An open-source library for geotechnical engineering analysis and modeling.

Soil Type Classification Through Image Processing and Machine Learning This project is in development process for our Thesis Project.

A software that classifies type of soil based on its physical properties using CNN. Outputs soil information and amendment for soil improvment.

• Designed a classification model with 5 classes and 98% testing accuracy using a Convolutional Neural Network. • Applied Data Augmentation and reduced validation losses by 30% by applying MobileNetV2 Architecture.

An application for Farmers to recommend them the best types of crops which can be cultivated on a certain piece of land using Soil Image.

Server code for the Agro-Companion website, featuring Flask-based services for deep learning-driven soil classification and fuzzy logic-based crop recommendation

Quarto version of the World Reference Base for Soil Resources, 4th Edition - https://obrl-soil.github.io/wrbsoil2022/

Agro companion is a soil classification, crop recommendation and crop information website. It helps identify seven types of soils, recommend 20+ crop based on specific factors and also help search information required to grow them.

Desarollo movido a https://github.com/ppizarror/granulometria-gui

The New Zealand Soil Classification, Draft 4th Edition

Soil classification using deep learning on image data. Trained a model to classify soil images into Alluvial, Black, Clay, and Red soil categories using PyTorch and GPU acceleration. Achieved high accuracy of 1.000 in a Kaggle competition.

Working repository for the code of the investigation of minimum soil sample masses

soilDT: the soil classification app

Soil classification (7 soils) dataset for the paper published in Engineering Applications of Artificial Intelligence: "An advanced artificial intelligence framework integrating ensembled convolutional neural networks and Vision Transformers for precise soil classification with adaptive fuzzy logic-based crop recommendations"