Agricultural Data

Agricultural data research focuses on leveraging diverse data sources—including satellite imagery, sensor networks, and genomic information—to improve crop management and yield prediction. Current research emphasizes the application of machine learning, particularly deep learning architectures like transformers, convolutional neural networks, and recurrent neural networks, along with classical methods like Naive Bayes and Random Forests, to analyze this complex data. This work aims to enhance the accuracy and efficiency of agricultural practices, leading to more sustainable and productive farming systems through improved decision-making and resource optimization. A key challenge is addressing data scarcity and bias in developing regions, with ongoing efforts focusing on data augmentation, transfer learning, and privacy-preserving techniques.