Deep learning in multi-sensor agriculture and crop management

Abstract

The integration of deep learning (DL) with multi-sensor data acquisition technologies is revolutionizing the field of agriculture and crop management, offering unprecedented precision and efficiency in monitoring and decision-making processes. This chapter explores the synergy between advanced DL algorithms and multi-sensor data. By integrating data from optical, SAR, thermal, and hyperspectral sensors, DL models offer higher accuracies in crop monitoring, classification, yield prediction, and stress detection, among other applications. This chapter highlights recent developments in the application of Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and transformers to analyze complex agricultural datasets, overcoming challenges related to environmental variability and the need for large-scale data. Despite computational and implementation challenges, these technologies promise enhanced crop yields, sustainability, and resource efficiency. The chapter emphasizes the importance of scalable and interpretable models, as well as integrated systems that leverage real-time data for informed decision-making, marking a huge step towards next-generation smart agriculture practices.