• KEERTHANA S
• Fathima Afrin M
• Bemisha JV
• Tamilselvi T

When composition is degraded or decomposed into minute fragments, minerals form, and soil is made up of these minerals. Soils are made up of loose humic substances materials that include 25% oxygen, 25% liquid, 45 percent minerals, and 5% organic compounds . Like different soil classification systems, the various soil system has multiple levels of information varying from the most basic to the most specific. Examples include cinder soil, siliceous soil, marsh soil, and yellow soil. Using TensorFlow and Keras, we demonstrate how to classify type of soil that used a convolutional network. The proposed technique distinguishes soil types from photos using CNN. The soil types were classified using the CNN model. The success of the acquired findings should improve if the CNN methodology is supplemented with additional image processing techniques and appropriately recognises soil types. We demonstrated how successfully convolutional neural networks ( cnn analyse various types of images. Finally, in building and deploying the model, host the Django framework.

Soil type, deep learning, TensorFlow, Keras, CNN

"Soil Type Prediction using Keras Framework", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.d268-d272, June-2022, Available :http://www.jetir.org/papers/JETIR2206334.pdf

"Soil Type Prediction using Keras Framework", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppd268-d272, June-2022, Available at : http://www.jetir.org/papers/JETIR2206334.pdf