Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology

Zsebő, Sándor; Bede, László; Kukorelli, Gábor; Kulmány, István Mihály; Milics, Gábor; Stencinger, Dávid; Teschner, Gergely; Varga, Zoltán; Vona, Viktória; Kovács, Attila József

doi:10.3390/drones8030088

Cited by 5 publications

(2 citation statements)

References 71 publications

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“…The general average of the genotypes was found to be 0.64. These results are in concurrence with those ofLopes and Reynolds (2012) andZsebő et al (2024) who inferred that NDVI values remained effective in differentiating the wheat genotypes and could be preferred over destructive sampling techniques to sort out the most performing genotypes. Moreover,Swoish et al (2022),Guan et al (2019) andNaser et al (…”

supporting

confidence: 88%

“…However, these sampling techniques tend to be labor-intensive, tedious, expensive and time-consuming. Moreover, it has been reported that sampling errors lead to misleading results in detecting and differentiating crop genotypes (Elliot and Regan, 1993;Ma et al, 2001;Babar et al, 2006;Kiran et al, 2015;Marino and Alvino, 2019;Zsebő et al, 2024).…”

Section: Introductionmentioning

confidence: 99%

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Proximal canopy sensing of twenty-two bread wheat genotypes for nutritional quality, yield attributes and grain yield under Mediterranean climate

Kızılgeçi,

Cebeli

2024

International Journal of Agriculture Environment and Food Sciences

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To ensure nutritional security of rapidly increasing population, research interest has revitalized in determining the nutritional quality traits of staple food crops, especially wheat. Besides higher yield potential, research gaps exist regarding nutritional quality assessment of promising wheat genotypes grown under the Mediterranean climate. A field study was conducted to determine the relationship between yield components and quality characteristics of 22 bread wheat genotypes using the SPAD meter, GreenSeeker (NDVI), and CM-1000 chlorophyll meter at different growing stages (Stem elongation, Heading, Anthesis and Milk stage). The recorded findings revealed that G-41 genotype surpassed the rest of bread wheat genotypes by recording the maximum grain yield, whereas G-60 genotype exhibited the highest protein and wet gluten content. Among response variables, SPAD and NDVI values at the heading stage and CM-1000 values at the milk stage were found to be statistically insignificant. According to the correlation and biplot analysis, a significant positive correlation was found between the SPAD values measured for the stem elongation, anthesis and milk stage and yield and quality characteristics. Significant positive correlations were found between the NDVI values at the stem elongation, anthesis, and milk stages and the yield components, and between the CM-1000 value at the heading stage and the grain yield.

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supporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Proximal canopy sensing of twenty-two bread wheat genotypes for nutritional quality, yield attributes and grain yield under Mediterranean climate

Kızılgeçi,

Cebeli

2024

International Journal of Agriculture Environment and Food Sciences

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Photosystems functioning, the content of chemical elements, flavonoids and brassinosteroids in leaves of chimera Cornus controversa

Likhanov,

Schwartau,

Zaimenko

et al. 2024

Ukr.Biochem.J.

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he formation of chimera is a well-known phenomenon in variegated ornamental and wild plants, but the biochemical peculiarities of this phenomenon are not well studied. The purpose of this work was to conduct a comparative study of the photosynthetic apparatus functioning, the content of macro-, microelements, flavonoids and brassinosteroids in the variegated and green leaves of the periclinal chimera Cornus controversa plant grown in the collection of M. M. Gryshko National Botanical Garden of the NAS of Ukraine. Sampling was made during the period from June to October 2022. The content of photosynthetic pigments and brassinosteroids was measured spectrophotometrically, autofluorescence of leaf tissues was studied using an inverted microscope with a multi-channel fluorescence imaging system, Normalized difference vegetation index (NDVI) – using GreenSeeker devices and fluorimeter, the level of macro- and microelements – using plasma emission spectrometer. Phenolic secondary metabolites were separated by HPTLC. To assess the protective capacity of flavonoids, a bioassay on the germination of radish seeds Raphanus in the presence of AlCl3 was used. It was shown that green leaves of C. controversa chimera, compared to variegated ones, had a twice as high NDVI index, maintained photosynthetic function longer in autumn, and had higher content of macronutrients (Mg, Ca and S) but lower concentration of toxic metals (Al, Cu, Fe, Zn, Ti). Instead, variegated leaves accumulated more brassinosteroids and phenolic metabolites, which were found not only to reduce the toxic effect of aluminum but also to have a growth-stimulating effect on the roots of radish seedlings. Such specialization of green and variegated leaves may contribute to better plant chimeras adaptation to environmental pollution. Keywords: brassinosteroids, Cornus controversa, macroelements, microelements, NDVI index, periclinal chimera, phenolic metabolites, photosynthetic pigments, tissue autofluorescence

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Oilseed Rape Yield Prediction from UAVs Using Vegetation Index and Machine Learning: A Case Study in East China

Hu,

Ren,

Zhou

et al. 2024

Agriculture

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Yield prediction is an important agriculture management for crop policy making. In recent years, unmanned aerial vehicles (UAVs) and spectral sensor technology have been widely used in crop production. This study aims to evaluate the ability of UAVs equipped with spectral sensors to predict oilseed rape yield. In an experiment, RGB and hyperspectral images were captured using a UAV at the seedling (S1), budding (S2), flowering (S3), and pod (S4) stages in oilseed rape plants. Canopy reflectance and spectral indices of oilseed rape were extracted and calculated from the hyperspectral images. After correlation analysis and principal component analysis (PCA), input spectral indices were screened to build yield prediction models using random forest regression (RF), multiple linear regression (MLR), and support vector machine regression (SVM). The results showed that UAVs equipped with spectral sensors have great potential in predicting crop yield at a large scale. Machine learning approaches such as RF can improve the accuracy of yield models in comparison with traditional methods (e.g., MLR). The RF-based training model had the highest determination coefficient (R2) (0.925) and lowest relative root mean square error (RRMSE) (5.91%). In testing, the MLR-based model had the highest R2 (0.732) and lowest RRMSE (11.26%). Moreover, we found that S2 was the best stage for predicting oilseed rape yield compared with the other growth stages. This study demonstrates a relatively accurate prediction for crop yield and provides valuable insight for field crop management.

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Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology

Cited by 5 publications

References 71 publications

Proximal canopy sensing of twenty-two bread wheat genotypes for nutritional quality, yield attributes and grain yield under Mediterranean climate

Proximal canopy sensing of twenty-two bread wheat genotypes for nutritional quality, yield attributes and grain yield under Mediterranean climate

Photosystems functioning, the content of chemical elements, flavonoids and brassinosteroids in leaves of chimera Cornus controversa

Oilseed Rape Yield Prediction from UAVs Using Vegetation Index and Machine Learning: A Case Study in East China

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