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CLASSIFICATION OF SOIL PROPERTIES FROM HYPERSPECTRAL DATA USING DENSENET-169+

Abstract 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. Authors K. Anandan Nehru College of Management, India Keywords DenseNet-169+, Soil Properties, Deep Learning, Prediction Yearly Full Views January February March April May June July August September October November December 0 0 0 2 2 3 0 0 0 0 0 0

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 Page Views : 96 Full Text Views : 7