Evaluation of tillage &amp; fertilization in Carthamus tinctorius L. using remote sensing

Papadopoulos, George; Mavroeidis, Antonios; Roussis, Ioannis; Kakabouki, Ioanna; Stavropoulos, Panteleimon; Bilalis, Dimitrios

doi:10.1016/j.atech.2022.100158

Cited by 2 publications

(2 citation statements)

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“…Data collection can be carried out by various means, Drones 2024, 8, 88 2 of 20 including different satellites [5][6][7][8], uncrewed aerial vehicles (UAVs) [9][10][11][12][13], or ground-based platforms [14][15][16][17]. These tools provide opportunities to monitor changes in plant and soil conditions, to predict in-season yields [18] and nutrient requirements [19], and to detect various diseases [20].…”

Section: Introductionmentioning

confidence: 99%

“…However, compared to remotely sensed image collection, their disadvantages are that they provide spot sampling, and the area covered by sampling is limited due to the poor resolution of collected points [17,23,29]. Despite their limited data collection capabilities, the GreenSeeker handheld sensor is an excellent tool for examining nitrogen conditions and biomass development of crops or predicting in-season plant yield [11,19,30].…”

Section: Introductionmentioning

confidence: 99%

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

Zsebő,

Bede,

Kukorelli

et al. 2024

Drones

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This work aims to compare and statistically analyze Normalized Difference Vegetation Index (NDVI) values provided by GreenSeeker handheld crop sensor measurements and calculate NDVI values derived from the MicaSense RedEdge-MX Dual Camera, to predict in-season winter wheat (Triticum aestivum L.) yield, improving a yield prediction model with cumulative growing degree days (CGDD) and days from sowing (DFS) data. The study area was located in Mosonmagyaróvár, Hungary. A small-scale field trial in winter wheat was constructed as a randomized block design including Environmental: N-135.3, P2O5-77.5, K2O-0; Balance: N-135.1, P2O5-91, K2O-0; Genezis: N-135, P2O5-75, K2O-45; and Control: N, P, K 0 kg/ha. The crop growth was monitored every second week between April and June 2022 and 2023, respectively. NDVI measurements recorded by GreenSeeker were taken at three pre-defined GPS points for each plot; NDVI values based on the MicaSense camera Red and NIR bands were calculated for the same points. Results showed a significant difference (p ≤ 0.05) between the Control and treated areas by GreenSeeker measurements and Micasense-based calculated NDVI values throughout the growing season, except for the heading stage. At the heading stage, significant differences could be measured by GreenSeeker. However, remotely sensed images did not show significant differences between the treated and Control parcels. Nevertheless, both sensors were found suitable for yield prediction, and 226 DAS was the most appropriate date for predicting winter wheat’s yield in treated plots based on NDVI values and meteorological data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zsebő,

Bede,

Kukorelli

et al. 2024

Drones

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Dissecting Physiological and Agronomic Diversity in Safflower Populations Using Proximal Phenotyping

et al. 2023

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Safflower (Carthamus tinctorius L.) is a highly adaptable but underutilized oilseed crop capable of growing in marginal environments, with crucial agronomical, commercial, and industrial uses. Considerable research is still needed to develop commercially relevant varieties, requiring effective, high-throughput digital phenotyping to identify key selection traits. In this study, field trials comprising a globally diverse collection of 350 safflower genotypes were conducted during 2017–2019. Crop traits assessed included phenology, grain yield, and oil quality, as well as unmanned aerial vehicle (UAV) multispectral data for estimating vegetation indices. Phenotypic traits and crop performance were highly dependent on environmental conditions, especially rainfall. High-performing genotypes had intermediate growth and phenology, with spineless genotypes performing similarly to spiked genotypes. Phenology parameters were significantly correlated to height, with significantly weak interaction with yield traits. The genotypes produced total oil content values ranging from 20.6–41.07%, oleic acid values ranging 7.57–74.5%, and linoleic acid values ranging from 17.0–83.1%. Multispectral data were used to model crop height, NDVI and EVI changes, and crop yield. NDVI data identified the start of flowering and dissected genotypes according to flowering class, growth pattern, and yield estimation. Overall, UAV-multispectral derived data are applicable to phenotyping key agronomical traits in large collections suitable for safflower breeding programs.

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Evaluation of tillage & fertilization in Carthamus tinctorius L. using remote sensing

Cited by 2 publications

References 48 publications

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

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

Dissecting Physiological and Agronomic Diversity in Safflower Populations Using Proximal Phenotyping

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