Transfer Learning Analysis For Predicting Soil Texture Classes From Soil Images

Abstract

Soil texture is one of the crucial characteristic in determining soil health. Classifying soil texture manually 1 is expensive, time consuming and requires experienced experts who are often limited available. Multiple machine leaning algorithms are proposed in the recent past to hold up a fully automated soil texture classification in 12 or lesser classes using soil images. Among such algorithms research on deep neural networks (DNNs) has been explored less. Wherever these DNNs are applied, they are used in isolation. Limited efforts are made to transfer the knowledge from 5 DNN of some other application and reuse the pre-trained network. In this work, concept of transfer learning is 6 investigated in soil texture prediction. Inceptionv3, ResNet50 and ResNet152 are trained on soil image dataset which 7 consists of images acquired from agricultural fields of multiple crops. It also shows analysis of different image 8 processing based segmentation techniques.