Year: 2024
Type: article
Abstract: The distribution of grain sizes in different soil samples is essential for agriculture and geotechnics, providing high-resolution soil maps crucial for land use planning. Traditional methods for soil texture analysis are reliable but often time-consuming and inconsistent. With that, this study aims to create an efficient predictive model for soil texture classification using deep learning techniques. A dataset of 4,556 images was extensively pre-processed and trained, with a model chosen for validation due to its low MSE value of 1.18. The model's performance, evaluated through Precision, Recall, and F1 Score, showed weighted averages of 88%, 78%, and 74%, respectively, and an overall accuracy of 94.56%. Validation using 456 images revealed high accuracy for Sandy and Clayey Soils but varying results for Loamy and Silty Soils. In Trial 1, the model achieved over 91% accuracy for all soil textures, with 100% accuracy for Sandy Soil. However, Trials 2 and 3 exhibited decreased accuracy for Loamy and Silty Soils, with the lowest accuracies at 61.40% and 65.78%, respectively. These results suggest that while the model is effective for certain soil textures, it requires further refinement and additional diverse training data to consistently match the reliability of traditional methods.
Source: INMATEH Agricultural Engineering
Institution: Central Luzon State University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4405666503