Quantifying the effects of soil texture and weather on cotton development and yield using UAV imagery

Feng, Aijing; Zhou, Jianfeng; Vories, Earl D.; Sudduth, Kenneth A.

doi:10.1007/s11119-022-09883-6

Cited by 4 publications

(2 citation statements)

References 47 publications

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“…This led this field to present consistent patterns of plant development throughout the cycle (see maps in Figures 6 and 11), even when considering the influence of the management of different inputs (growth regulator, nitrogen fertilization and others). The strong influence of soil type (texture, physical and chemical properties), whether spatially or temporally, on vegetation growth is widely known and reported (Basso et al 2001;Jiang et al 2020;Feng et al 2022). Soil texture plays an important role in determining the water content, soil nutrients and organic matter content, ultimately influencing crop development patterns through variability in vegetation dynamics.…”

Section: Interpretation Of Maps: Crop Height Vegetation Indices and S...mentioning

confidence: 99%

“…Soil texture plays an important role in determining the water content, soil nutrients and organic matter content, ultimately influencing crop development patterns through variability in vegetation dynamics. Feng et al (2022) evaluated the variability in cotton development due to variations in soil (electric conductivity-based texture at different depths) and weather conditions. These authors showed that the association between soil features and crop growth varied widely during the season and that this correlation was stronger when crops experienced water stress and that there was a consistently strong and negative correlation between sand content and soil moisture parameters.…”

Section: Interpretation Of Maps: Crop Height Vegetation Indices and S...mentioning

confidence: 99%

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Use of supervised and unsupervised approaches to make zonal application maps for variable-rate application of crop growth regulators in commercial cotton fields

Andrea,

de Oliveira,

Mota

et al. 2024

Preprint

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Background Zonal application maps aim to represent field variability by means of variables of interest that can further be translated into management practices. Zonal application maps for crop growth regulators in cotton under variable-rate strategies are commonly based exclusively on vegetation index variability. However, saturation by vegetation indexes cannot be avoided by multispectral imagery and dense crop vegetation areas. The objective of this study was to compare zonal application maps for crop growth regulator application under variable-rate conditions via two approaches: (i) relying on field-measured crop height and (ii) not relying on field-measured crop height. During the agricultural season, we developed zonal application maps using an unsupervised framework, representing local variability based on field-collected data, satellite imagery data, soil texture and phenology. Posteriorly, using data from 3 agricultural seasons, we developed a supervised framework to predict plant height by using a machine learning algorithm based on remote sensing and phenology data to test the development of the same maps without field data on plant height. Results The results indicated good performance of the machine learning model for predicting plant height, but these predictions presented much lower variability than that found in field conditions. Ultimately, the predictions went through the same unsupervised process to construct maps, but without requiring field-measured data. We tested both approaches in three fields on two different dates each. Fields with intense textural variability provided the highest compatibility between the maps of both approaches. Conversely, the lowest compatibility was found in fields with lower soil textural variation. Conclusion There is potential for the use of predictive modelling to assist in the construction of zonal application maps, but the adherence to real patterns of crop growth variability found in the field is highly variable and must be assessed on a case-by-case basis.

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Section: Interpretation Of Maps: Crop Height Vegetation Indices and S...mentioning

confidence: 99%

Section: Interpretation Of Maps: Crop Height Vegetation Indices and S...mentioning

confidence: 99%

Use of supervised and unsupervised approaches to make zonal application maps for variable-rate application of crop growth regulators in commercial cotton fields

Andrea,

de Oliveira,

Mota

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Developing an image processing pipeline to improve the position accuracy of single UAV images

Feng

Vong

Zhou

et al. 2023

Computers and Electronics in Agriculture

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Ground-based mobile imaging for detecting salt stress of cotton seedlings in the field

Li,

Ren,

et al. 2024

Computers and Electronics in Agriculture

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Quantifying the effects of soil texture and weather on cotton development and yield using UAV imagery

Cited by 4 publications

References 47 publications

Use of supervised and unsupervised approaches to make zonal application maps for variable-rate application of crop growth regulators in commercial cotton fields

Use of supervised and unsupervised approaches to make zonal application maps for variable-rate application of crop growth regulators in commercial cotton fields

Developing an image processing pipeline to improve the position accuracy of single UAV images

Ground-based mobile imaging for detecting salt stress of cotton seedlings in the field

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