Soil Nutrient Content Classification for Essential Oil Plants  using kNN

Arbawa, Yoke Kusuma; Dewi, Candra

doi:10.5220/0009957400960100

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“…In this study, various NN methods in DL, and a CNN have been applied to work on images. Arbawa and Dewi [13] introduced the categorization of soil nutrient content has been executed utilizing soil imagery as a substitute for laboratory soil testing.…”

Section: Related Workmentioning

confidence: 99%

Optimal Deep Convolutional Neural Network based Fusion Model for Soil Nutrient Analysis

Sharmila.G

2023

IJRITCC

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The vast majority of people in India, agriculture is their main line of work, and it has a large economic impact.. Soil is important for supplying vital nutrients to crops for better yield. Determining soil nutrients is certainly essential for selecting appropriate crops and monitoring growth. Common methods used by agriculturalists are inadequate to satisfy increasing demands and have to obstruct cultivating soil. For a better crop yield, agriculturalists must have an awareness regarding the soil nutrients for a specific crop. There comes a need for using Deep learning methods in soil analysis that would help farmers in the domain. This study introduces an Optimal Deep Convolutional Neural Network Fusion Model for Soil nutrient Type Classification (ODCNNF-STC) technique. The presented ODCNNF-STC technique examines the input soil images to classify them into different nutrients present in the soil. In this approach, the noise present in the soil images are initially filtered using a bilateral filter (BF) followed by contrast enhancement. The preprocessed soil images are fed to the model formed by the fusion of DenseNet201 and InceptionResNetV2 models extracting the soil images that can successfully differentiate soil nutrients. Finally, classification of soil nutrients were performed by three classifiers namely extreme learning machine (ELM), RMSProp optimizer-based 1DCNN, and RMSProp optimizer-based Stacked Auto Encoder (SAE). The experimental validation of ODCNNF-STC method is examined on real-time dataset of soil images with a maximum accuracy of 99.39% over recent methods.

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Section: Related Workmentioning

confidence: 99%

Optimal Deep Convolutional Neural Network based Fusion Model for Soil Nutrient Analysis

Sharmila.G

2023

IJRITCC

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Soil Nutrient Content Classification for Essential Oil Plants using kNN

Cited by 1 publication

References 0 publications

Optimal Deep Convolutional Neural Network based Fusion Model for Soil Nutrient Analysis

Optimal Deep Convolutional Neural Network based Fusion Model for Soil Nutrient Analysis

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