Determining Spatiotemporal Distribution of Macronutrients in a Cornfield Using Remote Sensing and a Deep Learning Model

Fertilizer misapplications have induced widespread environmental deteriorations, climatic catastrophes, and economic losses; meanwhile, the Precision Agriculture (PA) endorsements have been influential in alleviating these issues. This study intended to tackle the fertilizer consumption inefficienci...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.30256-30266
Main Authors: Jaihuni, Mustafa, Khan, Fawad, Lee, Deoghyun, Basak, Jayanta Kumar, Bhujel, Anil, Moon, Byeong Eun, Park, Jaesung, Kim, Hyeon Tae
Format: Article
Language:English
Subjects:
Agriculture
Artificial neural networks
Biological system modeling
Convolutional neural network-regression
Deep learning
Economic impact
Economic models
Fertilizers
Information retrieval
macronutrients in soil
NDVI
Nitrogen
Normalized difference vegetative index
Reflectivity
Regression analysis
Regression models
Remote sensing
Sensors
Soil
Soil analysis
UAV
Unmanned aerial vehicles
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Summary:Fertilizer misapplications have induced widespread environmental deteriorations, climatic catastrophes, and economic losses; meanwhile, the Precision Agriculture (PA) endorsements have been influential in alleviating these issues. This study intended to tackle the fertilizer consumption inefficiencies by utilizing non-destructive remote sensing technologies, soil macronutrient distribution analysis, and a deep learning model. Specifically, an Unmanned Air Vehicle (UAV) was used in a cornfield to capture the plant's reflectance information for retrieving the Normalized Difference Vegetation Index (NDVI) during the vegetative and reproductive growth stages. Consequently, the field's soil samples were examined for their Nitrogen, Phosphorus, Potassium, and Carbon (NPKC) macronutrient constituencies. Finally, a Convolutional Neural Network-Regression model was developed to predict infield NPKC spatiotemporal variations in soil using the NDVI measurements. The deep learning model effectively determined the surpluses or shortages of the NPKC macronutrients within the cornfield throughout the growth stages. The model performed vigorously with R 2 values of 0.93, 0.92, 0.98, and 0.83 in predicting N, P, K, and C levels in soil, respectively.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3059314