Detecting Soil Erosion Patterns Using Convolutional Neural Networks
Year: 2025
Type: article
Abstract: Environmental sustainability and agricultural output are seriously threatened by soil erosion. This research utilizes deep learning techniques to create an automated system that detects soil erosion from photographs in or-der to solve these issues. Specifically, visual data and structured environ-mental parameters are combined with a Convolutional Neural Network (CNN) to identify soil conditions and forecast the possibility of erosion. Building a CNN model with the purpose of analyzing and categorizing photographs according to soil conditions is the main task of this research. Lay-ers using max-pooling diminish the dimensionality of feature maps and rec-ord the most important characteristics, eliminating the unnecessary information. Fully linked layers then interpret these characteristics to create the final categorization. The model’s output is a binary classification indicating the presence or absence of soil erosion. In addition to image data, the model incorporates structured environmental data such as rainfall and temperature. This data is processed through a separate branch of the network and combined with CNN's output to enhance prediction accuracy. By integrating environmental factors, the model can account for conditions that in-fluence soil erosion, leading to more robust predictions. The CNN model is trained using the Adam optimizer with binary cross-entropy as the loss function. This configuration is suitable for binary classification tasks and helps optimize the model’s performance. The training process includes 20 epochs with a batch size of 8, and class weights are used to address any imbalance in the dataset.
Language: English
Subfield: Soil Science
Domain: Life Sciences
SDG: Life on land
Open Access status: hybrid
OpenAlex ID: W7118183563
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