Containers

Barata, Rafael Alexandre Pena; Ferraz, Gabriel Araújo e Silva; Bento, Nicole Lopes; Santana, Lucas Santos; Marin, Diego Bedin; Mattos, Drucylla Guerra; Schwerz, Felipe; Rossi, Giuseppe; Conti, Leonardo; Bambi, Gianluca

doi:10.3390/agriculture14030356

Cited by 2 publications

(1 citation statement)

References 53 publications

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“…By equipping UAVs with hyperspectral cameras, more comprehensive multidimensional image data can be obtained, which enables quantitative inversion of crop phenotypic information such as plant quantity ( Font et al., 2014 ), plant height ( Fang et al., 2016 ), lodging rate ( Barata et al., 2024 ), leaf area index ( Liu et al., 2021 ), chlorophyll content ( Kanning et al., 2018 ), nitrogen element content ( Sun et al., 2022 ; Wang et al., 2022c ), pest and disease information ( Liu et al., 2020 ), and other physical and chemical parameters. Compared to RGB three-band image data, hyperspectral imagery provides higher inversion accuracy.…”

Section: Introductionmentioning

confidence: 99%

Prediction of wheat SPAD using integrated multispectral and support vector machines

Wang,

Sun,

Bai

et al. 2024

Front. Plant Sci.

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Rapidly obtaining the chlorophyll content of crop leaves is of great significance for timely diagnosis of crop health and effective field management. Multispectral imagery obtained from unmanned aerial vehicles (UAV) is being used to remotely sense the SPAD (Soil and Plant Analyzer Development) values of wheat crops. However, existing research has not yet fully considered the impact of different growth stages and crop populations on the accuracy of SPAD estimation. In this study, 300 materials from winter wheat natural populations in Xinjiang, collected between 2020 to 2022, were analyzed. UAV multispectral images were obtained in the experimental area, and vegetation indices were extracted to analyze the correlation between the selected vegetation indices and SPAD values. The input variables for the model were screened, and a support vector machine (SVM) model was constructed to estimate SPAD values during the heading, flowering, and filling stages under different water stresses. The aim was to provide a method for the rapid acquisition of winter wheat SPAD values. The results showed that the SPAD values under normal irrigation were higher than those under water restriction. Multiple vegetation indices were significantly correlated with SPAD values. In the prediction model construction of SPAD, the different models had high estimation accuracy under both normal irrigation and water limitation treatments, with correlation coefficients of predicted and measured values under normal irrigation in different environments the value of r from 0.59 to 0.81 and RMSE from 2.15 to 11.64, compared to RE from 0.10% to 1.00%; and under drought stress in different environments, correlation coefficients of predicted and measured values of r was 0.69–0.79, RMSE was 2.30–12.94, and RE was 0.10%–1.30%. This study demonstrated that the optimal combination of feature selection methods and machine learning algorithms can lead to a more accurate estimation of winter wheat SPAD values. In summary, the SVM model based on UAV multispectral images can rapidly and accurately estimate winter wheat SPAD value.

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Section: Introductionmentioning

confidence: 99%

Prediction of wheat SPAD using integrated multispectral and support vector machines

Wang,

Sun,

Bai

et al. 2024

Front. Plant Sci.

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Multispectral Images for Drought Stress Evaluation of Arabica Coffee Genotypes Under Different Irrigation Regimes

da Silva,

Ribeiro Junior,

Ramos

et al. 2024

Sensors

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The advancement of digital agriculture combined with computational tools and Unmanned Aerial Vehicles (UAVs) has opened the way to large-scale data collection for the calculation of vegetation indices (VIs). These vegetation indexes (VIs) are useful for agricultural monitoring, as they highlight the inherent characteristics of vegetation and optimize the spatial and temporal evaluation of different crops. The experiment tested three coffee genotypes (Catuaí 62, E237 and Iapar 59) under five water regimes: (1) FI 100 (year-round irrigation with 100% replacement of evapotranspiration loss), (2) FI 50 (year-round irrigation with 50% evapotranspiration replacement), (3) WD 100 (no irrigation from June to September (dry season) and, thereafter, 100% evapotranspiration replacement), (4) WD 50 (no irrigation from June to September (water stress) and, thereafter, 50% evapotranspiration replacement) and (5) rainfed (no irrigation during the year). The irrigated treatments were watered with irrigation and precipitation. Most indices were highest in response to full irrigation (FI 100). The values of the NDVI ranged from 0.87 to 0.58 and the SAVI from 0.65 to 0.38, and the values of these indices were lowest for genotype E237 in the rainfed areas. The indices NDVI, OSAVI, MCARI, NDRE and GDVI were positively correlated—very strongly with photosynthesis (A) and strongly with transpiration (E) of the coffee trees. On the other hand, temperature-based indices, such as canopy temperature and the TCARI index correlated negatively with A, E and stomatal conductance (gs). Under full irrigation, the tested genotypes did not differ between the years of evaluation. Overall, the index values of Iapar 59 exceeded those of the other genotypes. The use of VIs to evaluate coffee tree performance under different water managements proved efficient in discriminating the best genotypes and optimal water conditions for each genotype. Given the economic importance of coffee as a crop and its susceptibility to extreme events such as drought, this study provides insights that facilitate the optimization of productivity and resilience of plantations under variable climatic conditions.

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UAV-Based Vegetation Indices to Evaluate Coffee Crop Response after Transplanting Seedlings Grown in Different Containers

Cited by 2 publications

References 53 publications

Prediction of wheat SPAD using integrated multispectral and support vector machines

Prediction of wheat SPAD using integrated multispectral and support vector machines

Multispectral Images for Drought Stress Evaluation of Arabica Coffee Genotypes Under Different Irrigation Regimes

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