Paper Title
COMPARATIVE ANALYSIS OF MACHINE LEARNING AND DEEP LEARNING IN SOIL CRACK DETECTION

Abstract
Soil plays a foundational role in agriculture by providing essential nutrients and attaining the moisture level for plant growth. With the increasing demand for precision agriculture and environmental monitoring, effective soil crack detection methods are vital. Artificial Intelligence (AI) utilizing machine learning (ML) and deep learning (DL) algorithms emerges as a powerful solution. This study compares the performance of ML and DL techniques, including LBP-SVM and the Inception V3 Model, to detect soil cracks using real-world datasets. These algorithms identify soil crack formations due to the reduction of moisture level in soil. Through evaluation of traditional machine learning and transfer learning techniques, the study achieves 73% accuracy for ML and 99% for DL models, demonstrating DL's superiority. Transfer learning is shown to enhance accuracy, highlighting its potential for advancing traditional ML methodologies in agricultural practices. Keywords - Dataset, Deep Learning, Machine Learning, No Crack, Soil Crack