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ICTACT Journal on Data Science and Machine Learning ( Volume: 5 , Issue: 2 )
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Accurate and effective mapping of soil properties is regarded as a
critical task in environmental and agricultural management. The
evaluation of properties of soil is a daunting task while monitoring and
sensing the environment. Existing image process methods are time-
consuming and they are limited with regions and classification its
training layer. However, the need of soil analysis and its properties is
essential at landscape level. In this paper, DenseNet-169+ used to
assess the soil properties via its classification task from real-input
images. The DenseNet-169+ studies the variability index of the soil
using Kriging interpolation technique. The simulation is conducted to
study the efficacy of the model under different soil conditions and the
efficacy of DenseNet-169+ is reported. The results of simulation show
that the proposed method achieves higher rate of classification
accuracy than other models.
K. Anandan Nehru College of Management, India
DenseNet-169+, Soil Properties, Deep Learning, Prediction
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| Published By : ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning ( Volume: 5 , Issue: 2 , Pages: 566 - 572 )
Date of Publication :
March 2024
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