Transfer Learning for Crop Type Classification: The Agricultural Breakthrough with AI

Abstract

In the rapidly evolving field of remote sensing, transfer learning has emerged as a transformative force, revolutionizing precision agriculture with unparalleled efficacy. Applying transfer learning in remote-sensing-based crop type classification and mapping stands to revolutionize global food production, garnering recognition and accolades in the realm of science, technology, engineering, and mathematics (STEM). This paper reviews groundbreaking research that showcases the exceptional potential of transfer learning technology to improve crop type mapping, agricultural productivity, and sustainable crop yield. The AI/machine learning (ML) models that have been trained on a large dataset for a specific task also known as pre-trained classifiers, has been utilized through various convolutional neural networks (CNNs) models, such as VGG16, GoogLeNet, SqueezeNet, and Dense-EfficientNet, adapting for crop type mapping. This study achieves significant results in crop type mapping through high-resolution drone and satellite imagery. Furthermore, the application of transfer learning extends to crop yield estimation, disease detection, crop field delineation, and crop phenotyping, propelling agricultural practices towards unprecedented accuracy. Innovative domain adaptation techniques and self-supervised pre-training strategies significantly enhance the model's ability to generalize and perform effectively across different geographic locations for crop classification. The result shows spatio-temporal transferability has significantly enhanced through transfer learning methods, which suggests the great potential in crop type classification and remote sensing applications. This research not only empowers end-users and stakeholder farmers with timely and accurate agro-geoinformation but also tackles the pressing challenge of limited labeled data for the AI/ML model to reuse existing models, reducing the need for large amounts of agro-geoinformation crop data, allowing the new model to make crop type mapping more feasible, and cost-effective.