Analysis of Vegetation Indices in the Cotton Crop in South Asia region using UAV Imagery

Sabah, Noor Us; Usama, Muhammad; Zafar, Zuhair; Shahzad, Muhammad; Fraz, Muhammad Moazam; Berns, Karsten

doi:10.1109/icet56601.2022.10004662

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Phenological stages are usually observed through manual field visits however, studies widely use remote sensing data acquired through satellites, Unmanned aerial vehicles (UAVs) 6,7 and PhenoCams 8 in the domain of agriculture for crop mapping, yield estimation, and phenology estimation 9 are used to reduce these manual efforts. However, the availability of UAV and PhenoCam data is sparse and costly to collect, leveraging the use of satellite data which is easily available with the advantage of being free to less costly.…”

Section: Introductionmentioning

confidence: 99%

Multi-year monitoring of wheat phenology and effect of climate change in the south Asian region using Sentinel-2 NDVI time series analysis

Mehmood,

Malik,

Zafar

et al. 2023

Image and Signal Processing for Remote Sensing XXIX

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Efficient crop management strategies and optimized agricultural practices are pivotal in maximizing overall yield of crops. An essential aspect of improving crop yield is tracking the phenological development of crops, which plays a crucial role in carrying out timely crop management activities, including irrigation, fertilization, pest control, and harvest. However, the lack of resources to acquire data for phenological detection in under-developed countries and the influence of climatic factors on phenology, pose significant challenges. Our work proposes a cost-effective methodology that harnesses the power of Earth Observation (EO) data to acquire essential ground data without the need to rely on manual collection. With a focus on South Asia region, we delve into the analysis of EO data for monitoring wheat phenology and its dynamic interactions with climatic factors. The study focuses on five wheat phenological stages (stem elongation, heading, medium milk, hard dough, and harvest) from 2020 to 2022. Breakpoint and extrema analysis following curve-fitting of Normalized Differential Vegetation Index (NDVI) from Sentinel-2 data accurately detects heading, medium milk, hard dough, and harvest with a one-to-three-day average difference for both years. Stem elongation is detected with a seven-day difference in 2021 and 2022. Furthermore, our analysis reveals that a significant temperature surge in 2022, coupled with minimal precipitation, causes an earlier maturation of the crop compared to in 2021. We thoroughly investigate this effect for 2021 and 2022 to assess the impact of the rate of change in weather conditions on wheat phenology. Embracing these findings can foster sustainable and productive agricultural practices.

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Section: Introductionmentioning

confidence: 99%

Multi-year monitoring of wheat phenology and effect of climate change in the south Asian region using Sentinel-2 NDVI time series analysis

Mehmood,

Malik,

Zafar

et al. 2023

Image and Signal Processing for Remote Sensing XXIX

Self Cite

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Enhancing Cotton Crop Mapping in Pakistan: Integrating Transfer and Active Learning with Remote Sensing Technologies

Sabah,

Zafar,

Satti

et al. 2024

2024 4th International Conference on Digital Futures and Transformative Technologies (ICoDT2)

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Use of Neural Networks for Planning the Correct Selection of Plant and Soil Samples in Precision Agriculture Technologies

Pasichnyk,

Dudnyk,

Opryshko

et al. 2023

Naukovì dopovìdì Nacìonalʹnogo unìversitetu bìoresursìv ì priro

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The article is devoted to the study of the use of neural networks to optimize the selection of plant stands in precision agriculture technologies. The study takes into account the complex aspects of sample selection, such as the speed of image acquisition, the effectiveness of assessing the state of mineral nutrition and soil moisture, etc. This data is a necessary component for precision farming technologies and, in particular, crop management. Research was conducted on production fields in 2019-2020 in Boryspil district of Kyiv region. Spectral studies were performed using the Slantrange 3p complex installed on the UAV. Data processing was performed both with the specialized software for spectral data Slantview and with the mathematical package MathCad. The assessment of the nature of the distribution of both individual spectral channels and their combination in the form of vegetation indices turned out to be unprepared for the identification of uneven water supply of areas. The red channel and its derivatives turned out to be the most promising in the direction of identifying the water supply of wheat. The use of neural networks made it possible to identify probable areas with increased water supply on the maps of the distribution of vegetation indices in the field. The duration of identification using neural networks will not interfere with the sampling procedure, so that such a procedure can be effectively implemented in agronomic practices. Therefore, the use of neural networks allows you to automate and increase the accuracy of selection, improving the quality of the analysis of plant stands, subject to compliance with soil sample evaluation technologies. The obtained results indicate the prospects of implementing this approach in modern agriculture.

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Analysis of Vegetation Indices in the Cotton Crop in South Asia region using UAV Imagery

Cited by 3 publications

References 12 publications

Multi-year monitoring of wheat phenology and effect of climate change in the south Asian region using Sentinel-2 NDVI time series analysis

Multi-year monitoring of wheat phenology and effect of climate change in the south Asian region using Sentinel-2 NDVI time series analysis

Enhancing Cotton Crop Mapping in Pakistan: Integrating Transfer and Active Learning with Remote Sensing Technologies

Use of Neural Networks for Planning the Correct Selection of Plant and Soil Samples in Precision Agriculture Technologies

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